Key Questions to Ask When Ordering exoskeleton joint actuator

Mar. 24, 2025

Survey about Exoskeletons - RobotShop Community

Hi everyone,

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I would like to become more active in the field of exoskeletons - probably building an exoskeleton. The questions are somehow always the same with every such project: Why? How come? What for? Pourquoi?
You could help me, building 'the right' exoskeleton. I just would like to find out, what makes sense most. At least I would like to avoid, just building an exoskeleton only for myself. Probably it could be useful for some other people as well.

That's why I've created a short survey (<7min) to shed some light on it. I would be very happy if you could take part in it and give me feedback:
https://yujp90be53w.typeform.com/to/mOvytIhT

I am very grateful for your feedback!
Of course, if you are interested I could present the results here.

Best regards
Enrico

Hi all,

as promised, here are the results of the survey. First of all: thanks to everyone, who participated in it. It was very helpful for me.
These are the direct answers to the questions. So no interpretation or correlation included here.
Let's start with the Demographics:

in total, there were 25 participants in the survey
the average age was 42
84% declared to live in europe, the others did not mention their current location

How likely is it that the participant or a friend/relative of the participant would use exoskeletons?

Which limb/joint would be of most interest to be supported?

8% of participants would like to have a full body exoskeleton
Besides, 12% would like to have a full set of legs
8% are interested in full hands (wrist & fingers)
72% are interested in (multiple) single joint solutions
0% wanted full arm exoskeletons

Exoskeletons for the knee most favorable (64%)
Hip and Foot ankle close by
Hand and back equal
Arm (Fingers, Elbow, Shoulder) on wish list of '20% of participants

For which activities would exoskeleton be used?

Just for fun/Cosplay/Accessory not of interest
As Sports device not very interesting
Most probably used, when advantage for work & health is expected

Motor assistance most important for exoskeletons ' full replacement of muscle activity demanded
Muscle activity sensing important
Design important, but no need for 'cool LEDs'
Controllability almost equal: RC, App, Interface to other processors or microcontrollers

Tradeoffs: Speed vs. Strength, Weight vs. Power, Energy vs. Weight

Mostly rated with '3'
The questions seem too technical to me. Most people don't know how much force/how much speed/how lightweight/'
It might depend on specific use case & kind of exoskeleton as well as on specific numbers (e.g. 15mins/2hrs/8hrs of operation vs. specific weight)

Willingness to pay for quality and durability

People prefer adequate, well built devices for sports, as a working aid or as a limb support, or
to some amount cheap exoskeletons just for fun
23% would not spend money for exoskeletons

Additional Comments
These were the comments, that the participants left

I hope, that it is as informative for you as it was for me. If you have questions regarding these, just let me know.

Best regards,
Enrico

Hi again,

I used the raw data to also correlate some of the answers.
What I also found out: with the 'hand' exoskeleton, I was not very precise. What I actually meant is a wrist exoskeleton. That's why the fingers and thumb are listed as a seperate exoskeleton. I hope, that all participants saw it the same way - but I cannot guarantee for that.

Lets start:
Age vs. Likeliness to wear exoskeletons
How is the likeliness to wear exoskeletons depending on the age of the participant?

no participants were younger than 20, so I skipped this line.
the bottom diagram is the sum of both upper diagrams (so the total sum-up of all entries is 200% in that matrix)
The higher the age, the more likely it is, that the person is willing to wear exoskeletons (or knows s.o. who might do so)
older than 30yrs ' more willing to wear exoskeletons

Limb/Joint of most interest vs. its application
This matrix correlates the interest in a certain joint with the purpose that the participant prefers. E.g. if someone is only interested in 'back exoskeletons' and prefers to wear it 'Just for fun' (high rating) it would increase the score in that field in the table.

body weaknesses: legs, especially knees seem to be the most favorable joints
Working aid: all joints equal
Shoulder, elbow, back not wanted for fun/Cosplay/Accessory
Ankle, knee & shoulder might also be used just for fun or sports
Fingers & hand interesting for fun and cosplay as well

Limb/Joint of most interest vs. required features
Question: can a certain feature be assigned to a certain type of exoskeleton or joint? E.g. do hand exoskeletons need LEDs and knee exoskeletons preferable strong motors? This diagram shows some trends about this:

Motor assistance for all joints important, but if highest interest for the legs and elbows
Sensing of muscle activity most interesting for legs and elbow
Design for leg exoskeleton seem important (but cool LEDs are not necessary)

Tradeoffs in relation to the joints
Do some trends regarding high strength/high speed, lightweight/high power or large/small battery correlate more or less with some joints?

So that's it actually.
As I said: hopefully it is helpful for you and don't hesitate to discuss about it or ask me some further questions.
For me it was interesting to go through the process, because it is the first survey that I ever set up.

Best regards,
Enrico

I didn't see any indication that you have formal training in orthotics. If not, keep in mind that you are attaching your device to a human being, and it will likely be powerful enough to injure the person if something goes wrong. Motor controllers can fail in a way that the motor turns on unexpectedly, and of course, a programming error will likely resut in the same thing at some point in its development. So, you have to be pretty good at both biomedical engineering, and in designing ultra-reliable robotics. You've seen a need, and you are now hunting for a way to address that need, but you have chosen a very difficult project.

If you want to proceed, I suggest you abandon any idea of strapping anything onto anyone for now. Instead, focus on things that people sit on, or perhaps grab. E.g. there are chairs that will lift up the seat cushion to help a person get out of the chair. Of course, that is a problem that is already solved. But, perhaps you can find another.

I will mention a particular need I see, that is not yet met in the market. Personally, I have a muscle disorder and am easily fatigued. This is only a problem when I go to shopping malls, or to a county fair. I am not an invalid, so I don't need medical grade equipment to get around. If an scooter occasionally gets stuck when it is used on the grass, I can deal with that. There are some relatively low cost mobility scooters out there, which are both very light (under 40 pounds), and low cost (around $400-$700). But, those cannot go over grass because the single drive wheel just spins in the grass. [ VEVOR Portable 3-Wheel Mobility Scooter for Seniors 12 Mile Range Max 330LBS | eBay or Robot or human? ]. There are other ones out there, that are both light-weight and can be used in grass, but those cost dramatically more. Engineering is not just about calculating forces, but also economics.

The ones with a single front power wheel, cannot go up hill, because all your weight transfers to the unpowered rear wheels. The one with rear-wheel power, really needs both wheels to be powered. Also, you really need larger wheels, such as ones made for hoverboards, that can go over grass. In fact, hoverboards have turned powerful BLDC motors into commodity items.

My bottom line recommendation is to start with something simpler, to help build your robotics skills. And choose something that doesn't present a danger to its user until you gain the knowledge and experience to properly design a biomedical device.

@fb1: I hope you have seen my pm?
@cadcoke5: Thanks for your suggestions. I can imagine, that it is very frustrating, to have only not-so-well-engineered devices available. Of course it would be a nice topic to redesign/optimize such a scooter.

I would prefer not to focus on low hanging fruits. For me arranging some wheels with motors and make them turn on command seem like a moderate challenge. But I guess, there are many people out there, who are quite capable of achieving such a functionality. Hereby, I would encourage everyone who is reading this to think of such a solution. Probably it is not only @cadcoke5 who would benefit from it.
Personnally, I would prefer something more challenging. I am not a biomedical engineer, but I think, engineering in this direction, learning about regulations and following them, might end up in a safe exoskeleton, that could one day be worn be someone. As you said, probably it is about engineering, and engineering a safe device that would not harm someone. Probably the biggest challenge in it lies in a good risk as well as failure mode and effects analysis - and of course the reduction of all the potential risks and failure modes. So I just try it this way. Of course the device would not be attached to any body (also not mine), if the device is not working properly.

But I thank you for your suggestions and look forward to many others.

Best regards
Enrico

Since your goal is to create a relatively novel approach to an exoskeleton, consider trying to design it so that any failure in controlling the motors are just not going to cause harm due to the nature of the design.

I gave the seat-lift idea as an example. Such devices use worm-drive actuators, so if a motor turns off, the user is not suddenly dropped down. And the motor system is designed so that its maximum speed cannot throw the user off the chair. Nor is the device even strapped to the person. Though, theoretically, the designer of such a system might have designed a lift that was strapped to the user's legs instead being inside the chair. There are also crane-type of lifts. Which help get people out of bed, as well as out of a chair. But, they are not as easy to use, and generally a person can't use it by themselves. There can be multiple ways to solve a problem, with some being safer or easier to use.

For your exoskeleton, I am not clear about your end-use goals. If it can be more narrowly defined, perhaps like the chair lift, it can lead to a more limited usage, but an easier and safer one. (i.e. the in-chair mechanism, vs. the crane type lift) For your exoskeleton, if the goal is to help users lift boxes and put them onto shelves, then the device may not need to strap to the user, It could be a somewhat independent robot, designed so that user grabs some handles on it, and directs the robot in its actions by moving those handles.

Review of control strategies for lower-limb exoskeletons to assist gait

From the control perspective, the main challenge for gait assistance is to contribute to the intended movement, since the device cannot directly communicate with the wearer to clearly recognize the intention and collaborate effectively. Effective collaboration can be interpreted in different ways, depending on the context and application. In general, for partial assistance it would mean synergy in forces or torques between the user and the device, and for full mobilization it would be coordination between the movements of the exoskeleton and those of the user's upper body. Many strategies are used to identify the user's intent, and apply an appropriate torque or motion accordingly. In the rest of this section, the existing strategies will be reviewed and discussed. Before getting into the review of these strategies, the rationale behind the criteria that were used for screening the literature and the proposed classification method will be explained, and the methodological steps will be described.

Methods

Scope and methodological steps

The main question to be addressed in this part is: what approaches have been used in the literature up to now for controlling lower-limb exoskeletons with the purpose of directly assisting the wearer's gait? Target devices for the controllers in this review do not need to provide an improvement of the user's health. Although the devices are typically anthropomorphic, exceptions also exist (such as [10,11,12,13]). The so-called 'soft exoskeletons' (exosuits) are included too, even if these are not really stiff 'skeletons', but closer to 'tendons and muscles'. The papers that do not deal directly with an exoskeleton, but suggest a sensing method that could be useful for them are included as well. As explained previously, many gait assistance devices are presented in the context of rehabilitation. In light of the similarities from the control perspective, we did not limit the scope of this review to a specific application; as long as the described controller is supposed to assist the user during gait, the method was included in this review regardless of the long-term goal.

This review aims to address wearable gait assisting exoskeletons, because they have the potential to be used for real-life applications out of the laboratory. However, the articles involving fixed-frame devices designed to explore such control strategies (e.g. LOPES [14], ALEX [15], the exoskeleton emulator of Collins et al. [16], etc.) are also included in this review. In addition, if at least part of the control strategy proposed for a fixed-frame rehabilitation device also assists the user's gait and is applicable to assistive exoskeletons, it is included (for example [17]). The strength augmentation devices are excluded because they are not designed to enhance the walking mobility. The main consequence is that they are of no use for people affected with gait deficiencies, or healthy people willing to improve their ability to walk (higher speed and/or endurance) with no load. They also mainly focus on load lifting so the control strategies may be different, and may also involve upper limbs. The task of carrying a load while walking (e.g. [18]) is closer to the topic of this article, but such devices still do not assist in moving the user's legs or relieve the user from the bodyweight. In addition, it makes comparing the performance even more difficult, because the assistance benefit depends on the amount of payload. However, a strength augmentation device that would enable its wearer to jump higher or run faster would have been included, but such reference could not be found. Similarly to the fixed-frame rehabilitation devices, a strength augmentation device can be still be included if at least part of the control strategy could be applicable to the assistance of the gait with no carried load (e.g. [19]). The inclusion and exclusion criteria used in the screening process of this review are summarized in Table 1.

Most of the publications were found using the following Google Scholar query:

robot* assist* control* (exoskele* OR orthosis)

and a similar query on Scopus: 'robot* assist*' 'control*' AND ( exoskele* OR orthosis ) AND NOT ( 'upper limb*' OR 'upper-limb*' OR 'hand exoskelet*' ) among the records published since January up to the end of August . The references cited in the two previous review papers by Yan et al. [5] and Tucker et al. [4] were also included.

First, the references were screened with the title, then the abstract, and finally the full-text to check if they fit the inclusion/exclusion criteria. Then, they were read entirely and entered in a database. The relevant articles cited by the ones already in the database were also added. A flowchart of the methodology is shown in Fig. 1. For each entry in the database, the following fields (as long as they were relevant/applicable) were entered: high-level control method, mid-level control method, low-level control method, type of actuator, short controller description, intended application, assisted joints, device name, and remarks.

Proposed classification

This review is centered on control strategies, being hardware-agnostic as much as possible. To be accurate enough in describing the different control strategies features, but with no redundancy in the descriptions, it was chosen to break the behavior into smaller functional units. Indeed, an initial assessment of the literature revealed that even among different control strategies, shared elements exist. Compared to describing each control strategy as an atomic entity, this classification method allows for reusing the same elements to represent several strategies.

The literature shows a considerable number of different controllers, with different structures, designs and actuation methods. However, the ultimate requirements in terms of performance and desired behavior are mostly similar. In an attempt to classify them, we will separate the controllers into smaller functional units that are comparable. Each functional unit can be used in several different combinations to form various controllers. Therefore, these functional units can be considered as the building 'blocks' of the controllers. Based on their role in the hierarchy of the control system, all of these blocks can be classified into three categories: high-level, mid-level and low-level control (see Fig. 2). This hierarchical classification is similar to the one used in [4].

Within each level, various methods and approaches thus form the different blocks. Some of the blocks within the same level perform the same function (in terms of outputs) using different methods, while others have a dissimilar functionality. Hence, even though the blocks in different levels may be used together, they are not always compatible. All of these blocks are shown in Fig. 3 and will be explained in detail later in the paper. It should also be noted that the reviewed control strategies do not necessarily cover all the three levels, with most of the research being focused on mid-level control. This review will then focus on mid-level control mostly.

Our analysis of the high- and mid-level layers is also implementation-agnostic, which means it focuses on the external behavior of the device rather than the way to program it or make the hardware design. Most of the hardware-specific aspects will be separately discussed in the low-level layer.

The results obtained by all these controllers are not compared, because the target users are different (healthy, elderly, paraplegic, stroke, etc.), the tasks are different (walking, running, ascending stairs, etc.), and even for the same task, the experimental protocol is often different. Such comparison is possible, but only with a narrower scope. For example, the review of Sawicki et al. [7] focuses on the partial assistance for the gait, to decrease the metabolic cost of locomotion for healthy people.

High-level control

The high-level control determines the general behavior of the exoskeleton. Exoskeletons can usually switch between several operating modes, depending on the desired type of activity, and the environment (e.g. walking on flat terrain, climbing stairs, and sit-to-stand transitions). Often, this change of mode does not occur frequently, and there is typically a gap of at least several seconds between two consecutive changes. This makes it possible to be selected by the user.

Relatively few papers are dedicated to high-level control. For most research purposes, the focus is on a certain mode of operation, and the experiments take place in controlled lab settings and are based on well-defined scenarios. However, reliable high-level control is crucial for the usability of exoskeletons for people in real-world situations and everyday life, where a variety of movements and gaits in different environments and terrain types are required and short transition times are necessary.

The inputs to the high-level controllers can come from the user (via input devices and/or sensors), the environment, or a combination of both. The output is usually a mode of operation. Artificial intelligence and machine learning methods are being increasingly used as a substitution for the user choice. The main motivation is to make the operation more automatic for the user, and possibly faster than manual input. Fundamental criteria for the usability of such methods are the real-time operation and short processing times, since decisions need to be made fairly quickly to allow enough reaction time for the lower-level controllers. Existing high-level control strategies are discussed in more detail below.

Explicit/manual user input (MUI)

The user directly determines the mode of operation of the exoskeleton, using input devices such as buttons [20'34] or voice commands [35, 36]. These methods are currently the most common due to their ease of implementation, higher predictability, and lower risk of errors. However, these advantages come at the cost of additional participation required from the user, which makes the user experience less natural, increases the cognitive load, and can slow down the operation. Moreover, this method is also prone to human errors which are more likely to happen during demanding tasks, long operation times, or with novice/distracted users. In this case, the challenge is both to make the user interface easy to use to minimize the learning time and the risk of manipulation errors, and also quick to use to avoid losing time in transitions. This is not trivial since the interface has to be used in a standing position, and the hands often have to hold crutches at the same time.

The explicit user input is commonly used in full-mobilization exoskeletons for complete spinal cord injury (SCI) patients, because no input can be obtained from the legs. It is also the most predictable for the user, which is important for trusting the device. In this case, buttons on the crutch handle, or a special wristwatch can be used. Voice command is not common because it requires speaking, which may feel awkward in public spaces. It is also more error-prone in noisy environments.

Brain-computer interface (BCI)

The user's brain activity is measured using electrodes, amplified and analyzed to determine the mode of operation [37,38,39]. Among the different brain signal recording methods, currently electroencephalography (EEG) is predominantly used since it is non-invasive and therefore safer and easier to use. Despite the promising features of these methods, there are many practical challenges associated with them, including high levels of concentration required from the user (and therefore limiting simultaneous cognitive activities such as speech), artifacts with muscular activation (EEG signals at the surface of the scalp have an amplitude close to 100 μV [40], while electromyography (EMG) signals are several millivolts), rather lengthy procedures for electrodes placement, the need of training for the user and the algorithm, and being very slow (in the order of seconds) or limited to very few commands [39, 41,42,43,44]. A thorough review of brain-computer interfaces BCIs for lower-limb gait assistance devices in general can be found in [45], and an in-depth review of methods based on EEG in [46].

Movements recognition (MOV)

This type of controller changes the behavior automatically depending on how the user moves or is intending to move. The main advantage of this method is that it does not require any cognitive load or direct input from the user, making the interaction more intuitive and natural. For this method, generally joint sensors and IMU data (often from the upper body in persons with paraplegia) are processed by a machine learning or fuzzy logic algorithm to recognize the situation [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64], although simpler threshold-based methods have also been proposed [65]. Sometimes, other types of signals such as the ground reaction forces or electromyography (EMG) are also used to infer the movement or the intention of the user [66,67,68,69,70,71]. Capacitive electromyography was also investigated [53]. In practice, often additional inputs are also required to complement these controllers (e.g. to disable them when the user needs to perform other activities while standing still in the device) since the movements of the user are not always sufficient to correctly determine the intention. In [72], the discrimination between walking and jumping is performed with a threshold on the phase difference between the two legs (shank segment), computed with the angle-speed diagram. Moreover, standing is detected if the magnitude of the phase vectors for the two legs is below a certain threshold.

Terrain identification (TER)

Generally, the most decisive factor in determining the mode of operation and high-level behavior of gait assistance devices is the terrain. Information about the terrain can hence be used to construct a high-level controller for such devices. In these controllers, embedded sensors are used to recognize the terrain type or obstacles in front of the user, in order to plan the steps accordingly [73].Footnote 2 Sensors used for these high-level controllers are most often cameras (either usual visible-light cameras [74, 75] or 3D depth-sensing [41, 76,77,78,79,80,81,82]), but other sensors such as infrared distance sensors [83] or fusion of laser distance sensors and inertial measurement unit (IMU) [73, 84, 85] have also been utilized.

Terrain identification has recently gained attention in the fields of orthotics and prosthetics, and the body of literature exploring it is relatively small. Even the existing papers are limited to proof of concept implementations, demonstrating the performance of terrain identification algorithms without actually integrating them into the high-level controller of a device [73, 75, 79, 80, 83,84,85]. These techniques are usually computationally expensive because of the image or point-cloud processing. However, promising results have been demonstrated and with the advances in pattern recognition and machine learning methods, successful implementations of such controllers are to be expected in future research.

Mid-level control

The mid-level is defined here as the continuous behavior of the robot, which computes the joints target torque or position, at each timestep of the main control loop. The mid-level controller plays the most important role in shaping the interaction of the device with the user, and the majority of the research on the control of exoskeletons is dedicated to this level. Although the output of the high-level controller also affects the behavior, it often only changes some parameters of the mid-level controller without fundamentally altering the essence of the interaction with the user.

In the proposed classification, the mid-level control blocks have been separated in two sublayers. As shown in Fig. 2, the 'detection/synchronization' sublayer estimates the gait phase or gait state, which is a piece of information commonly needed by the 'action' sublayer that actually computes the motor command. The first sublayer uses external inputs (from sensors and/or user interface) to determine the continuous phase or discrete state of gait. In the second sublayer, the desired physical output of the device is decided.

An exoskeleton controller can have a different control scheme for each joint. This is for example the case in [19], in which a simple spring is used for the ankle, an active damper for the knee, and torque control on the hip joint. Another example in [86] is an adaptive-frequency oscillator AFO-based impedance control for the hip, fixed position or zero torque control for the knee (depending on stance/swing), and event-triggered torque sequence for the ankle.

Detection/synchronization sublayer

The desired outcome of this sublayer is either the accurate gait phase (0'100%), or the gait state. Gait states are generally subphases of the gait cycle (e.g. stance/swing or finer divisions such as loading response/foot-flat/push-off), the kind and the number of which depend on each controller.

Manual trigger by user (MAN) This lets the user explicitly trigger the movement. This block is usually followed by the 'Linear increase of the gait phase' and 'Position profile'. This method is simple and used frequently to trigger the steps of a full mobilization exoskeleton. The trigger is generally a button ([20, 24, 26,27,28, 31, 87, 88]), but steps can also be triggered by EEG [42], although very slowly. It is worth mentioning that controllers in which the user manually triggers the start and stop of locomotion (and not the individual steps) such as [89, 90] do not belong in this category.

Impose the movement (IMP) Instead of synchronizing to the user, the robot imposes the movement continuously. So, it is the user's responsibility to stay synchronized with the robot. This is sometimes the case with early-stage full-mobilization exoskeletons that test the continuous gait without providing a user interface to use them in real-use conditions [91,92,93,94,95]. Other common cases are brain-computer interface (BCI)-controlled exoskeletons that do not need crutches, with start and stop commands instead of having to trigger each step [41, 43, 44]. As opposed to the rest of the blocks, this one does not represent an actual function in the controller, nor does it have an output for its following block. Rather, this block is only used to emphasize the lack of synchronization. It is always followed by 'Simple linear increase of the gait phase', which then usually feeds the 'Position profile' or 'Torque profile' blocks.

Event trigger (EVT) This method can be found in many exoskeletons for partial assistance and full mobilization. It consists in using an event of the gait to start a step, a torque profile or to transition a state machine. The most common event is the heel strike, detected with a foot switch at the heel or (rarely) with an instrumented treadmill [96,97,98,99,100,101,102,103,104,105,106,107]. If the pressure sensor is located under the forefoot, the late stance can be detected instead of the heel strike [108]. The reference instant can also be recognized with an inertial measurement unit (IMU) on the shank, when crossing the zero angular speed [109]. A variant is to detect the point of 'negative-to-positive power' of the ankle by looking at the ankle speed (one IMU on the foot, one IMU on the shank) [110], or with a classifier [111]. An alternative is to use an inertial measurement unit (IMU) in the foot sole [112,113,114]. Similarly, it is possible to detect the lift-off [48, 115]. A set of thresholds on the 'analog' ground reaction force can also be used to discriminate several phases in the gait cycle [116,117,118].

Events in the kinematics can also be used. The peak value of the hip angle is used in [119,120,121,122,123,124], or similarly the peak ankle dorsiflexion angle [125]. In [126] the state machine is transitioned with thresholds on the knee angle and velocity. In [10], there is a threshold on the time-derivative of the pressure of the passive pneumatic actuator, which relates to the joint speed. In [127], a hidden Markov model is used to detect the gait phases from trunk and segment angles measured with an inertial measurement unit (IMU).

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For full-mobilization exoskeletons, the steps can be triggered by weight shifting measured by the load cells under the feet [21, 128,129,130], by leaning toward the front or on the sides which is measured by the inertial measurement unit (IMU) [30, 128, 131], with a combination of the crutches load cells and the feet load cells [32], or a combination of the trunk tilt and the feet load [29].

Adaptive frequency oscillators (AFO) AFOs are dynamical systems with an oscillatory behavior that are capable of learning the features of a periodic input signal [132]. Due to the periodic nature of the gait, they can be used to determine the gait frequency and the phase. They can adapt quickly to a change of cadence, and do not need any prior knowledge on the shape of the gait pattern, except the fact that it is periodic. This makes them robust and makes the controller suitable for almost any user without the need for extensive parameter tuning or gait pre-recording. AFOs are usually fed with joints angles, but can also be used with any other periodic signal, such as the muscular activity, estimated using capacitive sensing [133] or interaction forces between the device and the user [134, 135].

AFOs can produce several useful pieces of information: the current progress in the gait cycle (0-100%), the frequency, and a filtered version of the input signal with no lag. Actually, the whole trajectory over the full gait cycle is modeled by the adaptive-frequency oscillator (AFO). These can be used in further action blocks, typically 'Torque profile', or 'Impedance control'. The output has occasionally been directly used as a position reference as well [134, 135].

While AFOs are able to compute precisely the frequency and the joint angle value function over the gait cycle, the reference moment (usually the heel strike at 0%) is unknown so the absolute gait cycle progress cannot be determined. Several techniques exist to solve this issue:

Foot switches can measure the instant of the heel strike [51, 136, 137]. This method is accurate, needs no heuristics, but requires an additional sensor. An inertial measurement unit (IMU) can also be used instead [138].
A special feature in the joint trajectory (e.g. minimum or maximum value, or maximum slope) at a known gait phase can also be recognized, but this is subject-dependant and less reliable [122] (and probably [50]).
Instead of a sine wave as the first harmonic, a known average human gait trajectory can be used [139,140,141]. This is less accurate if the user is walking in a non-typical way. Such an oscillator is called 'PSAO' (particularly shaped adaptive oscillator) by the development team of the GEMS exoskeleton [139].
Finally, strategies that do not use the absolute gait cycle progress can be selected, so that there is no need to obtain this information. This is the case for force fields that attract the joint towards its predicted position [56, 86], or compensation from a physical model (weight, inertia) [142].Footnote 3 Note that 'attracting toward the predicted position' is equivalent to using an impedance controller with the AFO-identified movement with a time offset (to follow the future) as the reference.

In [143], AFOs are also used, but the reference determination method is not explained. The Honda Stride Management Assist is also using a special AFO method according to a patent [144], but the details are not clearly documented.

The AFOs strategy is limited to the partial assistance paradigm, since the user needs to be able to initiate the gait and maintain it at least for a few steps.

Simple linear increase of the gait phase (LNP) This is the simplest way to determine or impose the gait phase. It consists of increasing linearly the gait phase over time, knowing in advance the step duration. If the movement is imposed all the time (IMP), the gait phase is looping continuously [38, 90, 91, 145]. If triggered manually [26, 35, 87] or with an event such as foot contact with the ground [97, 101, 146], lateral weight shifting [89], tilting the trunk [30, 147], or muscle activation (sensed via electromyography (EMG)) [82], it only runs once per trigger. The output of this block then feeds a position or torque profile.

Time-interpolated gait phase (TBP) This is the same as LNP, except that the gait cycle duration is determined automatically from the duration of the previous steps. This is very accurate if the gait is periodic and with a small inter-step variability. This method is very common for partial assistance [16, 96, 98,99,100, 102, 103, 105, 106, 108, 110, 113,114,115, 121, 123, 125, 148,149,150,151,152,153,154,155]. An extension of this method is to use a Gaussian probability density to reject outliers [156].

Angle-speed plot phase (ASP) This technique consists in determining the gait phase from the angle and speed of a single joint. Intuitively, the function that maps a joint angle to the gait phase is surjective but not injective, because there are at least two solutions, due to the back-and-forth movement. So at best, if the joint trajectory is not bouncing, there are two possible gait phases for a given joint angle. However, the speed has the opposite sign for the way back, so it gives enough information to disambiguate the gait phase. In practice, these states are plotted on an angle-speed graph, and the phase angle can be extracted (Fig. 4). The center of the trajectory must be defined by prior calibration. The main advantage of this method is that it keeps its accuracy even if the gait cadence changes rapidly. However, it is very sensitive to bouncing, and is inaccurate if a joint moves little during part of the gait cycle. This is why it is not used with the knee joint. This method is used in [72, 157].

Machine learning phase (MLP) The gait phase can also be estimated using machine learning, with techniques such as support vector machine (SVM) or neural networks. The machine learning methods are diverse and complex, so they will not be explained here. All the found references used a different machine learning method and different inputs. A neural network fed with the trunk IMU data and hip encoder angles is used in [158].

An online Gaussian process regression is fed with the joints angles and interaction forces with the thigh cuffs in [59]. In [159], the gait phase is estimated with a decision tree, from the segments IMU data and the feet loads. In [160], deep learning is used on the shank and thigh IMU data and feet loads. In [111], a SVM is used with the shank IMU data. In [53], a quadratic discriminant analysis allows to get the gait phase from capacitive sensors measuring the thigh muscles contraction. Finally, in [161], a computer vision classifier can estimate the gait phase from the data of depth cameras located on the crutches.

Other gait phase estimator (OTP) The gait phase can also be estimated by other less common methods. One of the controllers proposed in [162] ('State Estimation' controller in the paper) estimates the gait phase by fitting the recorded joint angles and the foot loads to a reference model, using least-square regression based on the method from [163]. In addition to this method, another variant is also suggested for comparison in [163], which determines the gait phase based on minimizing the squared error between the instantaneous ankle angle and contact forces at toe and heel with those of a reference model (the first method is called 'cross-correlation' and the second 'k-nearest neighbors' in the original paper). However, the estimated gait phases have not been used in a controller, but have only been compared to evaluate the estimation accuracy.

State machine (FSM) Controllers can switch behavior depending on transitions triggered by events. This may be useful because some states of the gait are non-continuous. The best example is the foot contact, which is binary (swing/stance) and changes the dynamics of the leg. Many controllers use a state machine and different criteria have been utilized for transitioning between the states.

Most commonly, the ground contact state of the feet, or equivalently the ground reaction force (GRF), is used either for the entire foot to only distinguish between stance/swing [164,165,166,167,168,169] or considering local components (e.g. at the heel and under the toes) to further differentiate between stance subphases [48, 67, 86, 117, 148, 162, 163, 170,171,172,173,174]. The gait state can also be determined by computing the center of pressure (CoP) position of the stance leg with four load cells per foot, then applying a threshold to identify four states [175, 176]. In one paper, the subphases of stance were detected only based on the total ground reaction force (GRF) [116]. For some state machines, the ground contact status has been used as the only factor for transitioning the states [67, 117, 162,163,164,165,166, 171, 172], but it has also been used in combination with joint angle(s) [116, 167, 170], joint angular velocities [173], segment angles and angular velocities [48, 168], or the relative position of the feet [177]. In [148], the linear acceleration of the shank is also used in addition to ground contact data to improve the accuracy of heel-strike detection. The amount of time elapsed since the onset of swing has also been used in addition to ground reaction force (GRF) data to further detect subphases of swing [169].

Joint angles and angular velocities have also been used without the ground contact information to transition states [126, 178,179,180,181,182]. In [47], in addition to the angle and angular velocity of the knee joint, the moment at the joint and the angular velocity of the leg are involved in state transitioning. The authors in [19] have augmented joint angles with the forces and moments sensed in the exoskeleton segments to transition the state machine. In an alternative method, the difference between left and right joint angles (hip and knee) are used along with zero-crossing events of hip angular velocity to transition between the states [151, 152].

In [10], thresholds on the derivative of the pneumatic actuator pressure (which indicates the direction of movement intended by the user) are used for the transitioning. Surface electromyography (EMG) has also been used as another indicator of user's intention to transition the states [66]. In [89], the estimated projection of the center of mass (CoM) on the ground relative to the feet is mostly used to transition between the states, but direct user input (via buttons) is required for transitioning in and out of the initial and final states, while transitioning between others (e.g. between shifting the weight to the stance leg and swing of the opposite leg) is initiated automatically.

Different states may only change the parameters and/or inputs to a controller (for example [48, 89, 117, 172, 183, 184]) or change the control strategy completely (for example [19, 61, 66, 86, 173, 185, 185]). It is also worth mentioning that sometimes the state machine does not involve any electronics, and is implemented using mechanical components only [164, 178, 179].

Action sublayer

The goal of this second sublayer is to generate a motor command, that can either be kinematic (angle or speed), or kinetic (torque or force).

Position profile (PPR) The goal of the position profile is to assist the user to move according to a predefined trajectory, supposed to be the intended one. The trajectories can be described in joint space or Cartesian space, often called 'foot locus' for this second case. These trajectories are usually completely predefined based on recorded gait data from healthy people [48, 89, 91, 145, 186,187,188]. Databases of recorded trajectories from different healthy people have also been used in some strategies, where the controller chooses which trajectory to use depending on the situation [67]. In another approach, the trajectories have been recorded as a therapist manually guided the subject's legs to achieve a desired gait pattern [187]. In [189], recorded trajectories from each subject walking in the exoskeleton in passive mode are averaged and used as reference. Some small modifications are generally necessary to account for user-specific and device-specific differences before actually using the trajectories recorded from healthy people for patients.

In many cases, the trajectories are significantly changed or fully generated at runtime, and some papers are completely dedicated to the problem of optimization/generation of trajectories [190,191,192,193]. In some studies, model-based computations [194,195,196,197] or polynomial minimum jerk trajectory generation methods [94] have been used to generate the trajectories offline. Trajectories can be generated so as to reach a certain target position/orientation in task space as well [191, 198]. For simpler implementations, the trajectory may also be defined approximately by a final target angle and a speed limitation instead of the complete path, and has been used for pneumatically actuated exoskeletons [88, 174].

However, the trajectories are not necessarily fixed or predefined. Online modifications can be applied to the baseline trajectories, as is the case in [199] and for the hip trajectories in [20] and [89] (only abduction/adduction angle in the latter). In [181], the user is free to move the legs during stance, and the baseline swing trajectory (from healthy subjects) is adapted at every step to match the leg configuration at the end of stance. More advanced methods have recently been proposed to automatically adapt the recorded gait trajectories from healthy people to the environment, and generate new trajectories for different types of terrain [82].

The trajectories could also be generated online, for example synthetic and parametrized trajectories can be used to adapt the foot clearance, step length and duration, peak joint flexion, etc. [77, 200]. The authors in [130] have proposed to generate the leg movement online to match the step length measured by a walker which is moved manually by the subject. In [192], a method is proposed to calculate the joint trajectories as a function of the movement of the crutches by the user's arms, based on synergies extracted from the data of healthy subjects walking with crutches. Some controllers that are based on AFOs predict the joint trajectories online based on the estimated gait frequency and phase [190], and the future positions could be used as the reference for the actual joint [13, 56, 142]. Phase information estimated by AFOs has also been used to generate a custom trajectory in order to approximately achieve the desired power output [137]. In a different approach, the trajectory is generated online before each step based on the spring-loaded inverted pendulum (SLIP) model, taking the dimensions of possible obstacles into account [201]. For exoskeletons targeted at hemiplegic people, the movement of the nonparetic side at each step has also been recorded and used as the reference trajectory for the paretic leg [202, 203]. In a similar approach, kernel-based nonlinear filters have been used to learn the movements of the nonparetic leg as a function of gait phase online, and the learned functions are then used to generate the reference trajectory for the paretic leg [204].

Using position profiles is often associated with rigid position control in the full mobilization case. Then, the position profile is simply played back over time [27, 32, 33, 131]. The challenge is then to generate a set of gait trajectories that are comfortable, stable and able to overcome obstacles. For partial assistance, it is associated with impedance control [13, 145, 182, 205,206,207]. These trajectories can be played back over time [89, 205], or may be time-invariant (a tunnel or force field around the nominal path) [17, 181, 197, 208,209,210,211]. In [137], the reference profile is artificially generated and tuned to achieve a certain pattern of assistance. A combination of rigid trajectory tracking for some degrees of freedom and partial assistance around a trajectory for others has also been used [196].

The major drawback of the fixed-position-profile-based methods is their lack of flexibility, especially in the case of full mobilization. Even with many of the online modified or generated trajectories, the user is still forced to walk with the given gait pattern, which may not be suitable, and the trajectories are often specific to a particular terrain. For the partial assistance paradigm, even though the user has the freedom to diverge from the profile, it is still imposed and the controller will try to push in that direction, which might not necessarily help the user.

Torque profile (TPR)

Using a torque profile is the most simple and common method for partial assistance. A torque profile can be played back over time when it is triggered by an event [48, 96,97,98,99,100, 104, 111, 115, 119, 121, 123, 125, 151, 154, 165]. As the timing is very important, the torque profile may have (possibly online) tunable delay at the beginning of the torque profile. The torque profile itself may change over time, and be optimized online [105]. The torque profile can be as simple as a square pulse [103]. In some studies, the torque profiles are fine-tuned offline based on subjective feedback from the users [136, 212] or previous measurements from the users [169]. In others, they are optimized online for metabolic cost reduction [105, 213, 214]. The gait phase can also be estimated continuously, so the torque is applied as a direct function of the phase, independently of the time [50, 122, 136, 138,139,140, 143, 215,216,217], or combined with other inputs [51, 137, 141].

Probably the simplest case is the constant extension torque profile applied to the knee joint, when the leg is in single stance [176].

Impedance controller (ZCT) Impedance control is a widely used method in rehabilitation robotics and many other fields where the mechanical interactions with the user and the environment are significant [218]. As already mentioned, this method is used mostly in partial assistance paradigms where the human limbs are considered as active elements. Impedance control is often implemented such that the user gets the assistance torque only in case of a large deviation from the intended movement. This is usually called 'assist-as-needed' and is mainly used for rehabilitation training, since it is believed to induce more active participation from the user compared to constant assistance or full mobilization, thus improving the learning and recovery.

In practice, impedance control can be implemented as an M/K/B (inertia/stiffness/damping) based dynamical system relating joint angles to torques [47, 49, 89, 127, 137, 153, 210, 219,220,221,222,223]. Either a reference target trajectory is played back over time [38, 145, 206], or the target is fixed and changes (also the stiffness and damping) only when the gait state changes [137, 166, 172, 180, 224,225,226]. In both cases, the target trajectory is generally in joint-space.

Another type of implementation is to use a force field with the joint states (angle, speed, acceleration, etc.) as inputs [181, 204, 227, 228]. A variation of the force field is the flow field controller proposed by Martinez et al. [229], which can also use the 'state' given by several joints, while applying torque only at one [168]. A combination of both the force field and the flow field is suggested by Jabbari Asl et al. [230]. Note that using a multi-dimensional force-field in foot-locus-space to assist the leg to follow a pre-defined trajectory (such as the strategy used in [211]) is time-invariant, and is not the same as playing back a reference trajectory, even if both are classified as impedance control.

The impedance controller is usually implemented in software by changing the motor torque depending on the position and movement of the joint, but it can also be implemented using mechanical elements only (see "Torque control"). In [231], a negative impedance is tuned to compensate that of the leg to make walking less demanding for the user, since less effort is required to generate the same movement of the legs.

Finally, another possible strategy is to 'attract' the joint to its future position with a virtual stiffness field [86, 142]. The future position can be predicted by exploiting the periodicity of the gait. The trajectory is typically identified online with an adaptive-frequency oscillator (AFO). This is equivalent to impedance control with the time-shifted identified trajectory as a target.

Muscles activity amplification (MYO) A joint torque that depends directly on the measured muscular activity is simple and can be very effective, since it can detect the intention of the user before the movement starts. However, it is usually limited by the fact that electromyography (EMG) sensors are time-consuming to set up, the signal amplitude may change because of changes of skin conductivity and muscles fatigue, and that some muscles are not accessible with surface electrodes. In addition, this technique becomes even more difficult in case of neurologic impairment. In fact, the muscles may have a lower contraction which reduces the amplitude of the measured voltage and hence the signal-to-noise ratio (SNR). This method is simply not usable with people affected with complete paraplegia because there is no voluntary stimulation of the muscles. It also cannot help people affected with coordination troubles, which would just be amplified by the device.

In this method, generally the calculated torque is directly applied to the joint [96, 116, 232,233,234, 234,235,236], but in some papers the torque is fed to an admittance model to generate position commands for the low-level controller [237, 238]. In terms of the approach to calculating the intended torque from muscle activity, several variants can be distinguished:

The amplification of independent muscles activities is typically implemented with one artificial muscle per biologic muscle [239]. Its advantage is that the co-contraction of the biologic muscles also produces co-contraction of the artificial muscles, which allows to amplify both the torque and stiffness of the muscles. This can also be implemented with a single muscle; however, in this case, the biologic co-contraction will make the orthosis produce net torque. This approach has also been used in ankle exoskeletons with only unidirectional actuation (e.g. plantarflexion assistance only) [239, 240]
The differential amplification of muscular activity computes the assistive torque by computing the difference of the activations [241]. Co-contraction just results in less torque. However, it may be approximative because of the non-linear activity/torque relationship.
A variant is to let one activation inhibit the other [242].
Instead of assuming the joint torque proportional to the raw measured activation, an alternative is using a calibrated musculoskeletal model to compute the joints torques from the measured activation [243].

In some approaches, muscle activity amplification is guided by a gait phase estimation method, where the activity of a certain muscle causes assistance only during a specific period of the gait cycle [116, 233]. A thorough review of these techniques can be found in [244].

In [245], the EMG-torque relation is estimated online during swing using a physical model. In [133], capacitive sensing is used instead of electromyography (EMG).

Direct joint torque estimation (JTE) The biological joint torques required for performing a certain movement can be estimated approximately using a simplified model with several weighted segments, and then (completely or partially) applied with an exoskeleton. Such a method has been used to assist squatting [92, 246, 247] or stair ascent [248] assuming quasi-static movement (neglecting inertia terms and only compensating the weight). A similar approach has been used in [49] to assist gait in different terrains (level ground, stairs, ramp) in conjunction with other strategies. In [249] the authors have used inverse dynamics (4 sets of equations depending on the contact point(s) with the ground) to estimate the joints torque. Another method that does not rely on an accurate model, is using ground reaction forces, shank angle, and shank length [250, 251]. It has also been proposed to use a spring-loaded inverted pendulum (SLIP) model to estimate the required biological hip and knee torques [252]. The point foot approximation is made, and the controller requires hip/knee joints angles, ground reaction forces, and center of pressure (CoP) position obtained with an instrumented treadmill. Similarly, in [253] the required stance ankle torque to compensate the effect of gravity has been derived based on a simple 2-DoF compass gait model. A mass model and ground reaction forces are used in [254] to estimate the hip and knee torques during gait, but the exoskeleton is actually not actuated.

Model-computed action to keep balance (BAL) Some control strategies address the issue of balance during gait based on different mathematical models of walking. For full mobilization exoskeletons, provided they have enough actuated degree of freedoms (DoFs) and that the user does not interfere, walking controllers developed for humanoids have been used [20, 194, 255, 256]. In [89] hip abd/adduction trajectories during swing are adapted online to improve lateral balance based on the 'extrapolated center of mass' concept. In another approach, the difference between model-computed and actual GRFs have been fed to an admittance model to update the predefined trajectories online [198]. In the partial assistance paradigm, Zha et al. [257] have developed a controller only assisting in case of loss of balance, which is detected based on a quantitative balance metric. A model-based assistive torque is then calculated as the weighted sum of gravity, Coriolis, and inertial terms with weights determined using fuzzy logic.

Neuromuscular model (NMM) A class of bio-inspired controllers attempt to mimic the human neuromuscular system, consisting of virtual neurons and muscles. These virtual muscles are mathematical models based on the Hill-type muscle model [258] that generate torques as a function of the activation signal and the current muscle states (which are in turn a function of joint angles and angular velocities). The torque applied to each joint is then obtained as the algebraic sum of the torques generated by the virtual muscles acting on that joint. Some of these controllers are based on the neuromuscular reflex model from Geyer and Herr [259]. This bio-inspired model works based on feedback loops, or 'reflexes', that receive joint position information, ground contact and virtual muscle lengths as inputs, and generate activation signals for the virtual muscles. This concept was initially proposed as a model that can reproduce gait patterns similar to the natural human gait.

The reflex model has often been used to control prostheses, but implementations can also be found in the exoskeleton literature. In some modified versions, the activation signals of the muscles generated based on the reflexes are augmented with central signals generated by AFOs [51, 260], although in [260] and similar studies [261, 262] only the reflex-based controller has been tested with subjects. The activation can also be a function of electromyography (EMG) signals measured from the user's biological muscles [243, 263]. In addition to joint torques, the neuromuscular model has also been used to determine stiffness [264]. In another work, the use of a neuromuscular model (which is explained in [265] and is different from the one used by the rest of the papers) has been mentioned, although it is not clear how it affects the proposed controller [266].

The main advantage of the neuromuscular model method is that it does not require a predetermined trajectory, and therefore does not impose the motion on the user. It rather follows the movements of the limbs and adapts to them, while being able to reject external perturbations. However, to operate properly, many parameters need to be tuned which can make the tuning process lengthy. Automated optimization with simulation tools is efficient, but such a process is difficult to implement with the actual hardware and user. Moreover, this method by itself is not suitable for complete spinal cord injury SCI patients since the user needs to at least initiate walking.

The neuromuscular reflex model has also been used in simulations of other assistive controllers to model the behavior of the human limb [152, 267]. In these papers, the neuromuscular model is simulated in parallel with the controller, receiving the torques generated by the controller as input and producing joint angles and speeds, which are fed back to the controller.

Body weight support (BWS) Body weight support was initially proposed as an augmentation to gait rehabilitation training, using stationary over-treadmill suspension systems [268]. The same idea can also be implemented using wearable lower-limb devices. Instead of providing assistance at the joints to move the legs, the idea is to relieve the user from a part of his/her weight, by having the exoskeleton pushing the trunk upward [11, 12]. This mainly works for the knee joint, because in stance, the partial gravity compensation consists simply in applying an extension torque. Note that this method is different from model-based gravity compensation which calculates joint torques required to resist gravity (e.g. the 'gravity compensation control approach' in [92]). The latter approach has been categorized as 'Direct joint torque estimation' in this review.

Direct joint control by the user (DJU) The joint torque can be directly controlled by a user (the wearer of the device or an external person such as a physical therapist), but this requires high cognitive load and prior training. This method has rarely been used, an example being [269] in which the pressure supplied to an artificial pneumatic muscle is proportional to the press of a button, controlled by a physical therapist or by the wearer. The actuator is used in an ankle exoskeleton to provide plantar flexion torque. In this study, the therapists could learn to properly activate the device to provide effective assistance, but most of the subjects could not successfully do it over 2 sessions.

An equivalent method for position-control also exists [270]. In this case, a pole is linking each foot the ipsilateral hand, with a multi-axis force sensor. Using an admittance controller, the position-controlled joints move according to the interaction force exerted by the hands, so that the feet 'follow' the hands.

Other function of feet/joints instant states (FJI) The instantaneous values of the sensors such as joint angle or ground reaction force can be provided as inputs to a custom memory-less function, that directly computes joint torques [165, 234, 271] or positions [272,273,274]. Occasionally, electromyography (EMG) signals have also been used [275]. This information can also be supplemented with an estimate of the gait frequency [276]. Note that the type of functions used in this category does not fit into common strategies such as model-based torque estimations or virtual impedance functions. In [72], jumping is assisted at the ankle level by an impedance-like function producing an ankle torque proportional to the angular speed of the shank. In [116], the actuator pressure is proportional to the hip angle or the ground reaction force, depending on the current state (state machine triggered by a threshold on the ground reaction force value). In [277, 278] a passive mechanism using springs has been designed to compensate the gravitational forces such that the leg is approximately in static equilibrium in all configurations.

This method can also make the paretic leg follow the motion of the healthy limb in people with asymmetric pathologies [272], but this method is usable only if the movements of both legs should be symmetric, which is the case for sit/stand transitions (or jumping with joined feet) but not walking. A similar but more sophisticated method is estimating the desired trajectory of the paretic leg as a function of the instantaneous movements of the healthy side, based on inter-joint synergies derived from healthy gait [279,280,281].

Other dynamical function of feet/joints instant states (FJD) In a similar manner to the FJI category, although much less common, custom dynamical functions can also be used to calculate the desired action. In [282] the hip torque is computed as proportional to the difference of the sine of the hip angles, delayed by approximately 0.25 s. This makes the assistance torque adapt almost instantly to the variations in the gait cadence. In [227], gait-cycle-iterative corrections (as a function of the positioning errors in the previous steps) are applied to the baseline torque which is calculated using an impedance controller.

Low-level control

This last layer is the closest to the actuators and therefore inevitably device-dependent. Most of the methods are not limited to exoskeletons but rather shared between many robotic applications, and the fact that they are being used in a gait assistance device does not affect the desired behavior (i.e. tracking of a reference input accurately while remaining stable). Therefore, papers focused only on low-level methods for exoskeletons and gait assistance devices are rare. Hence, we will limit this section to an overview of the existing methods and their relevant characteristics for gait assistance devices, without an exhaustive discussion about each method.

Actuators used in robotics are generally direct-current motors that are current-driven, and the field of wearable robotics is no exception. This current regulation is performed by a high-frequency (typically $\ge {10}\;{\text {kHz}}$) inner control loop. The target current is determined depending on the type of low-level controller. The motor then transmits its torque to the load via a transmission system. Traditionally, rigid transmission systems such as gearboxes, ball screws, and belt drives were most prevalent, but introducing compliant elements into the transmission is becoming increasingly common in the applications involving interaction and force control. This added compliance improves the safety of interaction and the fidelity of force control. Bowden cables are also frequently used in exoskeletons, since they allow the transmission of forces over longer distances, making it possible to place the actuators more proximally or even off-board to decrease the burden of added inertia on the user. Devices with off-board actuators (also known as tethered) have been proposed as research test benches to compare the effectiveness of different control strategies independently of the device [283, 284]. Another category of compliant actuators frequently used in exoskeletons is pneumatic actuators, most often in the form of artificial muscles, which offer advantages such as low weight (neglecting the weight of the off-board compressor) and desirable passive properties. Finally, some assistive devices do not use any actuators but rather rely on passive elements that can store and release energy. Hybrid actuators have also been proposed, combining more than one actuator type per joint [285]. The distribution of the actuator types in the reviewed articles is shown in Fig. 5. For a detailed review of the different actuation technologies and particularly the compliant ones, the reader is referred to [286, 287].

While actuators with compliant properties are often used in partial assistance devices, actuators with rigid transmission are still the standard in full mobilization exoskeletons. Accordingly, in the low-level control, full mobilization exoskeletons use position controllers but partial assistance devices mostly rely on force/torque control schemes. In our classification of low-level controllers, we make a general distinction between position or speed controllers against torque controllers. The torque control category is then further divided into different methods.

Position/speed controller (POS)

The rigid position control is usually performed with a proportional-integral-derivative (PID) regulator. As most actuators have a large gear ratio and significant damping, the position control is usually straightforward. More advanced techniques exist [288,289,290,291,292], although such high positioning accuracy is generally not required for exoskeletons, because the structure is often slightly flexible and makes the legs movement less precise anyway. Moreover, relying on highly precise movements is not practical when there is some level of variability in the environment and the user can also affect the movement (e.g. using the upper-body) in unpredictable ways. Some of the more advanced controllers have focused on adding more compliant behavior to the position controller, such as the so-called 'proxy-based sliding mode controller' [189, 293, 294], which offers smooth and gradual recovery in case of large errors. An iterative (over several gait cycles) online optimization of the torque profile to get the desired joint trajectory is presented in [227].

Torque control

The torque control is more challenging, because it requires a high bandwidth. A review of many low-level torque controllers can be found in [295]. For the case they tested (regular gait on a treadmill, Bowden cable transmission), they found that a proportional-derivative (PD) controller with iterative learning compensation was the best performing.

Open-loop feedforward torque control (OLT) Open-loop torque control is often chosen because it requires no torque sensor, which makes the hardware simpler. Two ways exist for its implementation. The first way is to set the motor current using a model of the actuator, including rotor inertia, dry friction, and damping [122]. This method is intended for stiff exoskeletons. Unfortunately, the inertia is hard to cancel because the acceleration is estimated from the position, which amplifies the measurement noise. The friction is also difficult to compensate because of its complex modeling. The second way, suitable for soft exosuits, is to run position control with a model of the stiffness of the system [112, 113, 143]. In [296], an admittance controller (force-to-speed) is followed by a speed control loop, to control the force. However, the cable-driven soft exosuit has a behavior that is too non-linear to get a consistent performance with the closed-loop control only. A feedforward component is then added, using a model that includes the suit stiffness, the actuator dynamics, and a thigh motion model (hip angle to cable retraction).

Fast closed-loop torque control (CLT) The closed-loop torque control is the classical way of controlling an accurate torque. It requires a torque sensor for feedback. The motor can be rigidly coupled to the joint (possibly through gears) or via a spring. The latter is called a series elastic actuator (SEA) and trades off some tracking bandwidth to get a better perturbation rejection performance. In other words, the softer the spring, the higher the torque regulation capability (a larger movement is necessary to achieve the torque perturbation), but the lower the ability to change torque fast (the motor has to spin more to achieve the torque variation). An advanced method to control the torque of a series elastic actuator (SEA) can be found in [297].

Gait-cycle iterative torque control (ITT)

Instead of controlling directly the joint torque with conventional fast closed-loop control, a position or speed sequence can be played back with a compliant actuator, which can do approximate torque control based on the actuator's force-length relationship. This is well suited to systems that are soft and difficult to control. At the end of each step of the gait, corrections are made according to the comparison between the achieved and desired torques. Since the reaction time is one step, this is only accurate if the gait is periodic and regular, which is typically the case on a treadmill. The displacement/torque relationship can be estimated before the experiment, and a constant motor speed control results in the desired force profile [113, 114]. In [101], position-control is used on a Bowden cable to follow a trajectory, which translates to a torque at the ankle as a result of the elasticity of the exosuit. The trajectory is manually adjusted online to get the right force profile. In [109], a fixed voltage profile is triggered some time after the heel strike. It is also possible to tune the target trajectory online to get the desired work or average positive power [16, 102, 137]. In [16], a target trajectory is tuned online to get the desired average torque. In [298], a speed profile is tuned online instead. In [112], the speed/position profile is tuned online over several gait cycles, to get the desired torque profile.

Special passive mechanical properties (PME) It is possible to exploit the passive mechanical properties of the actuator, to benefit from some control properties that would require a larger and more complex actuator to emulate them with conventional force control, or additional sensors. A first example are the pneumatic actuators, that are compliant and with a limited tracking bandwidth. This is exploited in [234], where the 'bang-bang' pressure controller does not result in a square torque profile, because of the smoothing by the limited actuator dynamics. In [226], the compliance of the locked actuator is used during stance. Actuation does not have to be bi-directional (e.g. pneumatic artificial muscles and Bowden cables can pull but not push), and this property is used to temporarily 'disconnect' the actuator from the exoskeleton mechanically, to get a passive high-performance 'transparency' (zero torque) without the need for a torque sensor [114]. In [26], the knee is position-controlled, but also features passive variable stiffness thanks to an additional actuator that controls the pre-tension of a spring. Achieving such compliance with a single actuator would not be possible, because of the high gear ratio and high inertia of the motor. A magnetorheological damper is used in [65] to vary the damping around the joint in different gait phases. The exoskeleton described in [72] uses a magnetorheological clutch, linked to a motor always running at full speed. This also makes it possible to mechanically 'disconnect' the joint from the motor and get transparency, when the clutch is off. A similar system was presented in [299], but using a dual conventional clutch able to apply torque in both directions. In [300], the supply pressure of the pneumatic actuators is calculated so as to achieve a desired compliance (or equivalently, stiffness). In another study, a clutch is used to connect and disconnect a spring and a DC motor which is running during 85% of the gait cycle to stretch the spring, and is disengaged during the push-off period to let the spring release the stored energy and assist the ankle [178]. In a similar but simpler approach, a DC motor is used to compress a spring during stance (and this compression is also augmented by the dorsiflexion of the human ankle), and the stored energy is released at push-off [301]. Thus, using the spring as a passive element allows using a lighter motor with a lower power output.