Robotic exoskeletons are wearable mechanisms capable of augmenting, restoring, or assisting the function of human limbs by acting in parallel (Pons, 2008). This technology can be applied in many fields, ranging from industrial (Fox et al., 2019) or military (Chu et al., 2006) domains, where the user is empowered to perform a heavy task, to space teleoperation (Lovasz et al., 2017) or health care (Cardona et al., 2020). More specifically, two of the fields in which lower-limb robotic exoskeletons have shown promising results are rehabilitation (Louie and Eng, 2016; Holanda et al., 2017) or assistance (Yan et al., 2015a) of human gait.

In this context, significant research efforts have been made to develop and improve wearable robotic devices that provide proper assistance during gait. A considerable amount of works have addressed different aspects of the design, control strategies, and experimental validation of these devices. To understand and organize the large body of information on this topic, several authors have reviewed the development of lower-limb robotic exoskeletons from multiple perspectives [see (Sanchez-Villamañan et al., 2019) for a complete review of compliant actuators currently used in robotic exoskeletons or (Pinto-Fernandez et al., 2020) for a detailed analysis of performance metrics, for instance].

In terms of control strategies, Tucker et al. provided an overview of the different control strategies for lower-limb robotic prostheses and orthoses and introduced a three-level paradigm controller development (Tucker et al., 2015). This same paradigm was recently updated and completed by Baud et al., who systematically analyzed the control strategies of lower-limb exoskeletons by dividing them into functional units (Baud et al., 2021). Considering these three-level paradigms, Ma et al. performed a deeper review of the high-level controllers responsible for the voluntary control of robotic devices (Ma et al., 2019), while Miscon et al. focused on middle-level controllers, in particular, joint trajectory generation for robotic exoskeletons (Miskon and Yusof, 2014). Low-level controllers responsible for direct actuator control were reviewed by Meng et al. (2015). The review proposed by Yan et al. also focused on control strategies for lower-limb exoskeletons, more specifically, on strategies that assisted user’s gait (Yan et al., 2015a). Similarly, Li et al. also reviewed control strategies for lower-limb exoskeletons but centered on rehabilitation purposes (Li et al., 2021).

Considering the number of actuated joints, robotic exoskeletons can be classified as single-joint or multi-joint (former, if they actuate over one unique joint; latter if more than one joint is assisted) (Yan et al., 2015a). Some authors have focused their reviews on single-joint exoskeleton robots. For instance, ankle robots have been reviewed by Mills et al. (2010) and also by Shi et al. (2019), while Chen et al. analyzed both knee (Chen et al., 2019) and hip (Chen et al., 2020) devices.

In contrast with complete lower-limb exoskeletons, which act over the hips and knees (even ankles) of both limbs, partial exoskeletons act partially over the locomotor system of the wearer by exclusively assisting one joint (single-joint exoskeletons) or one leg of the user (unilateral exoskeletons). Although they present several advantages [they are simpler and lighter than bilateral devices (Baud et al., 2021) and can target the specific function of the assisted joint during gait (Yan et al., 2015a)], they need to ensure proper interjoint coordination, especially with non-actuated joints. Unlike complete exoskeletons, which can impose the appropriate coordination between joints and legs, partial exoskeletons cannot actuate globally in the entire locomotor system; therefore, this interjoint coordination needs to be resolved by the controller of the device. Such coordination requires a dual interaction with humans: cognitive and physical. Proper delivery of the assistance is required to ensure that the user can benefit from the exoskeleton’s assistance, but also, a predictable timing that matches the user’s pre-estimation is required to achieve the exoskeleton’s embodiment. This would lead wearers to assimilate the robot’s action; so the exoskeleton would not be used as a tool but as a part of the user’s body (Li et al., 2021).

Understanding strategies that achieve proper coordination of human and robotic systems has become crucial, especially for those devices which aim to rehabilitate or assist the gait of impaired walking users. Although coordinated operation is crucial for the proper operation of a partial robotic exoskeleton, it has not yet been systematically analyzed in previous works. The main objective of this paper is to perform a comprehensive analysis of the different control strategies that are used to coordinate the action of partial exoskeletons with the user’s gait to ensure proper interjoint coordination. We have also included those strategies initially developed for bilateral or complete exoskeletons, that could be used to synchronize the action of partial exoskeletons. We have paid particular attention to the assessments and validations that the authors have carried out with their devices and strategies. The reported effects of each work on impaired or unimpaired walking subjects have also been summarized in this review. The content of this paper is organized as follows. Section 2 reports the literature search methodology that we followed. Section 3 describes the state of the art, organized into the five main coordination strategies that we identified. Finally, section 4 and section 5 discuss and conclude the main findings of this review.

We conducted a literature search using two different databases: Scopus and Web of Science from January 2004 until December 2021. To obtain results that cover the coordination issue between humans and robots, especially in unilateral or single-joint devices, we used the following search query in the title, abstract, and keywords:

Topic = {leg OR hip OR knee OR ankle OR foot OR [lower AND (limb* OR extremity OR body)]} AND Topic= (power* OR robot*) AND Topic = [ortho* OR exoskeleton* OR (wearable robot*) OR (portable robot*) OR (robot* suit) OR (robot exosuit)] AND Topic= (control* OR validation* AND Topic= (coord* OR unilateral OR (mono joint) OR (single joint) OR ((sound OR impaired OR paretic) AND (leg OR limb)) OR (hemip*))

Inclusion criteria for this review were as follows:

1) English full-text journal articles or conference proceedings.

Exclusion Criteria Included

1) Documents that only described the mechanical structure of the device or the design of actuators or new materials intended for gait assistance.

The initial number of articles (844) was reduced to 671 after looking for duplicated documents. After checking the title and abstract, we discarded 467 papers and 204 were selected for full-text reading. Based on the authors’ experience and the bibliography of the reviewed articles, 34 documents that were not included in the initial search were also considered for full-text reading. Then, we selected the 161 documents that fulfilled the inclusion criteria to be reported in this review. Figure 1 shows the flow diagram of the literature search and the document selection procedure.

Our analysis of the selected documents was focused on two main aspects: 1) coordination strategies with the actual user’s gait and 2) experimental validation of the devices regarding the subjects (number and pathology) and obtained results. The control strategies were analyzed regardless of the device’s morphology or actuation principle, based on the working principles that ensure proper interjoint coordination between the robotic exoskeleton and the user’s gait. The individual details of each reviewed paper are shown in Supplementary Table S1.

Across the literature, we have identified five strategies to synchronize the action of wearable robotic devices with human gait. The most extended methodology exploits the cyclic nature of human gait by using a finite state machine. Some authors leverage this cyclic property to estimate the continuous gait phase, which is a function that increases monotonically from 0 to 100% between consecutive heel strikes, and use this variable for the timing of the robot assistance. Other methodologies are based on the activity generated by the user, relying on either the movement itself or the muscle activity. Finally, mathematical tools have been developed, as in the case of central pattern generators that simulate the full gait dynamics of a subject. Figure 2 shows a diagram of the proposed classification.

In this paper, we have reviewed and classified the main control strategies that aim to synchronize the action of partial robotic exoskeletons with the current gait of their users. The distribution of the analyzed literature is represented in Figure 8. We have grouped the articles in this review according to their coordination strategies. The FSM-based coordination strategy was the most frequent, appearing in 34.8% of the reported articles due to its simplicity and favorable results. The second most common coordination strategy was based on continuous gait phase estimation, which occurred in 28.6% of the total reviewed articles. Among all of them, the methods based on the user’s kinematics were 21.1%, while the approaches based on muscular activity or CPG were only 11.8% and 3.7%, respectively.

Regarding the number of devices that have been used in each coordination methodology, Figure 9 represents the ratio of articles per device that have been reported in each strategy. This ratio indicates the average number of papers that reported results with the same device, so a high ratio means that the published articles involved few different devices, and therefore, the application of the control paradigm is not as widespread as it could seem. Remarkably, the highest ratios correspond to the time-based gait phase estimation and the myoelectric proportional coordination strategies. This is because the Soft Exosuit from Harvard University (Bae et al., 2015, 2018a, 2018b; Ding et al., 2016a, 2016b, 2017, 2018; Lee et al., 2016, Lee et al., 2017 G.; Awad et al., 2017a, 2017b; Kim et al., 2019; Siviy et al., 2020) was used in 68.4% of articles that estimated the continuous gait phase using the step time, whereas the HAL prototype (Kawamoto et al., 2010; Nilsson et al., 2014; Sczesny-Kaiser et al., 2019; Tan et al., 2018, 2020; Watanabe et al., 2014, 2020) and the ankle-foot-orthosis from the University of Michigan (Cain et al., 2007; Gordon and Ferris, 2007; Sawicki and Ferris, 2008; Kinnaird and Ferris, 2009; Kao et al., 2010; Koller et al., 2015, 2017) supposed, each one, the 43.8% of articles that reported results using a myoelectric proportional controller.

Figure 10 illustrates the cumulative frequency of articles related to each identified coordination strategy. As previously mentioned, FSM-based methods have been postulated to be the most frequent technique. However, the increasing tendency of algorithms that estimate the continuous gait phase is remarkable, surpassing muscular coordination and kinematics-based techniques, although their use started several years later. This implies the high interest aroused by these algorithms as tools for successfully coordinating the action of partial exoskeletons.

Regarding the experimental validations that the authors proposed for their devices, we have identified three main kinds: 1) technical validations that only assessed the generation of the assistance without analyzing its effects, 2) validations where the assistance was provided to unimpaired walking subjects, and 3) validations where the assistance was provided to impaired walking subjects. An overview of the distribution of the experimental validations in the reviewed articles is depicted in Figure 11.

Regarding the technical validations, it is a common practice that some authors reported the feasibility of their devices to provide the commanded assistance. However, some of them merely present these technical results without assessing the effects on the targeted population in subsequent studies (Aoyagi et al., 2007; Fleischer and Hommel, 2008; Shorter et al., 2011; Kim et al., 2015; Wu et al., 2015; Zhang et al., 2016; Huo et al., 2019). Therefore, the validations of these exoskeletons are very limited and, although they can generate the desired assistance, they lack the assessment of the main objective of the device, i.e., assisting the gait of the user.

The main difference between experimental validation with impaired and unimpaired walking subjects is remarkable. Device validations with impaired walking subjects were mainly focused on the effects of the gait kinematics, as indicated by the high ratio (78.1%) of papers reporting such results. In contrast, the articles with unimpaired walking subject validations reported this information only in 33.3% of the articles, while focusing equally on muscular response (31%) and metabolic cost (35.7%). Compared to these ratios, the validations with impaired walking subjects reported muscular and metabolic results only in 9.4 and 12.5% of the cases, respectively.

These differences between the validation with impaired or unimpaired walking subjects show that the literature focused on different objectives for different kinds of users. While the assistance of unimpaired walking subjects was intended to reduce their muscular effort and metabolic cost during gait, the assistance of impaired walking subjects focused on the functional aspects of gait. While this is logical because the primary purpose of the assistance for these subjects is to achieve an autonomous and stable gait, it also shows a lack of understanding of how impaired walking subjects react to the assistance provided; especially whether they were able to integrate the robot action and consequently adapted their muscular activity due to the robotic assistance.

Among the validations performed on impaired walking subjects, 73.1% focused on the instantaneous effects of the applied assistance during walking. However, some authors have explored the effects of the robot’s action on the gait of the impaired walking subject after long-term rehabilitation therapies, and 26.9% of the studies that involved impaired walking subjects focused on the therapeutic benefits of using robotic devices. However, these studies focused on six different devices: anklebot (Roy et al., 2013; Forrester et al., 2016), AlterG Bionic Leg (Wong et al., 2012; Stein et al., 2014), HAL exoskeleton (Kawamoto et al., 2009; Nilsson et al., 2014; Sczesny-Kaiser et al., 2019; Tan et al., 2018; Watanabe et al., 2014, 2020), T-Flex (Gomez-Vargas et al., 2021), the first (Banala et al., 2009) and the second version (Srivastava et al., 2015) of the ALEX prototype and the Samsung’s GEMS device (Lee et al., 2019).

The promising results obtained in these studies showed that robotic therapies based on coordinated gait assistance of the impaired limb are valid approaches to rehabilitate subjects with impaired gait. However, the role of these strategies in the muscular recruitment and physiological response of users is not yet clear, although these aspects are quite relevant to the success of rehabilitation therapy. Consequently, more studies are still needed to fully validate rehabilitation therapies that could be performed using the control algorithms and devices detailed in this review.

Another interesting aspect to consider is the number of subjects involved in the experimental validations. As shown in Figure 12, most of the reviewed papers involved a small number of subjects, limiting the level of significance of these works. The average numbers of subjects involved in the experimental validations were 5.6 for unimpaired walking subjects and 7.65 for impaired walking subjects. In this regard, only 11 of the reviewed works involved more than 15 subjects, all involving impaired walking subjects. Using a reduced sample size can be justified in studies that validate a device’s technical approach. However, rehabilitation or motor control studies should include a larger sample size to guarantee the statistical power of the conclusions.

Regarding the differences between the reported algorithms and devices, the variety is remarkable, not only in the coordination strategies, which is the scope of this document but also, in the low-level controllers and their actuation principles. There is a lack of comparative studies that establish the effects of these aspects in the assistance provided to the user. According to the reviewed literature, only a few studies have tried to compare the performance of the proposed device, usually focusing on the therapeutic outcome (Forrester et al., 2016; Lee et al., 2019; Sczesny-Kaiser et al., 2019; Stein et al., 2014; Watanabe et al., 2014, 2020), although other authors have compared the performance of two different controllers with the same device (Cain et al., 2007; Vallery et al., 2009; Koller et al., 2017; Hassan et al., 2018; Martinez et al., 2019). However, it is not yet clear which is the best method to coordinate robot assistance with the user’s gait.

As previously noted by other authors (Pinto-Fernandez et al., 2020; Baud et al., 2021), we have identified a high variability in the experimental validations that authors carried out with their devices. The absence of common protocols to evaluate the effects of robotic assistance makes it extremely difficult to extrapolate the results published by different articles for comparison. In this regard, we encourage following a common benchmark when assessing lower limb exoskeletons and their effects, as Torricelli et al. previously proposed (Torricelli et al., 2015). This would include common clinical scales to evaluate gait quality, as well as concrete assessments to evaluate robot performance, which would also be applicable to the partial exoskeletons reviewed in this document. From this perspective, previous comparisons between robotic devices that did not follow the same validation protocol should be viewed with caution as they could lead to misunderstandings.

A standardization in the assessment of robotic exoskeleton performance is required to ensure proper transition of the exoskeletons from research to clinical and domestic environments. It is necessary the definition of new “Gold-standards” to objectively assess the performance of these devices and the implications in wearer’s activities. These metrics would unify the validation process of robotic exoskeletons and would justify the scientific evidence and clinical relevance of their role in gait assistance or rehabilitation. In this regard, understanding and estimating the torque and force transmission from the robotic device to the user and the biomechanical effects of this interaction is crucial, as it could boost the therapeutic outcomes involving exoskeletons in rehabilitation therapies (Lerner et al., 2017a; George et al., 2021). These methods would enable the comparison between systems as they directly quantify the exoskeleton performance and establish a common paradigm that can be used independently of the evaluated device.

In this document, we reviewed the coordination strategies of partial robotic exoskeletons in a hardware-independent mode. Therefore, we grouped the articles only according to the coordination strategies, without considering the final implementation of the system. In fact, we considered not only single-joint or unilateral exoskeletons, but also strategies that were only related to software development (Mishra et al., 2014; Gui et al., 2017) or implemented in complete (Durandau et al., 2019; Martinez et al., 2018, 2019) or bilateral (Sawicki and Ferris, 2008; Lerner et al., 2017a, 2017b) exoskeletons whose strategies could be adapted to be used in partial robotic exoskeletons. The descriptions of the coordination strategies that we proposed were as general as possible so that other researchers could adapt them to different exoskeleton configurations independently from the original implementation. In this sense, for example, an FSM strategy can be applied regardless of the detected concrete events, or an EMG strategy can be used independently from the muscle measured and involved in the control.

Although these differences in the morphology did not affect the implementation of the coordination methods included in this review, they could affect the results obtained by the assistance. We have found multiple actuator systems and low-level controllers that include DC motors coupled to gearboxes with position (Wei et al., 2019) or impedance controllers (Villa-Parra et al., 2017) and compliant actuators under torque control, such as series elastic actuators (Giovacchini et al., 2015) or cable-driven systems (Siviy et al., 2020). This variety does not affect the implementation of the coordination strategies but still it is relevant for the interaction between the user and the device as well as the outcomes of the assistance.

These actuation and interface differences also highlight the need of common “gold-standard” metrics that globally assess the performance of these devices independently from their concrete implementational details. In this sense, other aspects, such as safety or stability, should also be objectively assessed for these wearable robotic devices, as they are key-factor to consider for a proper translation of these prototypes into real-life devices. Even the control strategies of these devices require to be assessed to ensure the robustness of future commercial robotic exoskeletons.

Independent of these implementational aspects, we can compare the coordination strategies according to each strategy’s working principle and their input and output information. As mentioned above, FSM algorithms are the most common approaches that we found. This is because they are the simplest methods from the processing and sensory points of view. Although simpler, they can adapt the action of the partial exoskeleton to discrete events, ensuring the correct coordination of both systems, human and robot, at specific instants.

In contrast to these methods, the strategies based on the estimation of the continuous gait phase lead to a continuous signal that can be used by other stages of the controller. This signal was a more versatile source of information, as it enabled its use beyond some discrete events. Regarding the methodologies for estimating this phase, the AO based approaches have a more adaptive behavior than the step-time based approaches, as they can instantaneously react to changes in gait velocity, instead of only responding to gait events. In addition, AO methodologies are simpler and require less computational effort than kinematics-based phase estimation approaches that generally rely on previous data analysis or artificial intelligence techniques.

The methods based on muscular activity require the most complex sensory systems, as they need to acquire muscular activity in real time, reject electromagnetic noise, and ensure that the movement of the robot did not interfere with the acquisition process. Additionally, neurological patients who cannot reliably recruit the involved muscles are not suitable to use this approach.

Additionally, if subjects cannot successfully generate movement in the paretic leg, they could not use those devices coordinated by the kinematics of the assisted leg or by coupling restrictions between joints. However, this disability did not interfere with the coordination strategy if it is based on the movements of the non-paretic leg, although these methodologies are more complex and usually require previous experimental data. Finally, strategies based on CPGs that simulate the generation of cyclic patterns by biological organisms are quite promising. However, the complexity of these methods and the reduced number of related papers suggest that more research is needed to completely validate their use.

Although all the reported methodologies have arisen promising results, the previously mentioned differences have implications for the safety and clinical application of these control approaches. The simpler the control algorithm, the safer implementation and wider the range of suitable users, although their performance could be slightly worse. In this sense, FSM and AO algorithms could be suitable for a greater patient typology than EMG based algorithms, although they would imply not considering the muscular activity into the therapy.

The performed review has demonstrated that this technology has reached the readiness level that ensures the proper control of partial exoskeletons. Additionally, reported muscular recruitment and metabolic cost effects are clues that point to the potential of these devices to be integrated into neural gait motor control, especially in unimpaired walking subjects. However, a research effort is still needed to validate this integration in neurologically impaired subjects.

Independent of the synchronization strategy followed, successful coordination between robot action and user’s gait will lead to the embodiment of the technology. Devices capable of matching their action with the one expected by the user will lead in integrating the exoskeleton into the body schema so it will be treated as part of the body instead of an external tool (Li et al., 2021). However, a research effort is required to demonstrate the capability of this technology of being embodied, as it has already been demonstrated for wheelchairs (Arnhoff and Mehl, 1963; Pazzaglia and Molinari, 2016) or prostheses (Penner et al., 2019; Bekrater-Bodmann, 2020).

This embodiment is the necessary next step for these robotic wearable devices. It will maximize the potential benefits of gait assistance, not only during rehabilitation therapies but also in non-supervised environments while assisting daily life activities.

The reviewed state of the art on coordination strategies between user gait and partial exoskeletons indicates that these techniques are mature enough to generate proper assistance for walking to both impaired and unimpaired walking subjects. More specifically, the algorithms based on FSM were the strategy most frequent, being validated repeatedly. However, the algorithms that estimate the continuous gait phase are being postulated as more robust alternatives, gaining relevance in recent years, especially those methods based on Adaptive Frequency Oscillators, due to their robustness and adaptability to gait speed changes. However, in the current state of the art, there is still a lack of studies that systematically analyze and isolate the role played by these coordination strategies, independent of the physical device on which they were implemented.

Although the theoretical objective of most robotic exoskeletons is to assist impaired walking subjects, there is a lack of information about how these subjects integrate the provided assistance in the gait neural control. Currently, the neurophysiological consequences of robotic assistance in impaired walking subjects remain elusive, including adaptation of muscle recruitment. In this context, research efforts should focus on the final states of the impaired walking users, deepening the understanding of physiological and cognitive interaction with the device, to lead to more effective neurorehabilitation and assistive approaches.