1. Introduction

2. Search Methodology and Classification Method

Figure 1. Flowchart representing inclusion and exclusion criteria we adopted for paper selection.

Figure 1. Flowchart representing inclusion and exclusion criteria we adopted for paper selection.

3. Performance Evaluation Criteria

3.1. Criteria Selection

Assessing the emerging technologies of rehabilitation in PLLEs is hard and yet there are no standard benchmarks defined for their evaluation. Rapid development and delivery of PLLEs by research and industry groups also amplify the necessity of developing evaluation and benchmarking tools for them [33]. Consequently, authors in [34] introduced a method for assessing and benchmarking for wearable robotic devices, that can be adopted for rehabilitation PLLEs assessment as well, since PLLEs belong to the same family and share many similarities with them in their structure, design and the interaction they have with the human body. They proposed to consider two important perspectives for evaluating these devices: 1) how these devices affect motor functions of the subjects, and 2) the interaction quality between patient and the device, as shown in Figure 2. As functional evaluation considers stability, efficiency, kinematics and dynamics of the patient and exoskeleton as a whole, the interaction evaluation includes physical and cognitive aspects between human and exoskeleton and also takes into account safety issues for the patient.

In functional evaluation, performance is defined as the level of accomplishment of a certain task such as walking speed, or body displacement. It can be further divided it into two sub-modules, namely, stability as an index to assess performance in presence of disturbances and efficiency as an index to measure cost of transportation and energy consumption. The functional evaluation related to biomechanical aspects considers how PLLEs can compensate and improve walking patterns. This can be done through either the study of kinematic models where various aspects of the motion such as center of mass (CoM), center of pressure (CoP) or inter-limb coordination are important or focusing on dynamic parameters, where inertia, ground/contacts reaction forces and gravity are considered.

In interaction evaluation, physical human-robot interaction becomes of high importance and it measures how the interaction is exploited to assist during walking, not to limit the user movements and its capability in ensuring comfort of the user. Instead, cognitive interaction metric measures the gap between human wishes and robot actions.

Despite such a precise selection of evaluation perspectives and aspects, the presented method considers all the wearable robots and is not addressing rehabilitation PLLEs specifically. Consequently, functional and interaction aspects are defined as abstract concepts and require accurate and specific implementations to match to rehabilitation PLLEs and their expected outcomes. Furthermore, in many cases evaluation of performance aspects individually, does not lead to meaningful outcomes, while a combined set of aspects might give a better perspective.

Figure 2. Wearable robot (WR) evaluation perspectives to be considered as proposed by [34].

Figure 2. Wearable robot (WR) evaluation perspectives to be considered as proposed by [34].

To have a clear evaluation of rehabilitation PLLEs which considers the evaluation perspectives described above, we identify a series of performance evaluation criteria as follows:

• Gait rehabilitation (GAR)
• Balance maintenance (BAM)
• Decision making (DEM)
• Locomotion modes (LOM)
• Safety, ergonomy and clinical use (SEC)

Each of these criteria incorporates one or more of the mentioned functional and interaction evaluation perspectives as shown in Table 1. Note that these criteria are not mutually independent and they may overlap in some aspects, but each of them potentially considers important functionalities that PLLEs can deliver. They have been selected so that their overall contribution covers a wide and representative range of expected outcomes of both therapeutic and assistance applications. Later in this section, we will discuss in detail the importance of these criteria and potential factors that can influence them.

Table 1. The one-to-some mappings between the evaluation perspectives presented in [34], and our performance evaluation criteria for PLLEs.

Table 1. The one-to-some mappings between the evaluation perspectives presented in [34], and our performance evaluation criteria for PLLEs.

Perspective, Criteria	GAR	BAM	DEM	LOM	SEC
Stability				✓	✓
Efficiency					✓
Kinematics	✓	✓		✓
Dynamics	✓	✓		✓	✓
Physical interaction	✓	✓			✓
Cognitive interaction	✓		✓
Safety		✓			✓

Table 1, describes the relationship and mappings between our described criteria and the ones defined in [34]. For instance, GAR criteria involves kinematics, dynamics, cognitive and physical interaction aspects. This is because, different parameters, such as gait pattern, the quality of interaction between patient and the PLLE, and cognitive encouragement of patient have direct and indirect influence on improving GAR. Later in this section, we elaborate all the parameters that have heavy impacts on these mappings for all the mentioned criteria.

3.3. Selected Performance Criteria

In this part, we describe our selected performance criteria in details. We also discuss the importance of them technically. Various parameters that can leave substantial impacts for improvement of these performance criteria are considered and, we discuss how these parameters can be implemented employing PLLEs.

3.3.1. Gait Rehabilitation (GAR)

Gait rehabilitation or recovery is the primary objective of neurorehabilitation training [44] as neurological disorders leave individuals with gait abnormalities such as reduced walking speed, uncoordinated inter-limb walking pattern, asymmetric step length, muscle weakness, compromised balance and force reduction [45]. However, regaining all described skills and functionalities is typically possible and at the same time complete recovery requires long time and effort. Therefore, success in robot-assisted rehabilitation is defined by achieving the primary outcome, that is, regaining the ability to walk independently, and measuring secondary outcomes such as walking speed and walking capacity (metres walked in a certain time) [15,46].

GAR training should aim at overcoming not only primary complications of the incident but also to prevent deconditioning of patients due to effects of secondary complications. Early training plays a crucial role, specifically for stroke survivors, as the patients in the first six months after stroke have the maximum likelihood of recovery [48,49,50].

Generally, GAR is a process which involves motor learning that can be promoted through both recovery and compensation mechanisms. Recovery is referred as employment of undamaged regions of the brain to activate the same muscles used before the injury, while compensation, in contrast, is the recruitment of alternative muscles to accomplish the same task [51]. There are several factors that have proven to influence the gait rehabilitation program effectiveness. Intensity of activity is a fundamental factor for the improvement of the performances in motor learning [52]. Although intensive activity in very simple forms, i.e., repeating the same activities over and over again, might be considered the most effective approach, it can be misleading in terms of providing temporary improvements [53], therefore it can not be considered the sole factor contributing to optimal gait rehabilitation. Variability of activities, i.e., performing various activities in a random fashion, shifting the order of activities and changing the frequency of the rest times among them, leads to achieve a more effective performance in motor learning rather than performing simple repetitive activities [54,55]. This can be associated to the fact that, in random activities, each action is considered a problem to be solved and it will lead to skill learning, whereas simple activities promote the memorizing of motions and forces in time sequences and replicating them later [51], Furthermore, therapy limited to simple tasks and targeting specific muscles may improve muscle motion and strength, however often it fails to translate in motor learning and function improvement [56,57,58]. Another important factor in recovery and motor learning is active physical and cognitive engagement of patients in training [35]. There are a number of ways to actively engage the patients during training. Adaptive assistance, for instance, cooperatively engages the patient and introduces gait variations that can lead to retraining the neural network in the spinal cord [59]. Cognitive challenges also can lead to actively engage patients to simultaneously improve motor and cognitive deficits [60]. Another approach to challenge patients while preventing cognitive stress and frustration is to adaptively change the difficulty of the training according to patients capabilities [61], as it has been shown that matching the complexity of tasks to the skill level of the patients leads to improvement of learning efficacy [62].

3.3.2. Balance Maintenance (BAM)

Balance dysfunction in patients with CNS lesion typically results in muscle weakness and uncoordinated gait patterns. Maintaining good levels of balance and stability over the rehabilitation session is one important limitation in current PLLEs and patients often struggle to keep their balance after standing up despite using crutches or while receiving physical support from a therapist. Balance mechanism revolves around the interaction of ground reaction forces (GRFs) applied to human body and the kinematic configuration of body segments. The sum of GRF vectors acting on various parts of human body pass through the center of pressure(CoP). Similarly, the configuration of body segments with different masses defines a point of application called center of mass (CoM). Euclidean distance between CoP and CoM is the determinant factor for balance and stability, thus balance assistance deals with both kinematics and kinetics of PLLE and the wearer as a whole. As shown in Figure 3(left), whenever CoP resultant of GRF passes through CoM, the net rate of angular momentum becomes zero and stability is guaranteed, while, in Figure 3(right), the notable euclidean distance between GRF vector passing from CoP to CoM creates a non-zero rate of angular momentum causing a fall situation. BAM can be defined as a sustainable locomotion pattern without risks of falling or injury. Balance allows for safe return to statically stable configuration [63] and maintain the upright posture of a human wearing a PLLE.

Figure 3. (left) vector of GRF(summation of ground normal reaction and friction forces) passes through CoM and rate of angular momentum is zero and balance is maintained, (right) GRF vector is creating a clock-wise rate of angular momentum and causing an excessive forward lean and enhancing fall danger.

Figure 3. (left) vector of GRF(summation of ground normal reaction and friction forces) passes through CoM and rate of angular momentum is zero and balance is maintained, (right) GRF vector is creating a clock-wise rate of angular momentum and causing an excessive forward lean and enhancing fall danger.

Humans implicitly adopt ankle strategy, or hip strategy, or foot placement, or a modular combination of them [64], to reach a plateau state of balance while walking or standing. Ankle strategy regulates CoP positioning by means of ankle motions, and hip strategy by attempts to adjust angular momentum of body segments by means of hip flexion and extensions in order to reach a static balance. Ankle and hip strategies are more prevalent for stance phase of locomotion, while foot placement adjustment is predominantly employed for dynamic balance while walking [65]. In static stability, projection of CoM in the transverse plane is restricted to remain inside the support polygon, that is, the contact area of the stance foot with the ground in case of single support and, the polygon area that bounds both feet in transverse plane in case of double support. Note that in case of using cane or crutches, the support polygon is formed by the bound area of feet and cane/crutches. Instead, in dynamic stability, CoM is able to freely move and exit support polygon and provide faster gaits [66].

As dynamical balance of locomotion heavily relies on step length in the sagittal plane, step width in frontal plane also plays a crucial role in maintaining balance [67]. Hip flexion and extension are required for balancing in the sagittal plane and hip abduction and adduction guarantee stability in the frontal plane. Figure 4 depicts human body planes in anatomical positions. Humans don’t have direct control over GRF and CoP adjustment, and modulate it through dynamic coupling [68], thus, it seems the optimal solution to maintain balance in PLLEs is to implement hip strategies in stance phase and adjust foot placements in swing phase.

Apart from the effective role of lower limb segments in BAM, upper limb segments such as trunk and arms are also important in both static and dynamic balance maintenance and recovery [70]. Humans exploit arm and trunk motions to adjust to or cancel angular momentum resultant from GRF in both sagittal and frontal planes to keep a stable locomotion or to recover stability in presence of perturbations. Note that, in PLLEs, trunk and arms are not actuated and in many cases the trunk is spatially restricted and arms do not follow physiological motions to adjust overall angular momentum of body as crutches or walker are employed to guarantee stability. Nevertheless, a feedback from upper limb configurations and motions can be helpful with stability strategies.

Figure 4. The reference planes of the human body in the standard anatomical position [69].

Figure 4. The reference planes of the human body in the standard anatomical position [69].

BAM, balance recovery and fall prevention are not only meant to guarantee safety; in theory, appropriate designs of PLLEs and/or control strategies would free patients from dependency on the support of crutches or walker, since walking by aid of crutches or walker, induces asymmetric and discrete walking patterns and uncoordinated lower and upper limb motions. This may lead to slow or inefficient rehabilitation programs as training follow non-physiological patterns. Moreover, recovered patients may adapt to non-physiological lower and upper limb synergies with added asymmetries leading to secondary complications as discussed in Section 3.3.1.

As discussed above, balance maintenance is a complex issue and both PLLE structure and control strategies play critical roles in determining it. However, we need to consider a number of factors once aiming to include balance and fall recovery solutions into a PLLE. Firstly, the design of PLLEs should follow human anatomy and be able to mimic human body and motions as precisely as possible to avoid introducing abnormalities. Secondly, balance maintenance solutions should not override normal human motions and do not act as opposed forces to muscles unless in severe balance challenges [71]. Third, following the definition of balance, as stated previously, balance solution should be actively monitoring locomotion to predict falling hazard, and must be triggered only if danger of fall is detected.

3.3.3. Locomotion Modes (LOM)

Activities of daily living (ADL) are not limited to walking on level grounds and they incorporate numerous modes from ascending and descending stairs and slopes, to walking on unstructured terrain. One of the main drawbacks of current PLLEs that prevents them from being widely used both for assistive and therapeutic applications in daily activities is their inefficiency in autonomous recognition of LOMs, immediate adaptation and smooth switching among them. Current solutions employ manual switching of locomotion modes triggered by the user or an assistant. Although manual switching guarantees reliability and robustness, it poses discontinuity in locomotion and can implement only few locomotion switching schemes with prior knowledge of locomotion parameters. Moreover, manual switching to some degrees decreases the independency of the user in mobility. Switching LOM entails a number of considerations as kinematic and kinetic aspects may change drastically and needs prompt actions to be taken by the PLLE.

It’s noteworthy to indicate that errors in mode recognition would lead to incorrect mode changes and result in actions opposed to volitional control of the user increasing the risk of harm [72].

The ability of switching from one locomotion mode to another, enhances balance maintenance and recovery, and ensures safety. Moreover, smooth transition among modes promotes natural and continuous gait patterns in patients.

3.3.4. Decision Making (DEM)

DEM is an important criteria for effectiveness of the PLLEs. By DEM, we refer to the capability of the PLLEs in managing between the decisions and intentions coming from the patient and the actuation control of the PLLE. In other words, DEM evaluates how well the priority between human decisions and the exoskeleton actuation, and ultimately, the safety in double agent paradigm context is addressed. We emphasize that the decisions are not related only to low-level gait pattern parameters, they also include high-level LOM functionalities.

PLLEs must ensure an effective physical and engaging cognitive interaction with the patients, as it is essential for both assistive and therapeutic applications. Solutions which focus on realising effective DEM, promote patient engagement. They also cognitively encourage and motivate patients to take the lead of their activities and avoid them from slacking. Moreover, including decisions of patients in locomotion, considers them as active human counterpart subjects rather than passive objects.

PLLEs can adapt to wearer decisions both proactively and reactively. Proactive adaptation refers to the recruitment of signals acquired from CNS or other musculoskeletal segments and identification of intents prior to muscle motions, and accordingly actuate the PLLE to fulfill human intentions. Whereas, reactive adaptation is functionally triggered measuring signals such as force and position that arise from physical interaction between human and PLLE. In contrast to reactive adaptation that is formed on the basis of physical interaction, proactive adaptation is resultant from cognitive interactions.

The inclusion of decisions and intentions of patients can be obtained within volitional based rehabilitation strategies such as assistance-as-needed [73,74], human-in-the-loop [75,76] and user-centered concepts [77], where the level of assistance is regulated with regard to detected intentions and evolution of the therapy [78].

The aforementioned assistive strategies require precise intent recognition and analysis that takes into account the specific impairment of the patient, since different pathologies require specific interventions in assisting them.

Biological signals received from injured limbs are different from those of healthy limbs and in some cases are absent. Hence, in many cases they may provide misleading information of patient intents. Therefore, a PLLE might need to implement alternative approaches exploiting unaffected limbs [79,80,81,82], walking supports like cane, crutches or walker, since they take part in the coordination of the gait and their movements represent upper limb motions and contribute to the whole synergy of locomotion [83].

The main factors that would help patients to gain control over the locomotion activities are gait initiation, gait termination, walking at a preferred speed, ability to switch between gait activities such as sitting down, standing up, ascending and descending stairs and slopes. To this aim, recognition of human decisions in locomotion can be divided into two types: gait phase recognition and gait activity recognition [84]. Human gait is formed by two main phases of stance and swing as shown in Figure 5. Generally, gait can be divided into seven discrete and ordered inter-state transitions, namely: loading response, mid stance, terminal stance, pre swing, in stance phase and, initial swing, mid swing and terminal swing in swing phase. Since recognition of numerous state transitions is very complex and prone to error, in some cases, a more simple division, where only single support, double support and swing phases are detected, might lead to satisfactory results.

Figure 5. Spectrum of human locomotion phases characterised by bio mechanical events. Overall gait is divided into stance and swing phases. Each phase comprises some sub phase segments representing bio mechanical events [47].

Figure 5. Spectrum of human locomotion phases characterised by bio mechanical events. Overall gait is divided into stance and swing phases. Each phase comprises some sub phase segments representing bio mechanical events [47].

Among all the gait-related DEM activities, gait initiation and termination are of higher importance, as they potentially promote cognitive capability and provide a positive feeling for patients on being independent in controlling their locomotion. Gait initiation is transition from the upright standing position to a toe-off event and is characterized by anticipatory postural adjustments where CoP is shifted [85]. Whereas, gait termination is transition from steady-state gait to the upright standing; the person in the final step increases braking forces of the lead foot and reduces push-off of trailing foot [86], preparing for a complete stop. Recognition of gait initiation and termination due to their complex biomechanical transition and the independent volition of the patient that can occur at any time, is a challenging task. Whereas, detecting gate phases between initiation and termination, due to cyclic nature of the gait remains less challenging.

Similarly, gait activity recognition can be defined as a smooth transition from one locomotion mode to another where the termination of the former and the initiation of the later are neglected to provide continuous and monotonic gait motion. Gait activity change detection for analogous reasons of gait initiation and termination detection, is a challenging task and demands accurate detection and processing.

3.3.5. Safety, Ergonomy and Clinical Use (SEC)

Safety is a crucial aspect in the evaluation of PLLEs. Despite their indubious advantage on supporting patients, PLLEs can potentially cause severe adverse events. Safety requirements should be considered from the early development phase of design and throughout its entire lifetime. Safety can be defined as an abstract representation of the ability of PLLEs in reducing a specific risk or dealing with a specific hazard [87]. Adverse effects often vary from discomfort, cardiovascular events, pain and injuries to bone fractures [88,89]. Adverse events do not only compromise the health of patients, but they also prevent patients from receiving continuous training and assistance from PLLEs. Reports in [15] indicate that a notable number of dropouts in clinical trials for gait training were adverse events related to pain and skin breakout [89]. The main sources of concern regarding safety arise from the PLLE itself, the prolonged interaction of vulnerable subjects with PLLEs and their interaction as a whole with the environment. Those risks might be relevant even in controlled environments like clinics. PLLEs are required to be compliant to the human body to avoid injuries and, at the same time, they should be stiff enough to convey forces to the human musculoskeletal system through contact points such as straps and foot plates to achieve the desired effects [45,87].

Although all PLLEs are required to acquire safety and conformity approvals from representative authorities before being delivered to the market, several adverse events and safety issues have been reported in [90]. This study, in particular, reported adverse events and identified risks associated with a series of PLLEs which had received necessary approvals as commercial medical devices. Non ergonomic design and device malfunctioning seem to be the major reasons of the incidents.

Fully ergonomic design of PLLEs is in theory difficult and in practice impossible, since PLLEs are designed to provide the minimal functional representation of a human lower body. A complete and human like design of PLLEs with existing technologies adds high complexity in design, compromises technical performance and deviates PLLEs from their final objectives. However, monitoring and controlling ergonomy related issues such as misalignment of PLLEs to lower body and non-physiological joint ranges can substantially reduce the associated risks.

Misalignment of PLLEs with human lower body is related to non concentric alignment of PLLE actuators with human joint axes that can be the result of either wrong dimension of PLLE segments, or poor adherence of straps or cuffs with the patient during locomotion. This misalignment negatively impacts the physical interaction with the patient and introduces hazards to the human body [91]. To alleviate this concern, some works proposed polycentric knee joints [92] for improving kinematic compatibility with human knee, especially at high degree of knee flexion. Another likely hazard is exceeding human joint limits such as moving the limb beyond anatomical joint range of motion (RoM), moving at high velocity and acceleration and introducing jerky motions, especially at the beginning of the gait. These limits are subjective and specific considerations need to be taken into account for each patient at different recovery levels.

Another important factor for rehabilitation in PLLEs is their applicability in clinical use. This can be achieved by considering several factors while designing PLLEs. Clinicians prefer to operate with easy-to-handle devices, where, they can easily take control of the device and configure it for their clinical aims. Patients with different physical conditions demand a modular PLLE, easily adjustable to their physical characteristics. They also differ in their neurological disorder, and this requires configurable levels of assistance and modes of use, that should be readily accessible for clinicians. Another possible approach for improving SEC is to include human-centred design methods from the initial phases of design and development of PLLEs. However, these kind of methods are usually complicated and entail ethical, methodological and financial challenges [93].

Durability of batteries and possibility for easy and fast battery recharging and replacement are also important to ensure clinical use. Lower weight of the PLLE also increases portability and overall performance, since it is easier for clinicians to guide patients with severe balance problems. Other design choices such as compact design of PLLEs, employing compact motors, avoiding exposed cables and proper placement of batteries and computer system [94], are also fundamental for clinical use.

4. Platform-Related Apparatus

4.1. Lower-Limb Exoskeletons

Rehabilitation PLLEs are designed and manufactured to partially or completely compensate for lower limbs loss of motor function. Those who partially compensate the patient strength, provide support only for one or few joints or a single limb. In contrast, complete ones cover both legs and provide at least support for locomotion in sagittal and/or frontal plane. Some examples of complete PLLEs are shown in Figure 6f–j. In this review paper we mainly focus on complete PLLEs.

Figure 6. Some examples of partial PLLEs: (a) Honda Walking Assist, with two active hip joints [95], (b) C-Brace orthotic device [96], (c) Hal single leg exoskeleton with actuated hip and knee joints [80], (d) AlterG Bionic Leg with actuated knee joint [97], (e) MAK, actuated knee [98]. Some examples of complete PLLEs: (f) Atalante of Wandercraft with (12 DoF) [95], (g) REX PLLE with ten actuated joints [99], (h) Ekso GT exoskeleton with four actuated joints [100], (i) TWIN PLLE with four actuated joints [101], (j) Indego PLLE with four actuated joints [102].

Figure 6. Some examples of partial PLLEs: (a) Honda Walking Assist, with two active hip joints [95], (b) C-Brace orthotic device [96], (c) Hal single leg exoskeleton with actuated hip and knee joints [80], (d) AlterG Bionic Leg with actuated knee joint [97], (e) MAK, actuated knee [98]. Some examples of complete PLLEs: (f) Atalante of Wandercraft with (12 DoF) [95], (g) REX PLLE with ten actuated joints [99], (h) Ekso GT exoskeleton with four actuated joints [100], (i) TWIN PLLE with four actuated joints [101], (j) Indego PLLE with four actuated joints [102].

To achieve safe and efficient physical interaction between a PLLE and the wearer, PLLEs are required to give assistance following the anthropomorphic structure of human body and to mimic biological lower limb functionalities. Each lower limb is composed of three main joints; one hip joint with three DoFs to provide flexion and extension, abduction and adduction, and internal and external rotations, respectively in sagittal, frontal and transverse anatomical planes. One single DoF joint actuates knee for both flexion and extension in sagittal plane. Similar to hip, one joint with three DoFs applies rotations of the ankle in all three planes. Therefore, to achieve a fully anthropomorphic kinematics, PLLEs should possess seven DoFs for each leg. Anthropomorphic design has a direct impact on BAM and SEC as humans maintain their static and dynamic balance by using all the joints and the corresponding muscles. We can hypothesize that anthropomorphic design can also affect the quality of GAR, since it enables the training of all the major and interconnected muscles. However, among developed PLLEs, a small number of them possess seven or more DoFs for each leg. Berkeley lower extremity exoskeleton (BLEEX) [103], was the first PLLE to aim for fully anthropomorphic design of PLLEs. Atalante and REX to date, are the only rehabilitation PLLEs that follow an anthropomorphic design with complete DoFs for each leg, although not all of their DoFs are actuated. The majority of rehabilitation PLLEs are underactuated, typically providing support only in sagittal plane with active hip and knee without ankle actuators. Obviously enough, technological barriers, excessive weight, cost and size, geometrical and design limitations, are the main factors preventing optimal anthropomorphic design of current PLLEs. A list of commercially available or common PLLEs used in research are shown in Figure 7a. As it is shown, all of the listed PLLEs support hip and knee flexion/extension (FE) actuation. Only few of them support ankle actuation and in the majority of designs, ankles are passive joints. Considerations about weight and complexities of the system, design limitations and small range of motion (RoM) of some biological DoFs are the main reasons to alternate active actuators with passive joints. This implies that research groups and industries over the years, have reached a consensus on commonalities of the design of PLLEs. However, the ankle joint, despite having low range of motion, has a predominant role in the locomotion and its active presence provides immediate changes to gait kinematics [104]. It is the first joint to meet GRFs applied to human foot and therefore its active presence becomes necessary in different phases of the gait. In toe-off phase, it can act as thrust force in pushing the human to move forward. In swing phase it applies enough force to damp the released energy of toe-off phase to guarantee a stable and level foot positioning and in heel-strike phase, it absorbs the energy resulting from the impact with the ground and prevents the disturbance to knee and hip joints [105]. Information of RoM of the joints of PLLEs from Figure 7a, demonstrate that there are considerable deviation of RoM of joints with those of human walking [19]. This incompatibility is mainly corrected at software level and by means of control strategies. However, exceeding normal RoM of human joints, especially for patients with stiff muscles and fragile joints, imposes a high risk of danger and has negative influence on SEC.

Another fundamental requirement for PLLEs design is their mechanical compliance. PLLEs, due to their tight physical interaction with the lower limbs, are required to mechanically conform to lower limbs by compliantly adapt to the configuration of biological legs to avoid risks and ensure SEC. Moreover, compliant legs are essential to achieve basic walking mechanics [106], since stiff legs are not able to reproduce human-like motions. In humans, actuation and compliance mechanisms are possible by means of an integrated complex of muscles and tendons. Compliance is the result of visco-elastic properties of muscles and series-elastic formation of tendons. Activation and contraction velocity of muscles also play a role in the stiffness of the joints [47]. However, to implement human-like actuation and compliance mechanisms, recent works have inclined toward using elastic elements embedded within actuators. Although elastic elements in robotic systems in recent years have become prevalent and are widely used even in prosthetic systems, they are rarely adopted in actuation of complete PLLEs as shown in Figure 7b.

Elastic elements can mainly be categorized into elements with fixed or variable stiffness, or in their arrangement form with the actuators, in series or in parallel. Each form of these arrangements has some advantages for PLLEs. Variable stiffness actuation (VSA) have been developed to replicate the ability of mammals in regulating joint stiffness. However, practical implementation of VSAs within PLLEs is difficult, given the typically cumbersome and complex nature of these devices. Alternatively, series elastic actuation (SEA) or parallel elastic actuation (PEA), with fixed stiffness, provide an acceptable level of compliance for actuation of PLLEs. A complete review on use of compliant actuation in lower limb exoskeletons is addressed in [20]. Obviously enough, compliant behaviour of the mechanical structure of PLLEs and the corresponding actuation system, as it promotes human-like motion and interaction with the environment, has substantial effects on BAM and SEC.

Figure 7. (a) RoM of actuators for various PLLEs. A-Active joint, P-Passive joint, N- No joint, NA-information is not available. (b) Ratio of PLLE maximum torque of motors over overall weight of the exoskeleton as an indicator for deliverable power, w.r.t DoF of each leg. Passive joints are considered to have 0.5 DoF. Blue circles depict stiff joints and red circles joints with SEA actuation. Human biological lower limb is considered to weigh 40 Kg and provide 150 Nm torque. We considered 50 Nm torque for PLLEs with unknown max torque value.

Figure 7. (a) RoM of actuators for various PLLEs. A-Active joint, P-Passive joint, N- No joint, NA-information is not available. (b) Ratio of PLLE maximum torque of motors over overall weight of the exoskeleton as an indicator for deliverable power, w.r.t DoF of each leg. Passive joints are considered to have 0.5 DoF. Blue circles depict stiff joints and red circles joints with SEA actuation. Human biological lower limb is considered to weigh 40 Kg and provide 150 Nm torque. We considered 50 Nm torque for PLLEs with unknown max torque value.

4.2. Control Algorithms and Strategies

Control strategies play a fundamental role in the mobilization of PLLE and the patient as a whole. To date, many different control strategies have been proposed in the field of PLLEs. In this section, we group these control strategies and evaluate their advantages w.r.t the criteria we defined before. We categorize control strategies based on their relevance in addressing rehabilitation aims and also in accomplishing ADL.

We differentiate them into following four categories: (I) assistive, (II) challenge-based, (III) volitional, and (IV) autonomous controllers. In Table 2, we listed a number of recent and promising works that represent interesting assistive, volitional and autonomous controllers. Please note that, we could not find recent works compliant with our topic-specific criteria according to Figure 1, for challenge-based controllers.

4.2.1. Assistive Controllers

Assistive controllers aim for partial or complete assistance of the patients, and are mainly in the following forms:

• Trajectory-based (position, velocity)
• Force/flow field
• Impedance and admittance
• Virtual constraints

Please note that the aforementioned assistive controllers are low-level controllers that may be adopted in other categories with an appropriate mid/high-level control framework. However, they are the common type of assistive controllers.

Figure 8. Assistive controllers: (a) linear model of the coupled 1-DOF human-exoskeleton system [107],(b) Hybrid zero dynamic-based controller supplemented with foot detection-based switching condition [108], (c) Performance of the proposed controller acting on paretic leg in forcing the trajectories back into the deadzone throughout gait [109], (d) AAN controller with the user torque contribution for different gait pathologies [109].

Figure 8. Assistive controllers: (a) linear model of the coupled 1-DOF human-exoskeleton system [107],(b) Hybrid zero dynamic-based controller supplemented with foot detection-based switching condition [108], (c) Performance of the proposed controller acting on paretic leg in forcing the trajectories back into the deadzone throughout gait [109], (d) AAN controller with the user torque contribution for different gait pathologies [109].

Trajectory-based controllers are widely adopted especially in commercial devices, to ensure a stable walking pattern [110,111,112,113,114,115] and mainly promote SEC criteria. In these controllers a pre-defined trajectory or pattern is regulated to fit patient-specific walking pattern requirements. Trajectories are usually based on biological walking patterns of human in either task or joint space. A low level controller, usually PID controller, is responsible for tracking the motor position/velocity trajectory with respect to time or phase of the gait. These controllers provide full assistance to the patients for all walking modalities from sit-to-stand to walking. Trajectories are pre-defined for the whole walking time and patients are able only in some cases to start or stop a step through adjusting trunk tilts or sending manual commands.

The force/flow field control modality partially assists the patient in tracking the predefined trajectory in the context of assistance-as-needed [116,117,118,119,120,121]. These controllers allow patients to contribute in walking by inserting forces at any phase. Therefore, in this control modality, patients are allowed to deviate from the main trajectory, that can be pre-defined or designed online, to an admissible extent that is bound within a limited zone.

Table 2. Description of the methods that some selected works implemented assistive, volitional, or autonomous control strategies on PLLEs

Table 2. Description of the methods that some selected works implemented assistive, volitional, or autonomous control strategies on PLLEs

Strategy	Ref.	Method	Description
Assistive	[122]	Impedance	Implemented an adaptive impedance controller, by introducing an adaptive control law that considers interaction torque of human-exoskeleton. A radial basis function neural network was adopted to approximate the uncertain dynamic.
	[115]	Tracking	Developed a parameter identification system for both human and exoskeleton and adopted linear quadratic programming method for trajectory tracking.
	[119]	AAN	A velocity field in task space was constructed to determine motion velocity limits at any configuration, and implemented a force field controller in joint space, embedding a tunnel for position tracking and control.
	[121]	Torque field	Proposed a torque field controller to guarantee coordination between limb joints and also allows the user to vary step length and time.
	[109]	Virtual constraints	Proposed a virtual constraint model to generate trajectories autonomously and to ensure stability of the user. This work adopted also AAN method to allow the user to deviate from desired trajectories within a deadzone that is defined according to velocity of CoM.
Volitional	[123]	BMI	Implemented a control framework to feed human detected intentions to the PLLE considering safety issues. This work successfully detects six different intentions.
	[124]	HMI	This work combined EEG and EMG signals to detect three movement classes of human intentions.
Autonomous	[125]	Synergy	An adaptive synergy-based control is developed to realize impedance adjustment on affected leg following the kinesiological information of healthy leg
	[126]	Balancing	A safety index based on extrapolated CoM of human-exoskeleton model together with dynamic movement primitives, is presented to promote BAM and control of exoskeleton.
	[127]	CoG transfer	It presents a control scheme capable of minimizing the reliance on pilot for transferring centre of gravity. It also provides an online trajectory planning considering CoG transfer and safety.

Virtual potential fields, corresponding to deviations from the main trajectory are created to guide the limbs toward the reference pattern. Force potential field [128,129], acts as a virtual wall for the motion of the legs, allowing the legs to move within a bounded area and not exceed from that area. It is mainly formed of tangential, normal and damping terms. Tangential and normal forces are regulated w.r.t tangential and normal distance between the current and desired trajectories in the configuration space, while the damping force limits the velocity of the leg. Normal forces push the leg toward the desired trajectory, whereas the tangential forces help the leg to track the desired trajectory. Flow potential fields instead, create a viscous fluid field and the force control is calculated relatively to the euclidean distance of the actual velocity from the desired velocity and limit the velocity of motion of the legs corresponding to gait phase. Force/flow field controllers promote physical engagement of the patients in gait and also provide a sort of DEM ability for patients. However, superiority of this approach over conventional methods (assistive controllers) for GAR is not confirmed yet [73].

Controllers based on impedance/admittance are highly beneficial for PLLEs [122,130,131,132], since the soft physical contact of PLLEs with the user and also the hard GRF acting on user soles, specifically while walking on uneven terrains or crossing obstacles [107,130], demands impedance/admittance controllers to track well the predefined trajectory. The main objective of the impedance controller is to minimise the influence of external forces on the PLLE and ensure a motion trajectory tracking. The impedance controller simulates the interaction forces as mechanical impedance. The controller, following the impedance model generates forces and applies them to the perturbed PLLE to both guarantee a proper position control and also dissipate the effect of external forces [133]. In admittance control, on the other hand, the controller has the role of mechanical admittance and the mechanical admittance and the PLLE is considered as mechanical impedance. Thus, the admittance controller by following the impedance model and observing external forces, generates desired position motions and sends them to the PLLE for position control. Impedance-based controllers are essential for SEC, as they consider physical interactions between user and environment. They also have shown positive effect on GAR on SCI patients [134].

Virtual constraints control methods allow to virtually add limits to some parameters or shape the output trajectories to meet specific constraints [109,135,136,137,138]. This method provides inputs considering the actual state of the PLLE and also satisfying predefined virtual constraints. Therefore, in contrast to trajectory tracking, these methods can deliver online and stable trajectory solutions that take inter-limb coordination problem into consideration. Defined constraints are usually kinematic functions such as desired forward velocity or dynamic functions such as desired angular momentum to follow. These constraints applied with the physical constraints, that is the dynamic model of PLLE together with the wearer, yield dynamic and stable solutions in presence of uncertainties. Moreover, the controller is not time-dependent and it mainly depends on the states of the system.

4.2.2. Challenge-Based Controllers

Challenge-based controllers aim to increase the physical engagement of the patients rather than partially or fully assist them. They can increase the difficulty of the training task, or oppose to the movements of the limbs. This can be done through various strategies such as resistive training, error augmentation, inducing constraints and applying perturbations [141]. Resistive training adds resistance to the movement of the paretic limb of the patient as a mean to increase muscle strength [142,143,144], however this method doesn’t lead to improvements of functional skills.

The error augmentation method was proposed to promote implicit learning and motor adaptation capacity, that is the capability of modifying motor commands to archive desired motions. Error augmentation is accomplished by applying forces to the limbs in order to push them away from approaching to the desired trajectory. Usually a force/viscous potential field is adopted to generate the disruptive force opposite to assistive force/viscose field forces. This method has proved to be efficient in motor adaptation [145,146,147,148,149,150]. Another approach to challenge and engage the patients is to induce constraints on intact limbs so that the patient relies more on affected limb. Induced constraints promote and enforces symmetric use of limbs in walking [151,152].

It’s noteworthy to mention that, challenge-based controllers have rarely been studied and adopted despite the diffusion of PLLEs in robot-assisted therapy in recent years. However, these methods in traditional therapies have been widely used and have proved be efficient in achieving positive GAR outcomes.

4.2.3. Volitional Controllers

A possible alternative to keep patients cognitively engaged to promote GAR, and allow them to participate in DEM for accomplishing tasks, is to introduce a higher level of controllers that include the intention of patients in controlling PLLEs. Volitional controller frameworks embed at least two hierarchical levels of control: one to identify the intentions of the patient and the other to integrate and implement the intentions that can be related to any of assistive or challenge-based methods we introduced previously. Intentions can range from walking pattern adjustments such as step length and velocity, to switching between LOMs. Intentions are mainly detected using sensory data or patient manual inputs. These sensors can be categorised into physiological sensors such as electromyographic (EMG) and electroencephalographic (EEG) that detect intentions prior to patient movements, and proprioceptive/exteroceptive sensors such as IMU, GRF, and camera sensors that provide intention information posterior to movements. Various algorithms have been proposed to exploit the acquired data and transform them to meaningful actions. Various control schemes also have been developed to integrate human intentions to the low level controllers to ensure stable and safe locomotion.

Methods based on EEG signals [123,124,153], attempt to recognise human intentions in the context of brain machine interaction (BMI), adopting different classifiers such as linear discriminant analysis (LDA) and Gaussian mixture model (GMM). However the problem with these methods is the reliability of signal acquisition and precision of the classifiers. EMG-based intent recognition also has its own problems, since reliability of the signals and robustness of the classification methods have excessive reliance on placement location of EMG electrodes and user-specific adjustments. Therefore, EMG-based models are frequently employed in conjunction with other sensors such as GRF and IMU [154,155,156,157,158].

Human recognised intentions at their best efficiency are not enough to independently drive PLLEs. Therefore, results of the recognition are usually limited to follow specific state transitions of control frameworks such as finite state machines (FSM).

4.2.4. Autonomous Controllers

Autonomous controllers are more adaptive and consider environment, kinematics and dynamics of interactions and human posture effects for gait pattern design and control, compared to what discussed previously, [125,159,160,161,162,163,164,165,166,167,168,169]. These controllers have more complex schemes and are composed of different layers including perception, high-level for decision making, and low level control for gait generation and actuation. These controllers aim at assisting patients, either fully autonomously or by means of human-in-loop strategies, with ADL rather than rehabilitation goals. These controllers aim at integrating different control modalities to promote flexibility in HMI and ensure safety and SEC.

The work presented in [139] provides a shared level of autonomy and control with the patient. In that work, a vision perception system is actively monitoring the environment for possible variations in LOM and shares the adaptation strategies with the patient for their further decisions as shown Figure 9a. It also provides adaptive step length adjustments for the user based on environment conditions.

A recent work [126], introduced a safety index to generate adaptive solutions for gait pattern considering human balance and also exploiting reference trajectories. They used a framework, as shown in Figure 9b, to observe the integrated model of PLLE together with the user and by considering a safety and balance index, generate safe targets. They proposed a novel theory to trace the overall CoM of user and exoskeleton online and evaluate the safety and take further actions. A different approach for online gait pattern design was implemented in [127]. That work addressed the limitations of CoM transfer from one foot to another to start a step for paraplegic patients. The authors of that work introduced a control scheme as shown in Figure 9c, where the motion of patient and exoskeleton are separated and using a finite state machine, CoM transfer is accomplished in specific states of a step to relax the patient from relying on CoM transfer for the initiation of a step.

Different approaches such as vision assisted gait pattern generation [140], within more sophisticated control schemes, Figure 9d, have been proposed and implemented. Although many of these works proved to provide stable solutions for assisting patients, still their safety concerns and their reliance on extra instruments, uncertain reliability and robustness, and more importantly, the cognitive load and stress of the patients, make them less popular.

Figure 9. (a) Overview of the lower-limb exoskeleton shared control system [139], (b) The architecture of the balance control strategy presented in [126], (c) Control structure of the human-exoskeleton system [127], (d) Autonomous gait pattern planning framework presented in [140].

Figure 9. (a) Overview of the lower-limb exoskeleton shared control system [139], (b) The architecture of the balance control strategy presented in [126], (c) Control structure of the human-exoskeleton system [127], (d) Autonomous gait pattern planning framework presented in [140].

5. Discussion

6. Conclusion

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