Characterization and Evaluation of Human-Exoskeleton Interaction Dynamics: A Review

Citation: Massardi, S.; RodriguezCianca, D.; Pinto-Fernandez, D.; Moreno, J.C.; Lancini, M.; Torricelli, D. Characterization and Evaluation of Human–Exoskeleton Interaction Dynamics: A Review. Sensors 2022, 22, 3993. https://doi.org/10.3390/ s22113993 Academic Editor: Carlo Ricciardi Received: 29 April 2022 Accepted: 23 May 2022 Published: 25 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Review Characterization and Evaluation of Human–Exoskeleton Interaction Dynamics: A Review Stefano Massardi 1,2 , David Rodriguez-Cianca 1, David Pinto-Fernandez 1,3 , Juan C. Moreno 1, Matteo Lancini 4and Diego Torricelli 1,* 1 Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), 28006 Madrid, Spain; [email protected] (S.M.); david.r[email protected] (D.R.-C.); [email protected] (D.P.-F.); [email protected] (J.C.M.) 2Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25100 Brescia, Italy 3Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain 4Department of Medical and Surgical Specialties, Radiological Sciences and Public Health (DSMC), University of Brescia, 25100 Brescia, Italy; [email protected] *Correspondence: [email protected] Abstract: Exoskeletons and exosuits have witnessed unprecedented growth in recent years, especially in the medical and industrial sectors. In order to be successfully integrated into the current society, these devices must comply with several commercialization rules and safety standards. Due to their intrinsic coupling with human limbs, one of the main challenges is to test and prove the quality of physical interaction with humans. However, the study of physical human–exoskeleton interactions (pHEI) has been poorly addressed in the literature. Understanding and identifying the technological ways to assess pHEI is necessary for the future acceptance and large-scale use of these devices. The harmonization of these evaluation processes represents a key factor in building a still missing accepted framework to inform human–device contact safety. In this review, we identify, analyze, and discuss the metrics, testing procedures, and measurement devices used to assess pHEI in the last ten years. Furthermore, we discuss the role of pHEI in safety contact evaluation. We found a very heterogeneous panorama in terms of sensors and testing methods, which are still far from considering realistic conditions and use-cases. We identified the main gaps and drawbacks of current approaches, pointing towards a number of promising research directions. This review aspires to help the wearable robotics community find agreements on interaction quality and safety assessment testing procedures. Keywords: exoskeletons; wearable robots; physical human–exoskeleton interaction; safety; forces; pressures 1. Introduction Exoskeletons are starting to be extensively used in many applications, spanning from military to industrial use, personal care, and medical applications. This is reflected by an increasing trend in the number of devices present on the market [ 1 ]. Their range of applicability is expanding together with the evolution of automatized industrial processes—which still require the involvement of human workers [ 2 , 3 ]—and the aging of the population. Aging is associated with increasing mobility impairments, making the demand for rehabilitation and assistive devices grow every year [ 4 ]. The use of exoskeletons in rehabilitation medicines represents one of the most grounded scenarios in their future development. Applications such as supporting mobility of spinal cord injured (SCI) persons and rehabilitation of major trauma patients remains the primary focus of exoskeleton research [4]. Due to the increasingly aging population [ 5 ], new scenarios are starting to receive attention, not only in the field of after-treatment therapies but also to help elderly people to remain independent by providing daily life assistance [ 6 ]. In several of these activities, exoskeletons help users to perform tasks by providing assistance and augmentation of individual capabilities through increasing the range of motion of individual joints [7]. Sensors 2022,22, 3993. https://doi.org/10.3390/s22113993 https://www.mdpi.com/journal/sensors Sensors 2022,22, 3993 2 of 19 Safety and user acceptability will be the underlying evaluation criteria for the mechanical design, actuation, and control architectures of future exoskeleton developments [ 5 ]. One fundamental aspect that differentiates exoskeletons from other robotic technologies is the intrinsic and close physical interaction with humans, defined as the generation and exchange of a net flux of power between both actors [ 5 ]. ISO 13482:2014 [ 6 ] states how physical interaction (e.g., contact forces) between robot and human will be designed to be as low as reasonably practicable. However, current international standards do not provide realistic protocols to assess contact safety, and in the case of exoskeletons, regulatory gaps are yet to be addressed [7,8]. In exoskeletons, force or torque are usually transferred through attachment devices (i.e., connection cuffs or orthoses) [ 9 ] producing interaction forces that are key in the generation of shear stresses, interface movements, and misalignments [ 10 ]. This physical interaction between the human user and the wearable robot should be carefully monitored and controlled since an unexpected behavior by one of the actors during the task might have an impact on safety and the system. For this reason, a crucial challenge in exoskeleton design is to minimize the risks introduced by the dynamic interaction between the human and the exoskeleton’s physical interfaces. A truly ergonomic physical interface should be customized to an individual’s own anthropometrics and needs [ 11 ]. However, this concept is unlikely to be applicable to most current devices as they follow a design adaptation concept to fit a large spectrum of possible users. However, how the predefined interface can influence physical human–exoskeleton interaction (pHEI) remains to be defined in a more generalized way. There is still considerable room for the research on pHEI to grow compared with other more accepted fields. Current exoskeleton evaluation processes often include tests with humans for the evaluation of physiological, kinematic, and kinetic effects of human–device interaction [ 12 ], but metrics and protocols able to characterize pHEI are still not clarified, preventing standardized pHEI evaluations. Additionally, different metrics can be difficult to apply to a broad spectrum of devices, and the selection of an accepted relevant set of metrics is far from being accomplished. Testbed platforms for proving exoskeleton compliance with contact safety requirements are still limited and developed for specific device solutions [ 13 ], while traditional comfort evaluations are based on subjective pain rating scales [ 14 – 16 ]. Qualitative feedback can be improved by user-centered designs and individual needs assessments [ 17 ] but do not normally include quantitative measurements able to produce a well-accepted body of scientific knowledge in the field of pHEI. In this growing and partially unexplored field, the necessity to concentrate the future efforts in a common direction is a pressing requirement for the wearable robot community. The aim of this work is to systematically review recent studies including pHEI measurements such as contact forces, torques, and pressures in order to summarize and analyze the current knowledge, techniques, and metrics used in pHEI measurement. This work answers to the need for building a comprehensive revision of pHEI-related works in an effort to create a first step in the acceptance of a shared set of metrics and methods in pHEI measurement and their safety assessment. 2. Materials and Methods We define pHEI measurement as any extraction of information related to forces (including torques and pressures) exchanged between a human and an exoskeleton during the execution of a task. Considering the recent and rapid growth of exoskeletons in the last year, we decided not to include studies older than 10 years as they are likely considering depreciated and early-stage devices that are now better equipped and developed. Similar growth is also seen in the world of sensor technologies; for these reasons, various searches were conducted on the Scopus scientific database between 1 January 2010 and 31 December 2021. We looked for articles that included references to human–exoskeleton interaction databases using the AND/OR/NOT Boolean operators with different combinations of terms from 3 sets of keywords: Sensors 2022,22, 3993 3 of 19 •exoskeleton*, physical assist*, wearable rob*; •physical human-rob*, human-robot inter*, phri, pressure, safety; •measure*, asses*, benchmar*, eval*. The searches provided a list of 785 publications. After removing duplicated publications and a preliminary review of titles and abstracts, 121 publications were selected for a full text review. A total of 54 publications have been included in this work. The review’s flow diagram is depicted in Figure 1. Sensors 2022, 22, x FOR PEER REVIEW 3 of 21 Similar growth is also seen in the world of sensor technologies; for these reasons, various searches were conducted on the Scopus scientific database between 1 January 2010 and 31 December 2021. We looked for articles that included references to human–exoskeleton interaction databases using the AND/OR/NOT Boolean operators with different combinations of terms from 3 sets of keywords: • exoskeleton*, physical assist*, wearable rob*; • physical human-rob*, human-robot inter*, phri, pressure, safety; • measure*, asses*, benchmar*, eval*. The searches provided a list of 785 publications. After removing duplicated publications and a preliminary review of titles and abstracts, 121 publications were selected for a full text review. A total of 54 publications have been included in this work. The review’s flow diagram is depicted in Figure 1. Figure 1. Prisma diagram of the conducted review. Kinematic or physiologically based metrics such as relative motions, discomfort, and fatigue do not provide direct information of pHEI but rather its consequences on the human body. For this reason, additional kinematic and physiological metrics are included in the results only when supported by pHEI measurements. Other measurements such as Ground reaction forces (GRFs) or Muscle activation (EMG) fell outside the research. Figure 1. Prisma diagram of the conducted review. Kinematic or physiologically based metrics such as relative motions, discomfort, and fatigue do not provide direct information of pHEI but rather its consequences on the human body. For this reason, additional kinematic and physiological metrics are included in the results only when supported by pHEI measurements. Other measurements such as Ground reaction forces (GRFs) or Muscle activation (EMG) fell outside the research. We decided to include both upper limb and lower limb exoskeletons since the proposed solutions can often be shared between the two. For the same reason, both powered exoskeleton, passive exoskeletons, and exosuits have been considered since interaction issues are common among wearable devices. Wrist and hand exoskeletons were excluded from this review since metrics, functions, and evaluations sensitively differ from lower and upper limb exoskeletons. Sensors 2022,22, 3993 4 of 19 We classified the papers based on the metric extracted and the sensor solution adopted. The following definitions apply in this paper for pHEI measurement: •Interaction forces: forces exchanged between the human body and wearable device. •Interaction torques: torques produced by interaction forces. •Interaction pressures: pressure calculated from interaction forces over a contact area. pHEI metrics are classified as follows: • Force metrics: metrics extracted from interaction force measurement, including normal and shear forces as well as overall interaction force, peak, and average contact force. • Torque metrics: metrics extracted from interaction torque measurement, normally represented by the single interaction torque generated during the task. • Pressure metrics: metrics extracted from interaction pressure measurement such as maximum pressure and pressure distribution. Indirect pHEI metrics were also considered: • Relative motions: relative motion (in one or more dimensions) between a defined part of the human body and the worn device (frame shift, skin slippage). • Misalignment: Mismatch in the correspondence in position and orientation between the anatomical and device joint axes. • Subjective experience metrics (SE): metrics extracted by means of live feedback or questionnaires (Table 1). 3. Results Of the 54 publications selected, 33 (61%) were published in the last 5 years, from 2016 to 2021 (Figure 2). Sensors 2022, 22, x FOR PEER REVIEW 4 of 21 We decided to include both upper limb and lower limb exoskeletons since the proposed solutions can often be shared between the two. For the same reason, both powered exoskeleton, passive exoskeletons, and exosuits have been considered since interaction issues are common among wearable devices. Wrist and hand exoskeletons were excluded from this review since metrics, functions, and evaluations sensitively differ from lower and upper limb exoskeletons. We classified the papers based on the metric extracted and the sensor solution adopted. The following definitions apply in this paper for pHEI measurement: • Interaction forces: forces exchanged between the human body and wearable device. • Interaction torques: torques produced by interaction forces. • Interaction pressures: pressure calculated from interaction forces over a contact area. pHEI metrics are classified as follows: • Force metrics: metrics extracted from interaction force measurement, including normal and shear forces as well as overall interaction force, peak, and average contact force. • Torque metrics: metrics extracted from interaction torque measurement, normally represented by the single interaction torque generated during the task. • Pressure metrics: metrics extracted from interaction pressure measurement such as maximum pressure and pressure distribution. Indirect pHEI metrics were also considered: • Relative motions: relative motion (in one or more dimensions) between a defined part of the human body and the worn device (frame shift, skin slippage). • Misalignment: Mismatch in the correspondence in position and orientation between the anatomical and device joint axes. • Subjective experience metrics (SE): metrics extracted by means of live feedback or questionnaires (Table 1). 3. Results Of the 54 publications selected, 33 (61%) were published in the last 5 years, from 2016 to 2021 (Figure 2). Figure 2. Black dots represents the number of publication per year, dotted line is the black dots trend. Figure 2. Black dots represents the number of publication per year, dotted line is the black dots trend. Figure 3shows the number of publications including each of the considered family of metrics divided into upper and lower limb studies. Interaction was mostly assessed at the lower limbs, with 34 publications (63%) using lower limb devices (including hip exoskeletons and passive leg orthoses) in comparison to the 23 results (42%) obtained for upper body exoskeletons (including shoulder, elbow, and arm support devices). Both groups are counting 3 publications including both upper and lower limb contact measurements. Force-related metrics were preponderant, with 42 publications (78%). Pressure and torque metrics were included in 19 (35%) and 16 (29%) results, respectively. We found a minor Sensors 2022,22, 3993 5 of 19 part of the results including supporting pHEI metrics such as user experience (15%), joint misalignments (9%), and relative motions (7%). Sensors 2022, 22, x FOR PEER REVIEW 5 of 21 Figure 3 shows the number of publications including each of the considered family of metrics divided into upper and lower limb studies. Interaction was mostly assessed at the lower limbs, with 34 publications (63%) using lower limb devices (including hip exoskeletons and passive leg orthoses) in comparison to the 23 results (42%) obtained for upper body exoskeletons (including shoulder, elbow, and arm support devices). Both groups are counting 3 publications including both upper and lower limb contact measurements. Force-related metrics were preponderant, with 42 publications (78%). Pressure and torque metrics were included in 19 (35%) and 16 (29%) results, respectively. We found a minor part of the results including supporting pHEI metrics such as user experience (15%), joint misalignments (9%), and relative motions (7%). Figure 3. Number of publications including general pHEI metrics divided for upper and lower limb devices. Metrics from Figure 3 are further detailed, dividing force metrics into overall force metrics, normal (perpendicular to the surface), tangential, and distributed force metrics. The same division is applied for pressure metrics excluding overall pressure since no metrics could fit. Results including these metrics are matched with the relative instrumentation used for their extraction. Figure 4 presents the number of results for each proposed metric, sensor solution, and the intersection between the two axes. Concerning the instrumentation, load cells (1-axis, 3-axis, and 6-axis) were used in half of the works (26 publications, 48%) to extract force and torque metrics. Optical systems such as fiber optics and laser sensors accounted for 15% of the results. The use of sensors based on force sensing resistors (FSRs) was found in 13 publications (24%), while optical motion tracking systems and air-based pressure sensors (air cushions, pneumatic pads) were found in 13% of the results. Pressure pads different from the above-mentioned technologies were found in 4 studies (7%). The remaining solutions, i.e., strain gauges, goniometers, inclinometers, and capacitive sensors, were found in 16% of the results. Questionnaires were used to extract user-experience metrics and were only considered when pHEI measurements were also included, accounting for 6 publications (11% of the Figure 3. Number of publications including general pHEI metrics divided for upper and lower limb devices. Metrics from Figure 3are further detailed, dividing force metrics into overall force metrics, normal (perpendicular to the surface), tangential, and distributed force metrics. The same division is applied for pressure metrics excluding overall pressure since no metrics could fit. Results including these metrics are matched with the relative instrumentation used for their extraction. Figure 4presents the number of results for each proposed metric, sensor solution, and the intersection between the two axes. Concerning the instrumentation, load cells (1-axis, 3-axis, and 6-axis) were used in half of the works (26 publications, 48%) to extract force and torque metrics. Optical systems such as fiber optics and laser sensors accounted for 15% of the results. The use of sensors based on force sensing resistors (FSRs) was found in 13 publications (24%), while optical motion tracking systems and air-based pressure sensors (air cushions, pneumatic pads) were found in 13% of the results. Pressure pads different from the above-mentioned technologies were found in 4 studies (7%). The remaining solutions, i.e., strain gauges, goniometers, inclinometers, and capacitive sensors, were found in 16% of the results. Questionnaires were used to extract user-experience metrics and were only considered when pHEI measurements were also included, accounting for 6 publications (11% of the results). Other than questionnaires, visual analog scales (VAS) were also used to extract perceived discomfort [ 18 ]. Among the sensor solutions presented in the recordings, 14 (28%) proposed customized sensor solutions for their pHEI measurements [ 9 , 18 – 36 ]. Most of the developed solutions were FSR-based [ 19 , 25 , 30 , 33 , 35 ] or air-based pressure sensors [ 18 , 23 , 28 , 29 , 31 ]. Optical solutions were divided into optical-fiber sensors [ 26 , 27 , 36 ] and optoelectronic laser-based sensors [ 9 , 20 , 21 ]. The remaining solutions were composed of force sensor [ 22 ], tactile sensor [ 24 ], 3D-printed capacitive sensors [ 32 ], and elastic band able to measure interaction through its deformation [34]. Sensors 2022,22, 3993 6 of 19 Sensors 2022, 22, x FOR PEER REVIEW 6 of 21 results). Other than questionnaires, visual analog scales (VAS) were also used to extract perceived discomfort [18]. Among the sensor solutions presented in the recordings, 14 (28%) proposed customized sensor solutions for their pHEI measurements [9,18–36]. Most of the developed solutions were FSR-based [19,25,30,33,35] or air-based pressure sensors [18,23,28,29,31]. Optical solutions were divided into optical-fiber sensors [26,27,36] and optoelectronic laser-based sensors [9,20,21]. The remaining solutions were composed of force sensor [22], tactile sensor [24], 3D-printed capacitive sensors [32], and elastic band able to measure interaction through its deformation [34]. Figure 4. Metrics and sensors solutions in the results. Bar plot on the right represents the number of publications including the listed sensor solutions. Bar plot on the bottom represents the number of publications including the listed metrics. Circles represent the number of studies extracting the relative metric through the relative sensor solution at the intersection. Force metrics were preponderant, and specifically one-dimensional forces (typically normal to the contact) were generally used for control purposes [19,28,33,37–41] or for contact evaluation strategies such as interface design evaluation [35,42], human–device kinematic compatibility [43,44], misalignment evaluation [31,45–48], or intention detection [40,49]. Normal force metrics were generally the mean absolute value of normal force during the task [18,45,50], force root mean square (RMS) [51–53], average force in a cyclic task [9,39,53–57], peak force [9,58–60], and force range [61]. Normal force mapping allowed the detection of possible areas for interface improvements [24,30,35,42,52]. Figure 4. Metrics and sensors solutions in the results. Bar plot on the right represents the number of publications including the listed sensor solutions. Bar plot on the bottom represents the number of publications including the listed metrics. Circles represent the number of studies extracting the relative metric through the relative sensor solution at the intersection. Force metrics were preponderant, and specifically one-dimensional forces (typically normal to the contact) were generally used for control purposes [ 19 , 28 , 33 , 37 – 41 ] or for contact evaluation strategies such as interface design evaluation [ 35 , 42 ], human–device kinematic compatibility [ 43 , 44 ], misalignment evaluation [ 31 , 45 – 48 ], or intention detection [ 40 , 49 ]. Normal force metrics were generally the mean absolute value of normal force during the task [ 18 , 45 , 50 ], force root mean square (RMS) [ 51 – 53 ], average force in a cyclic task [ 9 , 39 , 53 – 57 ], peak force [ 9 , 58 – 60 ], and force range [ 61 ]. Normal force mapping allowed the detection of possible areas for interface improvements [24,30,35,42,52]. Normal forces were also used for pressure prediction in new sensory solutions [ 29 ]. Torque metrics are normally taken at the joint level to compute interaction torque transferred to the user through the physical interface. Torques can be used in pHEI models to compute how loads are transferred, used for device control [ 34 , 38 , 51 , 58 , 62 , 63 ], and pHEI prediction and estimation [ 29 , 45 , 46 , 52 , 53 , 64 , 65 ]. Pressure measurements were often accompanied by pressure distribution evaluations [ 20 , 21 , 23 , 25 , 32 , 66 , 67 ], followed by maximum pressure reached during the task [ 9 , 31 , 55 , 67 , 68 ]. Maximum shear pressure was found only in [ 56 , 61 ], while strapping pressure was also included in a minor part of the results [ 18 , 45 ]. All the studies including pHEI modelling also physically measured pHEI in accordance with our review requirements. The use of models for pHEI evaluation is still limited, with direct measurements being the preferred option. Seventeen results (20%) implemented contact mod- Sensors 2022,22, 3993 7 of 19 elization, but 10 of them were for control purposes [37,38,40,41,49–51,54,58,62,63] , while 7 results modelled human–device interactions for pHEI prediction or evaluation [18,45,46,52,53,64,65] . Interaction was simply modelled with kinematic parameters for misalignment prediction [ 46 ]. Later, human–interface contact was modelled by a spring [ 45 , 52 ] or spring-damper element [ 18 , 58 , 65 ]. In [ 64 ], a more advanced model including the knee angle was needed because the spring damper was found to be insufficient to describe the interaction forces. Nonlinear spring-damper elements were suggested to better describe the contact behavior, at the cost of a higher associated uncertainty [ 65 ]. The stiffness and shape of the subject were claimed to change with motion. Therefore, an improved spring-damper-attitude model including limb position was needed for pHEI modelization in [53]. Twenty-two results (44%) focused on evaluating or improving pHEI safety [ 18 , 20 , 23 – 25 , 30 , 32 , 42 , 45 , 46 , 50 , 53 , 55 , 56 , 59 , 61 , 64 , 66 – 70 ]. However, 10 of them effectively compared results with safety references [18,20,25,45,55,56,59,66,68,69]. A minor part of these results considered shear pressures [ 56 , 61 , 69 ], whereas only two studies evaluated and applied safety thresholds [56,69]. Extensive presentation of the results is shown in Table 2, listing results by first author, year of publication, metrics, sensors for their extraction, synthetized protocol applied, device used, and sensorized part of the body. Table 1. Results including questionnaires with the related extracted metrics. Ref. Questionnaire Output [45] NASA TLX [71]Comfort, Physical demand, Mental demand, Temporal demand, operator performance, Effort [47]Custom Borg scale [72]Perceived comfort, Physical load [54] Custom Comfort, interface preference [55] Custom Comfort [70] Custom Safety [68] Borg category ratio (CR-10) [72] Van der Grinten and Smitt System Usability Scale (SUS) Perceived musculoskeletal effort (arm, trunk, leg) Local Perceived Pressure (back/shoulders, arms, chest, and belly/hips) Usability of the exoskeleton Table 2. Review summary. IF: interaction force, IT: interaction torque, IP: interaction pressure, n.a.: not applicable. Author and Ref. Year pHEI Metrics Sensor Protocol Device Sensor Placement Akyiama et al. [46]2012 IF/support metrics: Normal force, Misalignment Load cell 3D motion capture system n.a. Lower limb exoskeleton frame mounted on a dummy leg Lower leg Upper leg Akyiama et al. [64]2015 IF/support metrics: Normal/Tangential IF, Relative motions 3-axis Load cell 3D optical motion capture system 10 sit-to-stand motions Leg type motor-actuated lower-limb orthosis Lower leg Upper leg Akyiama et al. [69]2012 IF/IT/support metrics: Normal/Tangential IF, Interaction moment, Skin slippage, Relative motions 3-axis Load cell Slip sensor (2D imaging devices) 3D optical motion capture system 15 sit-to-stand motions Lower limb physical assistant robot Upper leg Amigo et al. [48]2012 IF/support metrics: Normal/Tangential IF, Misalignment 6-axis Load cell Full bridge strain gauges Forearm flexion-extension Arm orthoses Forearm Sensors 2022,22, 3993 8 of 19 Table 2. Cont. Author and Ref. Year pHEI Metrics Sensor Protocol Device Sensor Placement Awad et al. [70]2020 IF/support metrics: Disturbing force, Adverse event observation, Patient feedback Load cell Questionnaire 20 min of overground walking practice, 20 min of treadmill walking practice Lower limb soft exosuits Not specified Beil et al. [40]2018 IF: Overall 3D IF 3-axis Load cell 13 different motion tasks Lower limb exoskeleton Upper leg Lower leg Bartenbach et al. [47]2015 IF/support metrics: Overall IF, Misalignments, Perceived discomfort, Physical load Load cell 3D optical motion capture system Questionnaire 2 min of familiarization and 20 s of test on a treadmill Lower limb exoskeleton Lower leg Upper leg Bessler et al. [30]2019 IF/IT: Normal/Tangential IF, Force distribution, Interaction torque FSR sensor 3-axis load cell Moving forearm along 3 axis Forearm support Forearm Choi et al. [19]2018 IF: Normal force FSR sensor Treadmill walking Hip exoskeleton Thigh Christensen et al. [43]2018 IF: Normal force FSR sensor n.a. 3DOF spherical mechanism for shoulder joint exo Arm Forearm Del-ama et al. [39]2011 IF/IT: Mean interaction force Mean interaction torque (calculated) Gauge bridge 10 min leg swing Lower limb exoskeleton Lower leg De Rossi, Lenzi et al. [21] 2010 IP: Pressure distribution Matrix of optoelectronic sensors treadmill walk at 4 Km/h in 3 different conditions: “no-assistance” “low-assistance” and “high-assistance” Lower limb robotic platform Upper leg Lower leg Donati, De rossi et al. [9] 2013 IF/IP: Average IF, Maximum IF, Maximum IP Load cell Matrix of optoelectronic sensors Upper limb: Passive arm Active arc Lower limb: Transparent mode Viscous field Elbow active orthoses Lower limb robotic platform Forearm Upper leg Lower leg Fan et al. [58]2013 IF: Max normal IF, Normal IF Airbags sensor knee extension to 30◦and 60◦ Lower limb exoskeleton Calf Georgarakis et al. [61]2018 IF/IP: Normal/tangential IF range, Max normal/tangential IF, Shear IP range 3-axis Force sensor relax or contract the forearm muscles by grasping a handle according to different force pattern Upper limb exoskeleton Forearm Ghonasgi et al. [35]2021 IF: Force distribution FSR sensor matrix Elbow extensions Upper limb exoskeleton Upper arm Grosu et al. [22]2017 IF: Normal force 3-axis Force sensor n.a. Lower limb exoskeleton Hip Hasegawa et al. [23]2011 IP: Pressure distribution Active air mat Arm suspended Arm moving Arm lifting a weight Upper limb exoskeleton Forearm Huang et al. [38]2015 IF: Total normal force over 4 point FSR sensor n.a. Upper limb power-assist robotic exoskeleton Forearm Huysamen et al. [68]2018 IP/support metrics: Maximum pressure, Local perceived pressure, Subjective usability Pressure mat Questionnaires Lifting a load from the ground, with/without device, with/without load Back powered exoskeleton Shoulder Hip/lower back Thigh Sensors 2022,22, 3993 9 of 19 Table 2. Cont. Author and Ref. Year pHEI Metrics Sensor Protocol Device Sensor Placement Islam et al. [33]2019 IF: Normal force variation FSR sensor band Arm liftingh with different payloads Passive arm exoskeleton Upper arm Ito et al. [24]2018 IF: Force distribution Tactile sensor n.a. Wearable robot for upper limb Upper arm Kim et al. [57]2013 IF: Average normal IF Load cell Walking on a mat Prototype lower limb exoskeleton Shank Kim et al. [31]2021 IP/support metrics: Maximum pressure, Average normalized IP, Misalignment Air-bladder pressure sensor 3D optical motion capture system Knee flexion-extension using a pulling cable attached to the foot Lower limb exoskeleton Shank Langlois et al. [18]2020 IF/IP/support metrics: Normal IF, Strapping pressure, Relative motions, Energy dissipation, Perceived comfort Air cushion 3D optical motion capture system Visual analog scale randomly chosen motions at 5 different inflation pressure 7 DOF robotic manipulator Arm Langlois et al. [32]2021 IP: Pressure distribution 3D printed capacitive sensor pads Lifting weights with arm straight Upper arm interface Arm Lealjunior et al. [26] 2018 IT: Lifting torque Optical fiber sensor Potentiometer Free knee flexion and extension Lower limb exoskeleton Shank Lealjunior et al. [27] 2018 IF: Normal force Optical fiber sensor (Bragg) Free knee flexion and extension Lower limb exoskeleton Shank Lealjunior et al. [36] 2019 IF: Normal force Optical fiber sensor Load cell Free knee flexion and extension Lower limb exoskeleton Calf Lee et al. [41]2014 IF: Normal/Tangential IF Load cell Arm lifting at different load conditions Upper limb exoskeleton Handle Lenzi et al. [20]2011 IP: Normal IP, Pression distribution Matrix of optoelectronic sensors 1. leaving arm passive; 2. moving faster (higher frequency) than the robot; 3. moving slower than the robot; 4. imposing higher flexion angle than the robot; 5. imposing a higher extension angle than the robot. Elbow active orthoses Forearm Levesque et al. [42]2017 IF: Force distribution FSR matrix sensor FSR sensor legged deep squats, lunges, as well as stair climb and descent Lower limb exoskeleton Thigh Knee Tibia Li et al. [44]2019 IF/IT: 3-D IF, Normalized IF over 3-axis, 3-D IT, Normalized IT over 3-axis 6-axis Load cell Walking on treadmill Prototype lower limb exoskeleton Upper limb Lower limb Loboprat et al. [54] 2016 IF/support metrics: Average normal IF, Comfort Load cell EMG Questionnaire Elbow flexion-extension movements against gravity Passive upper limb support Handle Long et al. 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