Towards a validated glossary of usability attributes for the evaluation of wearable robotic devices

BACKGROUND

Despite technical advances in the field of wearable robotic devices (WRD), there is still limited user acceptance of these technologies. While usability often comes as a key factor influencing acceptance, there is a scattered landscape of definitions and scopes for the term. To advance usability evaluation, and to integrate usability features as design requirements during technology development, there is a need for benchmarks and shared terminology. These should be easily accessible and implementable by developers.

METHODS

An initial set of usability attributes (UA) was extracted from a literature survey on usability evaluation in WRD. The initial set of attributes was enriched and locally validated with seven developers of WRD through an online survey and a focus group. The locally validated glossary was then externally validated through a globally distributed online survey.

RESULTS

The result is the Robotics Usability Glossary (RUG), a comprehensive glossary of 41 UA validated by 70 WRD developers from 17 countries, ensuring its generalizability. 31 of the UA had high agreement scores among respondents and 27 were considered highly relevant in the field, but only 11 of them had been included as design criteria by the respondents.

CONCLUSIONS

Multiple UA ought to be considered for a comprehensive usability assessment. Usability remains inadequately incorporated into device development, indicating a need for increased awareness and end-user perspective. The RUG can be readily accessed through an online platform, the Interactive Usability Toolbox (IUT), developed to provide context-specific outcome measures and usability evaluation methods. Overall, this effort is an important step towards improving and promoting usability evaluation practices within WRD. It has the potential to pave the way for establishing usability evaluation benchmarks that further endorse the acceptance of WRD.

First-in-human demonstration of floating EMG sensors and stimulators wirelessly powered and operated by volume conduction

BACKGROUND

Recently we reported the design and evaluation of floating semi-implantable devices that receive power from and bidirectionally communicate with an external system using coupling by volume conduction. The approach, of which the semi-implantable devices are proof-of-concept prototypes, may overcome some limitations presented by existing neuroprostheses, especially those related to implant size and deployment, as the implants avoid bulky components and can be developed as threadlike devices. Here, it is reported the first-in-human acute demonstration of these devices for electromyography (EMG) sensing and electrical stimulation.

METHODS

A proof-of-concept device, consisting of implantable thin-film electrodes and a nonimplantable miniature electronic circuit connected to them, was deployed in the upper or lower limb of six healthy participants. Two external electrodes were strapped around the limb and were connected to the external system which delivered high frequency current bursts. Within these bursts, 13 commands were modulated to communicate with the implant.

RESULTS

Four devices were deployed in the biceps brachii and the gastrocnemius medialis muscles, and the external system was able to power and communicate with them. Limitations regarding insertion and communication speed are reported. Sensing and stimulation parameters were configured from the external system. In one participant, electrical stimulation and EMG acquisition assays were performed, demonstrating the feasibility of the approach to power and communicate with the floating device.

CONCLUSIONS

This is the first-in-human demonstration of EMG sensors and electrical stimulators powered and operated by volume conduction. These proof-of-concept devices can be miniaturized using current microelectronic technologies, enabling fully implantable networked neuroprosthetics.

Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study

BACKGROUND

There are a lot of tools to use for fall assessment, but there is not yet one that predicts the risk of falls in the elderly. This study aims to evaluate the use of the G-STRIDE prototype in the analysis of fall risk, defining the cut-off points to predict the risk of falling and developing a predictive model that allows discriminating between subjects with and without fall risks and those at risk of future falls.

METHODS

An observational, multicenter case-control study was conducted with older people coming from two different public hospitals and three different nursing homes. We gathered clinical variables ( Short Physical Performance Battery (SPPB), Standardized Frailty Criteria, Speed 4 m walk, Falls Efficacy Scale-International (FES-I), Time-Up Go Test, and Global Deterioration Scale (GDS)) and measured gait kinematics using an inertial measure unit (IMU). We performed a logistic regression model using a training set of observations (70% of the participants) to predict the probability of falls.

RESULTS

A total of 163 participants were included, 86 people with gait and balance disorders or falls and 77 without falls; 67,8% were females, with a mean age of 82,63 ± 6,01 years. G-STRIDE made it possible to measure gait parameters under normal living conditions. There are 46 cut-off values of conventional clinical parameters and those estimated with the G-STRIDE solution. A logistic regression mixed model, with four conventional and 2 kinematic variables allows us to identify people at risk of falls showing good predictive value with AUC of 77,6% (sensitivity 0,773 y specificity 0,780). In addition, we could predict the fallers in the test group (30% observations not in the model) with similar performance to conventional methods.

CONCLUSIONS

The G-STRIDE IMU device allows to predict the risk of falls using a mixed model with an accuracy of 0,776 with similar performance to conventional model. This approach allows better precision, low cost and less infrastructures for an early intervention and prevention of future falls.

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

BACKGROUND

Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data.

METHODS

A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles.

RESULTS

Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results.

CONCLUSIONS

This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

A qualitative study to elicit user requirements for lower limb wearable exoskeletons for gait rehabilitation in spinal cord injury

OBJECTIVE

We aim to determine a comprehensive set of requirements, perceptions, and expectations that people with spinal cord injury (SCI) and the clinicians in charge of their rehabilitation have regarding the use of wearable robots (WR) for gait rehabilitation.

BACKGROUND

There are concerns due to the limited user acceptance of WR for gait rehabilitation. Developers need to emphasize understanding the needs and constraints of all stakeholders involved, including the real-life dynamics of rehabilitation centers.

METHODS

15 people with SCI, 9 without experience with WR and 6 with experience with these technologies, and 10 clinicians from 3 rehabilitation centers in Spain were interviewed. A directed content analysis approach was used.

RESULTS

78 codes grouped into 9 categories (physical results, usability, psychology-related codes, technical characteristics, activities, acquisition issues, context of use, development of the technologies and clinical rehabilitation context) were expressed by at least 20% of the users interviewed, of whom 16 were not found in the literature. The agreement percentage between each group and subgroup included in the study, calculated as the number of codes that more than 20% of both groups expressed, divided over the total amount of codes any of those two groups agreed on (≥ 20%), showed limited agreement between patients and clinicians (50.00%) and between both types of patients (55.77%). The limited accessibility and availability of lower limb exoskeletons for gait rehabilitation arose in most of the interviews.

CONCLUSIONS

The limited agreement percentage between patients and clinicians indicates that including both types of users in the design process of these technologies is important, given that their requirements are complementary. Engaging users with prior technology experience is recommended, as they often exhibit strong internal consensus and articulate well-defined requirements. This study adds up the knowledge available in the literature and the new codes found in our data, which enlighten important aspects that ought to be addressed in the field to develop technologies that respond to users' needs, are usable and feasible to implement in their intended contexts.

APPLICATION

The set of criteria summarized in our study will be useful to guide the design, development, and evaluation of WR for gait rehabilitation to meet user's needs and allow them to be implemented in their intended context of use.

Construct Validity of the Gait Deviation Index for People With Incomplete Spinal Cord Injury (GDI-SCI)

BACKGROUND

The Gait Deviation Index for Spinal Cord Injury (SCI-GDI) was recently proposed as a dimensionless multivariate kinematic measure based on 21 gait features derived from 3-dimensional kinematic data which quantifies gait impairment of adult population with incomplete spinal cord injury (iSCI) relative to the normative gait of a healthy group. Nevertheless, no validity studies of the SCI-GDI have been published to date.

OBJECTIVE

To assess the construct validity of the SCI-GDI in adult population following iSCI. Methods. SCI-GDI data were obtained from a sample of 50 healthy volunteers and 35 adults with iSCI. iSCI group was also assessed with the following measures: 10-Meter Walk Test (10MWT) at both self-selected (SS) and maximum speeds, Timed Up and Go Test (TUGT), SS and maximum levels of the Walking Index for Spinal Cord Injury (WISCI) II, mobility items of the Spinal Cord Independence Measure III (SCIM IIIIOMob), Lower Extremity Motor Score (LEMS), and Modified Ashworth Scale (MAS). Spearman's correlation coefficient was used to investigate the relationship with the SCI-GDI.

RESULTS

SCI-GDI shows strong correlation with the 10MWT (r ≥ -.716) and good correlation with LEMS (r = .638), TUGT (r = -.582), SS WISCI II levels (r = .521), and SCIM IIIIOMob (r = .501). No significant correlations were found with maximum WISCI II levels and MAS (P > .006).

CONCLUSIONS

Construct validity of the SCI-GDI was demonstrated with the 10MWT, TUGT, LEMS, SCIM IIIIOMob, and SS WISCI II levels for independently walking adults with iSCI. Future work will include assessing the psychometric characteristics with a more heterogeneous sample, also considering the pediatric population.

A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults

The GSTRIDE database contains information of the health status assessment of 163 elderly adults. We provide socio-demographic data, functional and frailty variables, and the outcomes from tests commonly performed for the evaluation of elder people. The database contains gait parameters estimated from the measurements of an Inertial Measurement Unit (IMU) placed on the foot of volunteers. These parameters include the total walking distance, the number of strides and multiple spatio-temporal gait parameters, such as stride length, stride time, speed, foot angles and clearance, among others. The main processed database is stored, apart from MS Excel, in CSV format to ensure their usability. The database is complemented with the raw IMU recordings in TXT format, in order to let researchers test other algorithms of gait analysis. We include the Python programming codes as a base to reproduce or modify them. The database stores data to study the frailty-related parameters that distinguish faller and non-faller populations, and analyze the gait-related parameters in the frail subjects, which are essential topics for the elderly.

Assessing falls in the elderly population using G-STRIDE foot-mounted inertial sensor

Falls are one of the main concerns in the elderly population due to their high prevalence and associated consequences. Guidelines for the management of the elder with falls are comprised of multidimensional assessments, especially gait and balance. Daily clinical practice needs for timely, effortless, and precise tools to assess gait. This work presents the clinical validation of the G-STRIDE system, a 6-axis inertial measurement unit (IMU) with onboard processing algorithms, that allows the calculation of walking-related metrics correlated with clinical markers of fall risk. A cross-sectional case-control study was conducted with 163 participants (falls and non-falls groups). All volunteers were assessed with clinical scales and conducted a 15-min walking test at a self-selected pace while wearing the G-STRIDE. G-STRIDE is a low-cost solution to facilitate the transfer to society and clinical evaluations. It is open hardware and flexible and, thus, has the advantage of providing runtime data processing. Walking descriptors were derived from the device, and a correlation analysis was conducted between walking and clinical variables. G-STRIDE allowed measuring walking parameters in non-restricted walking conditions (e.g. hallway). Walking parameters statistically discriminate between falls and non-falls groups. We found good/excellent estimation accuracy (ICC = 0.885; [Formula: see text]) for walking speed, showing good/excellent correlation between gait speed and several clinical variables. G-STRIDE can calculate walking-related metrics that allow for discrimination between falls and non-falls groups, which correlates with clinical indicators of fall risk. A preliminary fall-risk assessment based on the walking parameters was found to improve the Timed Up and Go test in the identification of fallers.

Exoskeleton-based training improves walking independence in incomplete spinal cord injury patients: results from a randomized controlled trial

BACKGROUND

In recent years, ambulatory lower limb exoskeletons are being gradually introduced into the clinical practice to complement walking rehabilitation programs. However, the clinical evidence of the outcomes attained with these devices is still limited and nonconclusive. Furthermore, the user-to-robot adaptation mechanisms responsible for functional improvement are still not adequately unveiled. This study aimed to (1) assess the safety and feasibility of using the HANK exoskeleton for walking rehabilitation, and (2) investigate the effects on walking function after a training program with it.

METHODS

A randomized controlled trial was conducted including a cohort of 23 patients with less than 1 year since injury, neurological level of injury (C2-L4) and severity (American Spinal Cord Injury Association Impairment Scale [AIS] C or D). The intervention was comprised of 15 one-hour gait training sessions with lower limb exoskeleton HANK. Safety was assessed through monitoring of adverse events, and pain and fatigue through a Visual Analogue Scale. LEMS, WISCI-II, and SCIM-III scales were assessed, along with the 10MWT, 6MWT, and the TUG walking tests (see text for acronyms).

RESULTS

No major adverse events were reported. Participants in the intervention group (IG) reported 1.8 cm (SD 1.0) for pain and 3.8 (SD 1.7) for fatigue using the VAS. Statistically significant differences were observed for the WISCI-II for both the "group" factor (F = 16.75, p < 0.001) and "group-time" interactions (F = 8.87; p < 0.01). A post-hoc analysis revealed a statistically significant increase of 3.54 points (SD 2.65, p < 0.0001) after intervention for the IG but not in the CG (0.7 points, SD 1.49, p = 0.285). No statistical differences were observed between groups for the remaining variables.

CONCLUSIONS

The use of HANK exoskeleton in clinical settings is safe and well-tolerated by the patients. Patients receiving treatment with the exoskeleton improved their walking independence as measured by the WISCI-II after the treatment.

Powering Electronic Implants by High Frequency Volume Conduction: In Human Validation

OBJECTIVE

Wireless power transfer (WPT) is used as an alternative to batteries to accomplish miniaturization in electronic medical implants. However, established WPT methods require bulky parts within the implant or cumbersome external systems, hindering minimally invasive deployments and the development of networks of implants. As an alternative, we propose a WPT approach based on volume conduction of high frequency (HF) current bursts. These currents are applied through external electrodes and are collected by the implants through two electrodes at their opposite ends. This approach avoids bulky components, enabling the development of flexible threadlike implants.

METHODS

We study in humans if HF (6.78 MHz) current bursts complying with safety standards and applied through two textile electrodes strapped around a limb can provide substantial powers from pairs of implanted electrodes.

RESULTS

Time averaged electric powers obtained from needle electrodes (diameter = 0.4 mm, length = 3 mm, separation = 30 mm) inserted into arms and lower legs of five healthy participants were 5.9 ± 0.7 mW and 2.4 ± 0.3 mW respectively. We also characterize the coupling between the external system and the implants using personalized two-port impedance models generated from medical images.

CONCLUSIONS

The results demonstrate that innocuous and imperceptible HF current bursts that flow through the tissues by volume conduction can be used to wirelessly power threadlike implants.

SIGNIFICANCE

This is the first time that WPT based on volume conduction is demonstrated in humans. This method overcomes the limitations of existing WPT methods in terms of minimal invasiveness and usability.

Derivation of the Gait Deviation Index for Spinal Cord Injury

The Gait Deviation Index (GDI) is a dimensionless multivariate measure of overall gait pathology represented as a single score that indicates the gait deviation from a normal gait average. It is calculated using kinematic data recorded during a three-dimensional gait analysis and an orthonormal vectorial basis with 15 gait features that was originally obtained using singular value decomposition and feature analysis on a dataset of children with cerebral palsy. Ever since, it has been used as an outcome measure to study gait in several conditions, including spinal cord injury (SCI). Nevertheless, the validity of implementing the GDI in a population with SCI has not been studied yet. We investigate the application of these mathematical methods to derive a similar metric but with a dataset of adults with SCI (SCI-GDI). The new SCI-GDI is compared with the original GDI to evaluate their differences and assess the need for a specific GDI for SCI and with the WISCI II to evaluate its sensibility. Our findings show that a 21-feature basis is necessary to account for most of the variance in gait patterns in the SCI population and to provide high-quality reconstructions of the gait curves included in the dataset and in foreign data. Furthermore, using only the first 15 features of our SCI basis, the fidelity of the reconstructions obtained in our population is higher than that when using the basis of the original GDI. The results showed that the SCI-GDI discriminates most levels of the WISCI II scale, except for levels 12 and 18. Statistically significant differences were found between both indexes within each WISCI II level except for 12, 20, and the control group (p < 0.05). In all levels, the average GDI value was greater than the average SCI-GDI value, but the difference between both indexes is larger in data with greater impairment and it reduces progressively toward a normal gait pattern. In conclusion, the implementation of the original GDI in SCI may lead to overestimation of gait function, and our new SCI-GDI is more sensitive to larger gait impairment than the GDI. Further validation of the SCI-GDI with other scales validated in SCI is needed.

Transcranial direct current stimulation combined with robotic therapy for upper and lower limb function after stroke: a systematic review and meta-analysis of randomized control trials

BACKGROUND

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method able to modulate neuronal activity after stroke. The aim of this systematic review was to determine if tDCS combined with robotic therapy (RT) improves limb function after stroke when compared to RT alone.

METHODS

A search for randomized controlled trials (RCTs) published prior to July 15, 2021 was performed. The main outcome was function assessed with the Fugl-Meyer motor assessment for upper extremities (FM/ue) and 10-m walking test (10MWT) for the lower limbs. As secondary outcomes, strength was assessed with the Motricity Index (MI) or Medical Research Council scale (MRC), spasticity with the modified Ashworth scale (MAS), functional independence with the Barthel Index (BI), and kinematic parameters.

RESULTS

Ten studies were included for analysis (n = 368 enrolled participants). The results showed a non-significant effect for tDCS combined with RT to improve upper limb function [standardized mean difference (SMD) = - 0.12; 95% confidence interval (CI): - 0.35-0.11)]. However, a positive effect of the combined therapy was observed in the lower limb function (SMD = 0.48; 95% CI: - 0.15-1.12). Significant results favouring tDCS combined with RT were not found in strength (SMD = - 0.15; 95% CI: - 0.4-0.1), spasticity [mean difference (MD) =  - 0.15; 95% CI: - 0.8-0.5)], functional independence (MD = 2.5; 95% CI: - 1.9-6.9) or velocity of movement (SMD = 0.06; 95% CI: - 0.3-0.5) with a "moderate" or "low" recommendation level according to the GRADE guidelines.

CONCLUSIONS

Current findings suggest that tDCS combined with RT does not improve upper limb function, strength, spasticity, functional independence or velocity of movement after stroke. However, tDCS may enhance the effects of RT alone for lower limb function. tDCS parameters and the stage or type of stroke injury could be crucial factors that determine the effectiveness of this therapy.

Effect of posture and body weight loading on spinal posterior root reflex responses

The posterior root muscle response (PRM) is a monosynaptic reflex that is evoked by single pulse transcutaneous spinal cord stimulation (tSCS). The main aim of this work was to analyse how body weight loading influences PRM reflex threshold measured from several lower limb muscles in healthy participants. PRM reflex responses were evoked with 1-ms rectangular monophasic pulses applied at an interval of 6 s via a self-adhesive electrode (9 × 5 cm) at the T11-T12 vertebral level. Surface electromyographic activity of lower limb muscles was recorded during four different conditions, one in decubitus supine (DS) and the other three involving standing at 100%, 50%, and 0% body weight loading (BW). PRM threshold intensity, peak-to-peak amplitude, and latency for each muscle were analysed in different conditions study. PRM reflex threshold increased with body weight unloading compared with DS, and the largest change was observed between DS and 0% BW for the proximal muscles and between DS and 50% BW for distal muscles. Peak-to-peak amplitude analysis showed only a significant mean decrease of 34.6% (SD 10.4, p = 0.028) in TA and 53.6% (SD 15.1, p = 0.019) in GM muscles between DS and 50% BW. No significant differences were observed for PRM latency. This study has shown that sensorimotor networks can be activated with tSCS in various conditions of body weight unloading. Higher stimulus intensities are necessary to evoke reflex response during standing at 50% body weight loading. These results have practical implications for gait rehabilitation training programmes that include body weight support.

Intramuscular EMG-Driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients

OBJECTIVE

Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling.

METHODS

Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG.

RESULTS

The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients.

CONCLUSION

We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients.

SIGNIFICANCE

Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation.

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.

Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals

BACKGROUND

Gait training for individuals with neurological disorders is challenging in providing the suitable assistance and more adaptive behaviour towards user needs. The user specific adaptation can be defined based on the user interaction with the orthosis and by monitoring the user intentions. In this paper, an adaptive control model, commanded by the user intention, is evaluated using a lower limb exoskeleton with incomplete spinal cord injury individuals (SCI).

METHODS

A user intention based adaptive control model has been developed and evaluated with 4 incomplete SCI individuals across 3 sessions of training per individual. The adaptive control model modifies the joint impedance properties of the exoskeleton as a function of the human-orthosis interaction torques and the joint trajectory evolution along the gait sequence, in real time. The volitional input of the user is identified by monitoring the neural signals, pertaining to the user's motor activity. These volitional inputs are used as a trigger to initiate the gait movement, allowing the user to control the initialization of the exoskeleton movement, independently. A Finite-state machine based control model is used in this set-up which helps in combining the volitional orders with the gait adaptation.

RESULTS

The exoskeleton demonstrated an adaptive assistance depending on the patients' performance without guiding them to follow an imposed trajectory. The exoskeleton initiated the trajectory based on the user intention command received from the brain machine interface, demonstrating it as a reliable trigger. The exoskeleton maintained the equilibrium by providing suitable assistance throughout the experiments. A progressive change in the maximum flexion of the knee joint was observed at the end of each session which shows improvement in the patient performance. Results of the adaptive impedance were evaluated by comparing with the application of a constant impedance value. Participants reported that the movement of the exoskeleton was flexible and the walking patterns were similar to their own distinct patterns.

CONCLUSIONS

This study demonstrates that user specific adaptive control can be applied on a wearable robot based on the human-orthosis interaction torques and modifying the joints' impedance properties. The patients perceived no external or impulsive force and felt comfortable with the assistance provided by the exoskeleton. The main goal of such a user dependent control is to assist the patients' needs and adapt to their characteristics, thus maximizing their engagement in the therapy and avoiding slacking. In addition, the initiation directly controlled by the brain allows synchronizing the user's intention with the afferent stimulus provided by the movement of the exoskeleton, which maximizes the potentiality of the system in neuro-rehabilitative therapies.

Simultaneous estimation of human and exoskeleton motion: A simplified protocol

Adequate benchmarking procedures in the area of wearable robots is gaining importance in order to compare different devices on a quantitative basis, improve them and support the standardization and regulation procedures. Performance assessment usually focuses on the execution of locomotion tasks, and is mostly based on kinematic-related measures. Typical drawbacks of marker-based motion capture systems, gold standard for measure of human limb motion, become challenging when measuring limb kinematics, due to the concomitant presence of the robot. This work answers the question of how to reliably assess the subject's body motion by placing markers over the exoskeleton. Focusing on the ankle joint, the proposed methodology showed that it is possible to reconstruct the trajectory of the subject's joint by placing markers on the exoskeleton, although foot flexibility during walking can impact the reconstruction accuracy. More experiments are needed to confirm this hypothesis, and more subjects and walking conditions are needed to better characterize the errors of the proposed methodology, although our results are promising, indicating small errors.

Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).

Modular control of gait after incomplete spinal cord injury: differences between sides

STUDY DESIGN

This is an analytical descriptive study.

OBJECTIVES

The main goal of this study was to compare the modular organization of bilateral lower limb control in incomplete spinal cord injury (iSCI) patients during overground walking, using muscle synergies analysis. The secondary goal was to determine whether the similarity between the patients and control group correlate with clinical indicators of walking performance.

SETTING

This study was conducted in National Hospital for Spinal Cord Injury (Toledo, Spain).

METHODS

Eight iSCI patients and eight healthy subjects completed 10 walking trials at matched speed. For each trial, three-dimensional motion analysis and surface electromyography (sEMG) analysis of seven leg muscles from both limbs were performed. Muscle synergies were extracted from sEMG signals using a non-negative matrix factorization algorithm. The optimal number of synergies has been defined as the minimum number needed to obtain variability accounted for (VAF) ⩾90%.

RESULTS

When compared with healthy references, iSCI patients showed fewer muscle synergies in the most affected side and, in both sides, significant differences in the composition of synergy 2. The degree of similarity of these variables with the healthy reference, together with the composition of synergy 3 of the most affected side, presented significant correlations (P<0.05) with walking performance.

CONCLUSION

The analysis of muscle synergies shows potential to detect differences between the two sides in patients with iSCI. Specifically, the VAF may constitute a new neurophysiological metric to assess and monitor patients' condition throughout the gait recovery process.

Decoding the Attentional Demands of Gait through EEG Gamma Band Features

Rehabilitation techniques are evolving focused on improving their performance in terms of duration and level of recovery. Current studies encourage the patient’s involvement in their rehabilitation. Brain-Computer Interfaces are capable of decoding the cognitive state of users to provide feedback to an external device. On this paper, cortical information obtained from the scalp is acquired with the goal of studying the cognitive mechanisms related to the users’ attention to the gait. Data from 10 healthy users and 3 incomplete Spinal Cord Injury patients are acquired during treadmill walking. During gait, users are asked to perform 4 attentional tasks. Data obtained are treated to reduce movement artifacts. Features from δ(1 − 4Hz), θ(4 − 8Hz), α(8 − 12Hz), β(12 − 30Hz), γ_low(30 − 50Hz), γ_high(50 − 90Hz) frequency bands are extracted and analyzed to find which ones provide more information related to attention. The selected bands are tested with 5 classifiers to distinguish between tasks. Classification results are also compared with chance levels to evaluate performance. Results show success rates of ∼67% for healthy users and ∼59% for patients. These values are obtained using features from γ band suggesting that the attention mechanisms are related to selective attention mechanisms, meaning that, while the attention on gait decreases the level of attention on the environment and external visual information increases. Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine classifiers provide the best results for all users. Results from patients are slightly lower, but significantly different, than those obtained from healthy users supporting the idea that the patients pay more attention to gait during non-attentional tasks due to the inherent difficulties they have during normal gait. This study provides evidence of the existence of classifiable cortical information related to the attention level on the gait. This fact could allow the development of a real-time system that obtains the attention level during lower limb rehabilitation. This information could be used as feedback to adapt the rehabilitation strategy.

Hybrid gait training with an overground robot for people with incomplete spinal cord injury: a pilot study

Locomotor training has proved to provide beneficial effect in terms of mobility in incomplete paraplegic patients. Neuroprosthetic technology can contribute to increase the efficacy of a training paradigm in the promotion of a locomotor pattern. Robotic exoskeletons can be used to manage the unavoidable loss of performance of artificially driven muscles. Hybrid exoskeletons blend complementary robotic and neuro-prosthetic technologies. The aim of this pilot study was to determine the effects of hybrid gait training in three case studies with persons with incomplete spinal cord injury (iSCI) in terms of locomotion performance during assisted gait, patient-robot adaptations, impact on ambulation and assessment of lower limb muscle strength and spasticity. Participants with iSCI received interventions with a hybrid bilateral exoskeleton for 4 days. Assessment of gait function revealed that patients improved the 6 min and 10 m walking tests after the intervention, and further improvements were observed 1 week after the intervention. Muscle examination revealed improvements in knee and hip sagittal muscle balance scores and decreased score in ankle extensor balance. It is concluded that improvements in biomechanical function of the knee joint after the tested overground hybrid gait trainer are coherent with improvements in gait performance.