Fully embedded myoelectric control for a wearable robotic hand orthosis

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis

PURPOSE

Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care.

METHODS

In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians' perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video.

RESULTS

154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time.

CONCLUSIONS

Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders' perspectives on utility and design.

Non-invasive muscle-machine interface open source project: wearable hand myoelectrical orthosis (MES-FES)

The paper describes the development of an open-source, low-cost, wearable hand myoelectrical orthosis (neuro-orthosis) device for people with hand disabilities. The device uses functional electrical stimulation (FES) driven by myoelectrical signals (MES) to assist hand movements, enabling users to perform daily activities with greater ease and independence. The device comprises a forearm-wearable device developed using the 3D additive manufacturing principle, allowing user customization. Fixed non-disposable electrodes are attached to the myoelectrical orthosis, aiding the correct positioning for the user. The whole control system is stand-alone, and parameters can be controlled by Bluetooth communication, making the device wireless. The paper describes the MES-FES device's design, development, and testing, including its technical specifications, usability, and effectiveness. The open-source project aims to provide an accessible and affordable solution for people with spinal cord lesions while contributing to the growing research on noninvasive muscle-machine interfaces.

Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface

OBJECTIVE

Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs.

APPROACH

To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations.

MAIN RESULTS

Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort.

SIGNIFICANCE

The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.

Novel Wearable HD-EMG Sensor With Shift-Robust Gesture Recognition Using Deep Learning

In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyography (HD-EMG) sensor called EMaGer. The stretchable, wearable sensor adapts to different forearm sizes while maintaining uniform electrode density around the limb. Leveraging this uniformity, we propose novel array barrel-shifting data augmentation (ABSDA) approach used with a convolutional neural network (CNN), and an anti-aliased CNN (AA-CNN), that provides shift invariance around the limb for improved classification robustness to electrode movement, forearm orientation, and inter-session variability. Signals are sampled from a 4×16 HD-EMG array of electrodes at a frequency of 1 kHz and 16-bit resolution. Using data from 12 non-amputated participants, the approach is tested in response to sensor rotation, forearm rotation, and inter-session scenarios. The proposed ABSDA-CNN method improves inter-session accuracy by 25.67% on average across users for 6 gesture classes compared to conventional CNN classification. A comparison with other devices shows that this benefit is enabled by the unique design of the EMaGer array. The AA-CNN yields improvements of up to 63.05% accuracy over non-augmented methods when tested with electrode displacements ranging from -45 ° to +45 ° around the limb. Overall, this article demonstrates the benefits of co-designing sensor systems, processing methods, and inference algorithms to leverage synergistic and interdependent properties to solve state-of-the-art problems.

Individuals with moderate to severe hand impairments may struggle to use EMG control for assistive devices

Neurological trauma, such as stroke, traumatic brain injury (TBI), spinal cord injury, and cerebral palsy can cause mild to severe upper limb impairments. Hand impairment makes it difficult for individuals to complete activities of daily living, especially bimanual tasks. A robotic hand orthosis or hand exoskeleton can be used to restore partial function of an intact but impaired hand. It is common for upper extremity prostheses and orthoses to use electromyography (EMG) sensing as a method for the user to control their device. However some individuals with an intact but impaired hand may struggle to use a myoelectrically controlled device due to potentially confounding muscle activity. This study was conducted to evaluate the application of conventional EMG control techniques as a robotic orthosis/exoskeleton user input method for individuals with mild to severe hand impairments. Nine impaired subjects and ten healthy subjects were asked to perform repeated contractions of muscles in their forearm and then onset analysis and feature classification were used to determine the accuracy of the employed EMG techniques. The average accuracy for contraction identification across employed EMG techniques was 95.4% ± 4.9 for the healthy subjects and 73.9% ± 13.1 for the impaired subjects with a range of 47.0% ± 19.1 - 91.6% ± 8.5. These preliminary results suggest that the conventional EMG control technologies employed in this paper may be difficult for some impaired individuals to use due to their unreliable muscle control.

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future

The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs “Mapping Knowledge Domains” (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.

An sEMG-Controlled Forearm Bracelet for Assessing and Training Manual Dexterity in Rehabilitation: A Systematic Review

Background: The ability to perform activities of daily living (ADL) is essential to preserving functional independence and quality of life. In recent years, rehabilitation strategies based on new technologies, such as MYO Armband^®, have been implemented to improve dexterity in people with upper limb impairment. Over the last few years, many studies have been published focusing on the accuracy of the MYO Armband^® to capture electromyographic and inertial data, as well as the clinical effects of using it as a rehabilitation tool in people with loss of upper limb function. Nevertheless, to our knowledge, there has been no systematic review of this subject. Methods: A systematically comprehensive literature search was conducted in order to identify original studies that answered the PICO question (patient/population, intervention, comparison, and outcome): What is the accuracy level and the clinical effects of the MYO Armband^® in people with motor impairment of the upper limb compared with other assessment techniques or interventions or no intervention whatsoever? The following data sources were used: Pubmed, Scopus, Web of Science, ScienceDirect, Physiotherapy Evidence Database, and the Cochrane Library. After identifying the eligible articles, a cross-search of their references was also completed for additional studies. The following data were extracted from the papers: study design, disease or condition, intervention, sample, dosage, outcome measures or data collection procedure and data analysis and results. The authors independently collected these data following the CONSORT 2010 statement when possible, and eventually reached a consensus on the extracted data, resolving disagreements through discussion. To assess the methodological quality of papers included, the tool for the critical appraisal of epidemiological cross-sectional studies was used, since only case series studies were identified after the search. Additionally, the articles were classified according to the levels of evidence and grades of recommendation for diagnosis studies established by the Oxford Center for Evidence-Based Medicine. Also, The Cochrane Handbook for Systematic Reviews of Interventions was used by two independent reviewers to assess risk of bias, assessing the six different domains. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed to carry out this review. Results: 10 articles with a total 180 participants were included in the review. The characteristics of included studies, sample and intervention characteristics, outcome measures, the accuracy of the system and effects of the interventions and the assessment of methodological quality of the studies and risk of bias are shown. Conclusions: Therapy with the MYO Armband^® has shown clinical changes in range of motion, dexterity, performance, functionality and satisfaction. It has also proven to be an accurate system to capture signals from the forearm muscles in people with motor impairment of the upper limb. However, further research should be conducted using bigger samples, well-defined protocols, comparing with control groups or comparing with other assessment or therapeutic tools, since the studies published so far present a high risk of bias and low level of evidence and grade of recommendation.

Effects of EMG-Controlled Video Games on the Upper Limb Functionality in Patients with Multiple Sclerosis: A Feasibility Study and Development Description

Multiple sclerosis (MS) is the most common inflammatory neurological disease in young adults, with a high prevalence worldwide (2.8 million people). To aid in the MS treatment, using VR tools in cognitive and motor rehabilitation of such disease has been growing progressively in the last years. However, the role of VR as a rehabilitative tool in MS treatment is still under debate. This paper explores the effects of VR training using EMG activation in upper limb functionality. An experimental training protocol using video games controlled using an MYO armband sensor was conducted in a sample of patients with MS. Results support the use of EMG-commanded video games as a rehabilitative tool in patients with MS, obtaining favorable outcomes related to upper limb functionality and satisfaction.

Clinical utility of a pediatric hand exoskeleton: identifying users, practicability, and acceptance, and recommendations for design improvement

BACKGROUND

Children and adolescents with upper limb impairments can experience limited bimanual performance reducing daily-life independence. We have developed a fully wearable pediatric hand exoskeleton (PEXO) to train or compensate for impaired hand function. In this study, we investigated its appropriateness, practicability, and acceptability.

METHODS

Children and adolescents aged 6-18 years with functional limitations in at least one hand due to a neurological cause were selected for this cross-sectional evaluation. We characterized participants by various clinical tests and quantified bimanual performance with the Assisting Hand Assessment (AHA). We identified children whose AHA scaled score increased by ≥ 7 points when using the hand exoskeleton and determined clinical predictors to investigate appropriateness. The time needed to don each component and the number of technical issues were recorded to evaluate practicability. For acceptability, the experiences of the patients and the therapist with PEXO were evaluated. We further noted any adverse events.

RESULTS

Eleven children (median age 11.4 years) agreed to participate, but data was available for nine participants. The median AHA scaled score was higher with PEXO (68; IQR: 59.5-83) than without (55; IQR: 37.5-80.5; p = 0.035). The Box and Block test, the Selective Control of the Upper Extremity Scale, and finger extensor muscle strength could differentiate well between those participants who improved in AHA scaled scores by ≥ 7 points and those who did not (sensitivity and specificity varied between 0.75 and 1.00). The median times needed to don the back module, the glove, and the hand module were 62, 150, and 160 s, respectively, but all participants needed assistance. The most critical failures were the robustness of the transmission system, the electronics, and the attachment system. Acceptance was generally high, particularly in participants who improved bimanual performance with PEXO. Five participants experienced some pressure points. No adverse events occurred.

CONCLUSIONS

PEXO is a safe exoskeleton that can improve bimanual hand performance in young patients with minimal hand function. PEXO receives high acceptance. We formulated recommendations to improve technical issues and the donning before such exoskeletons can be used under daily-life conditions for therapy or as an assistive device. Trial registration Not appropriate.

Evaluation of User-Prosthesis-Interfaces for sEMG-Based Multifunctional Prosthetic Hands

The complexity of the user interfaces and the operating modes present in numerous assistive devices, such as intelligent prostheses, influence patients to shed them from their daily living activities. A methodology to evaluate how diverse aspects impact the workload evoked when using an upper-limb bionic prosthesis for unilateral transradial amputees is proposed and thus able to determine how user-friendly an interface is. The evaluation process consists of adapting the same 3D-printed terminal device to the different user-prosthesis-interface schemes to facilitate running the tests and avoid any possible bias. Moreover, a study comparing the results gathered by both limb-impaired and healthy subjects was carried out to contrast the subjective opinions of both types of volunteers and determines if their reactions have a significant discrepancy, as done in several other studies.

Physiologic signaling and viability of the muscle cuff regenerative peripheral nerve interface (MC-RPNI) for intact peripheral nerves

Background. Robotic exoskeleton devices have become a promising modality for restoration of extremity function in individuals with limb loss or functional weakness. However, there exists no consistent or reliable way to record efferent motor action potentials from intact peripheral nerves to control device movement. Peripheral nerve motor action potentials are similar in amplitude to that of background noise, producing an unfavorable signal-to-noise ratio (SNR) that makes these signals difficult to detect and interpret. To address this issue, we have developed the muscle cuff regenerative peripheral nerve interface (MC-RPNI), a construct consisting of a free skeletal muscle graft wrapped circumferentially around an intact peripheral nerve. Over time, the muscle graft regenerates, and the intact nerve undergoes collateral axonal sprouting to reinnervate the muscle. The MC-RPNI amplifies efferent motor action potentials by several magnitudes, thereby increasing the SNR, allowing for higher fidelity signaling and detection of motor intention. The goal of this study was to characterize the signaling capabilities and viability of the MC-RPNI over time.Methods. Thirty-seven rats were randomly assigned to one of five experimental groups (Groups A-E). For MC-RPNI animals, their contralateral extensor digitorum longus (EDL) muscle was harvested and trimmed to either 8 mm (Group A) or 13 mm (Group B) in length, wrapped circumferentially around the intact ipsilateral common peroneal (CP) nerve, secured, and allowed to heal for 3 months. Additionally, one 8 mm (Group C) and one 13 mm (Group D) length group had an epineurial window created in the CP nerve immediately preceding MC-RPNI creation. Group E consisted of sham surgery animals. At 3 months, electrophysiologic analyses were conducted to determine the signaling capabilities of the MC-RPNI. Additionally, electromyography and isometric force analyses were performed on the CP-innervated EDL to determine the effects of the MC-RPNI on end organ function. Following evaluation, the CP nerve, MC-RPNI, and ipsilateral EDL muscle were harvested for histomorphometric analysis.Results. Study endpoint analysis was performed at 3 months post-surgery. All rats displayed visible muscle contractions in both the MC-RPNI and EDL following proximal CP nerve stimulation. Compound muscle action potentials were recorded from the MC-RPNI following proximal CP nerve stimulation and ranged from 3.67 ± 0.58 mV to 6.04 ± 1.01 mV, providing efferent motor action potential amplification of 10-20 times that of a normal physiologic nerve action potential. Maximum tetanic isometric force (F_o) testing of the distally-innervated EDL muscle in MC-RPNI groups producedF_o(2341 ± 114 mN-2832 ± 102 mN) similar to controls (2497 ± 122 mN), thus demonstrating that creation of MC-RPNIs did not adversely impact the function of the distally-innervated EDL muscle. Overall, comparison between all MC-RPNI sub-groups did not reveal any statistically significant differences in signaling capabilities or negative effects on distal-innervated muscle function as compared to the control group.Conclusions. MC-RPNIs have the capability to provide efferent motor action potential amplification from intact nerves without adversely impacting distal muscle function. Neither the size of the muscle graft nor the presence of an epineurial window in the nerve had any significant impact on the ability of the MC-RPNI to amplify efferent motor action potentials from intact nerves. These results support the potential for the MC-RPNI to serve as a biologic nerve interface to control advanced exoskeleton devices.

Defining the design requirements for an assistive powered hand exoskeleton: A pilot explorative interview study and case series

BACKGROUND

Powered hand exoskeletons are an emerging technology that have shown promise in assisting individuals with impaired hand function. A number of hand exoskeleton designs have been described in the literature; however, the majority have not been supported by patient-oriented criteria.

OBJECTIVE

The aim of this study was to define preliminary end-user needs and expectations for an assistive hand exoskeleton.

STUDY DESIGN

Explorative interview and case series.

METHODS

Six clinicians and eight individuals with impaired hand function were interviewed in small groups or individually. A standardized list of questions was used to elicit feedback on specific design criteria or promote the discovery of new criteria. In addition, three participants with impaired hand function returned for a second session where hand characteristics, such as range of motion and force required to flex/extend fingers, were recorded to further quantify design requirements.

RESULTS

Interview responses indicated that there was general consensus among participants on criteria relating to important grasp patterns, grip strength, wear time, and acceptable bulk/weight. However, interview responses and hand characteristics also revealed important differences between individuals with impaired hand function.

CONCLUSION

Qualitative and quantitative data were collected to develop an understanding of end-user design requirements for assistive hand exoskeletons. Although the data collected were helpful in identifying some preliminary criteria, differences between participants exist and identifying a universal set of criteria applicable across individuals with impaired hand function is challenging. This work reinforces the importance of involving users of rehabilitation technology in the device development process.

Automatic and Objective Assessment of Motor Skills Performance in Flexible Bronchoscopy

BACKGROUND

Motor skills have been identified as a useful measure to evaluate competency in bronchoscopy. However, no automatic assessment system of motor skills with a clear pass/fail criterion in flexible bronchoscopy exists.

OBJECTIVES

The objective of the study was to develop an objective and automatic measure of motor skills in bronchoscopy and set a pass/fail criterion.

METHODS

Participants conducted 3 bronchoscopies each in a simulated setting. They were equipped with a Myo Armband that measured lower arm movements through an inertial measurement unit, and hand and finger motions through electromyography sensors. These measures were composed into an objective and automatic composite score of motor skills, the motor bronchoscopy skills score (MoBSS).

RESULTS

Twelve novices, eleven intermediates, and ten expert bronchoscopy operators participated, resulting in 99 procedures available for assessment. MoBSS was correlated with a higher diagnostic completeness (Pearson's correlation, r = 0.43, p < 0.001) and a lower procedure time (Pearson's correlation, r = -0.90, p < 0.001). MoBSS was able to differentiate operator performance based on the experience level (one-way ANOVA, p < 0.001). Using the contrasting groups' method, a passing score of -0.08 MoBSS was defined that failed 30/36 (83%) novice, 5/33 (15%) intermediate, and 1/30 (3%) expert procedures.

CONCLUSIONS

MoBSS can be used as an automatic and unbiased assessment tool for motor skills performance in flexible bronchoscopy. MoBSS has the potential to generate automatic feedback to help guide trainees toward expert performance.

A Novel Point-in-Polygon-Based sEMG Classifier for Hand Exoskeleton Systems

In the early 2000s, data from the latest World Health Organization estimates paint a picture where one-seventh of the world population needs at least one assistive device. Fortunately, these years are also characterized by a marked technological drive which takes the name of the Fourth Industrial Revolution. In this terrain, robotics is making its way through more and more aspects of everyday life, and robotics-based assistance/rehabilitation is considered one of the most encouraging applications. Providing high-intensity rehabilitation sessions or home assistance through low-cost robotic devices can be indeed an effective solution to democratize services otherwise not accessible to everyone. However, the identification of an intuitive and reliable real-time control system does arise as one of the critical issues to unravel for this technology in order to land in homes or clinics. Intention recognition techniques from surface ElectroMyoGraphic (sEMG) signals are referred to as one of the main ways-to-go in literature. Nevertheless, even if widely studied, the implementation of such procedures to real-case scenarios is still rarely addressed. In a previous work, the development and implementation of a novel sEMG-based classification strategy to control a fully-wearable Hand Exoskeleton System (HES) have been qualitatively assessed by the authors. This paper aims to furtherly demonstrate the validity of such a classification strategy by giving quantitative evidence about the favourable comparison to some of the standard machine-learning-based methods. Real-time action, computational lightness, and suitability to embedded electronics will emerge as the major characteristics of all the investigated techniques.

User-Driven Functional Movement Training With a Wearable Hand Robot After Stroke

We studied the performance of a robotic orthosis designed to assist the paretic hand after stroke. It is wearable and fully user-controlled, serving two possible roles: as a therapeutic tool that facilitates device-mediated hand exercises to recover neuromuscular function or as an assistive device for use in everyday activities to aid functional use of the hand. We present the clinical outcomes of a pilot study designed as a feasibility test for these hypotheses. 11 chronic stroke (>2 years) patients with moderate muscle tone (Modified Ashworth Scale ≤ 2 in upper extremity) engaged in a month-long training protocol using the orthosis. Individuals were evaluated using standardized outcome measures, both with and without orthosis assistance. Fugl-Meyer post intervention scores without robotic assistance showed improvement focused specifically at the distal joints of the upper limb, suggesting the use of the orthosis as a rehabilitative device for the hand. Action Research Arm Test scores post intervention with robotic assistance showed that the device may serve an assistive role in grasping tasks. These results highlight the potential for wearable and user-driven robotic hand orthoses to extend the use and training of the affected upper limb after stroke.

Fully Wearable Actuated Soft Exoskeleton for Grasping Assistance in Everyday Activities

Worldwide, over 50 million people suffer from persistent hand impairments after stroke or spinal cord injury (SCI). This results in major loss of independence and quality of life. Robotic hand exoskeletons can compensate for lost motor function and assist in grasping tasks performed in everyday activities. Several recent prototypes can partially provide this assistance. However, it remains challenging to integrate the dexterity required for daily tasks in a safe and user-friendly design that is acceptable for daily use in subjects with neuromotor hand impairments. We present the design of RELab tenoexo; a fully wearable assistive soft hand exoskeleton for daily activities. We present sleek mechanisms for a hand module that generates the four most frequently used grasp types, employing a remote actuation system that reduces weight on the hand. For optimal assistance and highest adaptability, we present various design and control options to customize the modular device, along with an automated tailoring algorithm that allows automatically generated hand modules for individual users. Mechanical evaluation shows that RELab tenoexo covers the range of motion and the fingertip forces required to assist users in up to 80% of all grasping activities. In user tests, we find that the low weight, unintrusive size, high wearing comfort, and appealing appearance are beneficial for user acceptance and usability in daily life. Finally, we demonstrate that RELab tenoexo leads to an immediate improvement of the functional grasping ability in a subject with SCI.

A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning

This paper presents a real-time fine gesture recognition system for multi-articulating hand prosthesis control, using an embedded convolutional neural network (CNN) to classify hand-muscle contractions sensed at the forearm. The sensor consists in a custom non-intrusive, compact, and easy-to-install 32-channel high-density surface electromyography (HDsEMG) electrode array, built on a flexible printed circuit board (PCB) to allow wrapping around the forearm. The sensor provides a low-noise digitization interface with wireless data transmission through an industrial, scientific and medical (ISM) radio link. An original frequency-time-space cross-domain preprocessing method is proposed to enhance gesture-specific data homogeneity and generate reliable muscle activation maps, leading to 98.15% accuracy when using a majority vote over 5 subsequent inferences by the proposed CNN. The obtained real-time gesture recognition, within 100 to 200 ms, and CNN properties show reliable and promising results to improve on the state-of-the-art of commercial hand prostheses. Moreover, edge computing using a specialized embedded artificial intelligence (AI) platform ensures reliable, secure and low latency real-time operation as well as quick and easy access to training, fine-tuning and calibration of the neural network. Co-design of the signal processing, AI algorithms and sensing hardware ensures a reliable and power-efficient embedded gesture recognition system.

Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.

PEXO - A Pediatric Whole Hand Exoskeleton for Grasping Assistance in Task-Oriented Training

Children with hand motor impairment due to cerebral palsy, traumatic brain injury, or pediatric stroke are considerably affected in their independence, development, and quality of life. Treatment conventionally includes task-oriented training in occupational therapy. While dose and intensity of hand therapy can be promoted through technology, these approaches are mostly limited to large stationary robotic devices for non-task-oriented training, or passive wearable devices for children with mild impairments. Here we present PEXO, a fully wearable actuated pediatric hand exoskeleton to cover the special needs of children (6 to 12 years of age) with strong impairments in hand function. Through three degrees of freedom, PEXO provides assistance in various grasp types needed for the execution of functional tasks. It is lightweight, water proof, and inherently interacts safely with the user. It meets mechanical requirements such as force, fast closing movement, and battery lifetime derived from literature and discussions with clinicians. Appealing appearance, user-friendly design, and intuitive control with visual feedback of forearm muscle activity should keep the user motivated during training in the clinic or at home. A pilot test with a 6-years old child with stroke showed that PEXO can provide assistance in grasping various objects weighing up to 0.5 kg. These are promising first results on the way to make hand exoskeletons accessible for children with neuromotor disorders.