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Abstract Background Children with unilateral cerebral palsy (CP) exhibit motor impairments predominantly on one side of the body, while also having ipsilesional and bilateral impairments. These impairments are known to persist through adulthood, but their extent have not been described in adults with CP. This study’s aim is to characterize bilateral and unilateral upper limbs impairments in adults with CP. Methods Nineteen adults with CP (34.3 years old ± 11.5) performed three robotic assessments in the Kinarm Exoskeleton Lab, including two bilateral tasks (Object Hit [asymmetric independent goals task] and Ball on Bar [symmetric common goal task]) and one unilateral task (Visually Guided Reaching, performed with the more affected arm [MA] and less affected arm [LA]). Individual results were compared to sex, age and handedness matched normative data, describing the proportion of participants exhibiting impairments in each task-specific variable (e.g., Hand speed), each performance category (e.g., Feedforward control) and in global task performance. Associations were assessed using Spearman correlation coefficients between: 1: the results of the MA and LA of each limb in the unilateral task; and 2: the results of each limb in the unilateral vs. the bilateral tasks. Results The majority of participants exhibited impairments in bilateral tasks (84%). The bilateral performance categories (i.e., Bimanual) identifying bilateral coordination impairments were impaired in the majority of participants (Object Hit: 57.8%; Ball on Bar: 31.6%). Most of the participants were impaired when performing a unilateral task with their MA arm (63%) and a smaller proportion with their LA arm (31%). The Feedforward control was the unilateral performance category showing the highest proportion of impaired participants while displaying the strongest relationship between the MA and LA arms impairments (rs = 0.93). Feedback control was the unilateral performance category most often associated with impairments in bilateral tasks (6 out of 8 performance categories). Conclusions Adults with CP experienced more impairment in bilateral tasks while still having substantial impairments in unilateral tasks. They frequently display Feedforward control impairments combined with a higher reliance on Feedback control during both bilateral and unilateral tasks, leading to poorer motor performance.

IntroductionChildren with cerebral palsy (CP) exhibit a variety of sensory impairments that can interfere with motor performance, but how these impairments persist into adulthood needs further investigation. The objective of this study was to describe the sensory impairments in adults having CP and how they relate to motor impairments.MethodsNineteen adults having CP performed a set of robotic and clinical assessments. These assessments were targeting different sensory functions and motor functions (bilateral and unilateral tasks). Frequency of each type of impairments was determined by comparing individual results to normative data. Association between the sensory and motor impairments was assessed with Spearman correlation coefficient.ResultsImpairment in stereognosis was the most frequent, affecting 57.9% of participants. Although less frequently impaired (26.3%), tactile discrimination was associated with all the motor tasks (unilateral and bilateral, either robotic or clinical). Performance in robotic motor assessments was more frequently associated with sensory impairments than with clinical assessments. Finally, sensory impairments were not more closely associated with bilateral tasks than with unilateral tasks.DiscussionSomatosensory and visuo-perceptual impairments are frequent among adults with CP, with 84.2% showing impairments in at least one sensory function. These sensory impairments show a moderate association with motor impairments.

BackgroundFatigue impacts motor performance and upper limb kinematics. It is of interest to study whether it is possible to minimize the potentially detrimental effects of fatigue with prevention programs.ObjectiveTo determine the effect of task-specific training on upper limb kinematics and motor performance when reaching in a fatigued state.MethodsThirty healthy participants were recruited (Training group n = 15; Control group n = 15). Both groups took part in two evaluation sessions (Day 1 and Day 5) during which they performed a reaching task (as quickly and accurately as possible) in two conditions (rested and fatigued). During the reaching task, joint kinematics and motor performance (accuracy and speed) were evaluated. The Training group participated in three task-specific training sessions between Day 1 and Day 5; they trained once a day, for three days. The Control group did not perform any training. A three-way non-parametric ANOVA for repeated measures (Nonparametric Analysis of Longitudinal Data; NparLD) was used to assess the impact of the training (Condition [within subject]: rested, fatigued; Day [within subject]: Day 1 vs. Day 5 and Group [between subjects]: Training vs. Control).ResultsAfter the training period, the Training group significantly improved their reaching speed compared to the Control group (Day x Group p < .01; Time effect: Training group = p < .01, Control group p = .20). No between-group difference was observed with respect to accuracy. The Training group showed a reduction in contralateral trunk rotation and lateral trunk flexion in Day 2 under the fatigue condition (Group x Day p < .04; Time effect: Training group = p < .01, Control group = p < .59).ConclusionAfter the 3-day training, participants demonstrated improved speed and reduced reliance on trunk compensations to complete the task under fatigue conditions. Task-specific training could help minimizing some effects of fatigue.

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP). Methods: A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150). Results: In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75). Conclusions: The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use.

Introduction Approximately 33% of people who contracted COVID-19 still experience symptoms 12 weeks after infection onset. This persistence of symptoms is now considered a syndrome itself called ‘long COVID’. Evidence regarding long COVID and its cognitive and physical impacts is growing, but the literature is currently lacking objectively measured data to guide towards adapted healthcare trajectories. The objectives are to describe the physical and cognitive impairments experienced by individuals living with long COVID using self-reported and clinical objective measures, and to compare the evolution over time of the physical and cognitive state between adults living with long COVID (at least one physical or cognitive COVID-19 symptom for more than 12 weeks following infection; long COVID group), people who developed COVID-19 but did not experience persistent symptoms (short COVID group) and people who did not develop COVID-19 (control group).Methods and analysis In this longitudinal cohort study, 120 participants will be recruited in each group. Variables will be collected through three evaluation sessions over 6 months (baseline, 3 months, 6 months). Variables include self-administered questionnaires on health-related quality of life, comorbidity, sleep, pain, anxiety, depressive symptoms, fatigue and cognitive function, as well as objective measures of cognitive (attention, memory, executive functioning) and physical (grip strength, balance, gait speed, gait endurance, VO2, frailty) functions. Activity, heart rate and sleep will be monitored with a fitness tracker watch for 7 days following evaluation sessions. Maximum-likelihood analyses of variance (ANOVAs) will be used to compare data at baseline between groups. Repeated measures ANOVAs will be used to compare the longitudinal performance variations across groups of the self-reported and clinical variables.Ethics and dissemination Ethics committees of the CIUSSS de la Capitale-Nationale and CIUSSS de l’Est-de-l’Île-de-Montréal approved the project. Results will be disseminated through clinical and community platforms as well as through peer-reviewed manuscripts and international conferences.Trial registration number NCT05216536.

Physical disabilities significantly impact individuals' ability to perform activities of daily living (ADL), leading to reduced autonomy and difficulty to accomplish daily living tasks. One such barrier is the difficulty or inability to open doors, preventing access to different rooms in their residence (bathroom, bedroom, etc.), rooms at work, or shopping areas. Existing solutions, such as robotic arms on wheelchairs and door openers mounted at the top of doors, remain expensive, complex to install, and relatively difficult for the target population to use. Addressing this challenge, this study introduces a Robotic Wheel Door Opener System (RWDOS) designed to facilitate the opening and closing of the door. The RWDOS is installed at the bottom of a door and is controlled remotely through a smartphone application. The paper presents the mechanical design and control system along with a cost analysis. It concludes with initial results and outlines the future directions for the project. [ABSTRACT FROM AUTHOR]

Assistive technologies (ATs) often have a high-dimensionality of possible movements (e.g., assistive robot with several degrees of freedom or a computer), but the users have to control them with low-dimensionality sensors and interfaces (e.g., switches). This paper presents the development of an open-source interface based on a sequence-matching algorithm for the control of ATs. Sequence matching allows the user to input several different commands with low-dimensionality sensors by not only recognizing their output, but also their sequential pattern through time, similarly to Morse code. In this paper, the algorithm is applied to the recognition of hand gestures, inputted using an inertial measurement unit worn by the user. An SVM-based algorithm, that is aimed to be robust, with small training sets (e.g., five examples per class) is developed to recognize gestures in real-time. Finally, the interface is applied to control a computer’s mouse and keyboard. The interface was compared against (and combined with) the head movement-based AssystMouse software. The hand gesture interface showed encouraging results for this application but could also be used with other body parts (e.g., head and feet) and could control various ATs (e.g., assistive robotic arm and prosthesis).

Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces such as joysticks that tend to be unintuitive and cumbersome to use. In contrast, vocal commands may represent a viable and intuitive alternative. This work represents an important step toward providing a viable vocal interface for people living with upper limb disabilities by proposing a novel lightweight vocal command recognition system. The proposed model leverages the MobileNet2 architecture, augmenting it with a novel approach to the self-attention mechanism, achieving a new state-of-the-art performance for Keyword Spotting (KWS) on the Google Speech Commands Dataset (GSCD). Moreover, this work presents a new dataset, referred to as the French Speech Commands Dataset (FSCD), comprising 4963 vocal command utterances. Using the GSCD as the source, we used Transfer Learning (TL) to adapt the model to this cross-language task. TL has been shown to significantly improve the model performance on the FSCD. The viability of the proposed approach is further demonstrated through real-life control of a robotic arm by four healthy participants using both the proposed vocal interface and a joystick.

Abstract Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.

Introduction: Children with cerebral palsy (CP) exhibit a variety of sensory impairments that can interfere with motor performance, but how these impairments persist into adulthood needs further investigation. The objective of this study was to describe the sensory impairments in adults having CP and how they relate to motor impairments. Methods: Nineteen adults having CP performed a set of robotic and clinical assessments. These assessments were targeting different sensory functions and motor functions (bilateral and unilateral tasks). Frequency of each type of impairments was determined by comparing individual results to normative data. Association between the sensory and motor impairments was assessed with Spearman correlation coefficient. Results: Impairment in stereognosis was the most frequent, affecting 57.9% of participants. Although less frequently impaired (26.3%), tactile discrimination was associated with all the motor tasks (unilateral and bilateral, either robotic or clinical). Performance in robotic motor assessments was more frequently associated with sensory impairments than with clinical assessments. Finally, sensory impairments were not more closely associated with bilateral tasks than with unilateral tasks. Discussion: Somatosensory and visuo-perceptual impairments are frequent among adults with CP, with 84.2% showing impairments in at least one sensory function. These sensory impairments show a moderate association with motor impairments. [ABSTRACT FROM AUTHOR]

Developmental coordination disorder (DCD) impairs motor skills in children, particularly handwriting, which is a significant part of school activities and personal development. Current robotic assistants often lack a fully physical interaction or tangible result, thereby limiting their effectiveness in coordination development similar to handwriting. To address this gap, a robotic assistant capable of physical input and output could enhance development. This paper presents the development of a robotic handwriting assistant to aid children with DCD. The prototype is a planar robot controlled via a joystick capable of covering an 8.5" by 11" paper sheet. The paper provides insight into the causal real-world context of the prototype's development and discusses the designs considerations, the conception and the control. It concludes with intial results and the future for the project. [ABSTRACT FROM AUTHOR]

Work-related upper extremity disorders are costly to society due to resulting medical costs, presenteeism and absenteeism. Although their aetiology is likely multifactorial, physical workplace factors are known to play an important role in their development. Promising options for preventing work-related upper extremity disorders include assistive technologies such as dynamic arm supports designed to follow the movement of the arm while compensating for its weight. The objective of this study was to assess the effects of a dynamic arm support on perceived exertion, muscle activity and movement patterns of the upper limb during repetitive manual tasks in healthy individuals. Thirty healthy right-handed individuals were allocated either a static or a dynamic task to perform with and without a dynamic arm support. During the task, surface electromyographic activity (anterior and middle deltoid, upper trapezius) and upper limb kinematics (elbow, shoulder, sternoclavicular) were measured using surface EMG and inertial sensors. Results showed that the dynamic arm support significantly reduced perceived exertion during the tasks and limited the development of muscular fatigue of the anterior and middle deltoid as demonstrated by EMG signal mean epoch amplitudes and median frequency of the EMG power spectrum. The dynamic arm support also prevented a decrease in shoulder elevation and an increase in total shoulder joint excursion during static and dynamic task, respectively. These results denote the potential benefits of dynamic arm supports in work environments. Further studies should focus on their efficacy, acceptability and implementability in work settings.

Abstract Background People with chronic shoulder pain have been shown to present with motor adaptations during arm movements. These adaptations may create abnormal physical stress on shoulder tendons and muscles. However, how and why these adaptations develop from the acute stage of pain is still not well‐understood. Objective To investigate motor adaptations following acute experimental shoulder pain during upper limb reaching. Methods Forty participants were assigned to the Control or Pain group. They completed a task consisting of reaching targets in a virtual reality environment at three time points: (1) baseline (both groups pain‐free), (2) experimental phase (Pain group experiencing acute shoulder pain induced by injecting hypertonic saline into subacromial space), and (3) Post experimental phase (both groups pain‐free). Electromyographic (EMG) activity, kinematics, and performance data were collected. Results The Pain group showed altered movement planning and execution as shown by a significant increased delay to reach muscles EMG peak and a loss of accuracy, compared to controls that have decreased their mean delay to reach muscles peak and improved their movement speed through the phases. The Pain group also showed protective kinematic adaptations using less shoulder elevation and elbow flexion, which persisted when they no longer felt the experimental pain. Conclusion Acute experimental pain altered movement planning and execution, which affected task performance. Kinematic data also suggest that such adaptations may persist over time, which could explain those observed in chronic pain populations.

Assistive devices are designed to enhance individuals with disabilities' functional abilities. The rise of 3D printing technology enabled the production of individualized assistive devices (IADs). A REHAB-LAB is intended for IAD provision involving technical referents and occupational therapists. This study aimed to develop the REHAB-LAB logic model; to explore its fidelity and desirability; and to explore the characteristics of arising initiatives of IAD production. The REHAB-LAB logic model development involved stakeholders throughout the research process. A pragmatic multimethod approach followed two phases 1) logic model development and 2) exploration of its fidelity and desirability. The REHAB-LAB logic model presented the resources (equipment, space, human) required to implement IAD provision in a rehabilitation center, and the expected deliverables (activities and outputs). The REHAB-LAB logic model highlights the interdisciplinarity of IAD provision including occupational therapists, doctors, engineers, managers, and technical referents and places the users at the center of the IAD production. Results confirmed the fidelity and desirability of the REHAB-LAB logic model. The REHAB-LAB logic model can be used as a reference for future healthcare organizations wishing to implement an IAD provision. This research highlighted the interest of IAD provision based on the REHAB-LAB model involving users and transdisciplinary practices [ABSTRACT FROM AUTHOR]

Adults living with cerebral palsy (CP) report bimanual and unimanual difficulties that interfere with their participation in activities of daily living (ADL). There is a lack of quantitative methods to assess the impact of these motor dysfunctions on the relative use of each arm. The objective of this study was to evaluate the concurrent and discriminative validity of accelerometry-based metrics when used to assess bimanual and unimanual functions. Methods: A group of control subjects and hemiplegic adults living with CP performed six ADL tasks, during which they were wearing an Actigraph GT9X on each wrist and being filmed. Four bimanual and unimanual metrics were calculated from both accelerometry-based and video-based data; these metrics were then compared to one other with an intraclass correlation coefficient (ICC). Some of these metrics were previously validated in other clinical population, while others were novel. The discriminative validity was assessed through comparisons between groups and between tasks. Results: The concurrent validity was considered as good to excellent (ICC = 0.61–0.97) depending on the experience of the raters. The tasks made it possible to discriminate between groups. Conclusion: The proposed accelerometry-based metrics are a promising tool to evaluate bimanual and unimanual functions in adults living with CP.

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP). Methods: A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150). Results: In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75). Conclusions: The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use. [ABSTRACT FROM AUTHOR]

Dynamic arm supports (DAS) can assist individuals with severe upper limb disabilities who use a wheelchair to accomplish their daily activities. The objective of this case series was to assess the potential of a DAS to improve power wheelchair (PWC) control in real-life contexts and to describe the integration process. A secondary objective was to explore factors that may influence the DAS integration process. This case series includes four participants fitted with the Kinova DAS O110. A one-year follow-up was planned for each participant with qualitative and quantitative data collection to assess DAS outcomes. Assessment methods were selected to cover the three vantages of assistive technology outcomes assessment: effectiveness, subjective well-being, and social significance. Among the four participants, one used the DAS to help with his wheelchair control for at least 12 months (successful integration). For him, the DAS led to significantly improved wheelchair skills and an important positive psychosocial impact. He was, however, only able to use the DAS for PWC control (no effects on other daily activities), and could not use it in some contexts because the device increased his PWC width. As for the other participants, they stopped using the DAS for different reasons, including a DAS-related adverse event. This study demonstrated that a wheelchair-bound DAS has the potential to improve PWC use, but successful integration requires specific factors. The service delivery process and the environmental accessibility are crucial for the successful integration of such a device and to avoid safety issues. No study deeply assessed the impacts of dynamic arm support on power wheelchair control, and the associated facilitators and obstacles. A wheelchair-bound dynamic arm support has the potential to improve power wheelchair control for individuals with upper limb disabilities and to lead to positive psychosocial impacts if some conditions conductive to successful integration are met. The service delivery process and the environmental accessibility appear as crucial aspects for the successful integration of a new device, such as dynamic arm support and avoiding safety issues. [ABSTRACT FROM AUTHOR]

Background: Fatigue impacts motor performance and upper limb kinematics. It is of interest to study whether it is possible to minimize the potentially detrimental effects of fatigue with prevention programs. Objective: To determine the effect of task-specific training on upper limb kinematics and motor performance when reaching in a fatigued state. Methods: Thirty healthy participants were recruited (Training group n = 15; Control group n = 15). Both groups took part in two evaluation sessions (Day 1 and Day 5) during which they performed a reaching task (as quickly and accurately as possible) in two conditions (rested and fatigued). During the reaching task, joint kinematics and motor performance (accuracy and speed) were evaluated. The Training group participated in three task-specific training sessions between Day 1 and Day 5; they trained once a day, for three days. The Control group did not perform any training. A three-way non-parametric ANOVA for repeated measures (Nonparametric Analysis of Longitudinal Data; NparLD) was used to assess the impact of the training (Condition [within subject]: rested, fatigued; Day [within subject]: Day 1 vs. Day 5 and Group [between subjects]: Training vs. Control). Results: After the training period, the Training group significantly improved their reaching speed compared to the Control group (Day x Group p <.01; Time effect: Training group = p <.01, Control group p =.20). No between-group difference was observed with respect to accuracy. The Training group showed a reduction in contralateral trunk rotation and lateral trunk flexion in Day 2 under the fatigue condition (Group x Day p <.04; Time effect: Training group = p <.01, Control group = p <.59). Conclusion: After the 3-day training, participants demonstrated improved speed and reduced reliance on trunk compensations to complete the task under fatigue conditions. Task-specific training could help minimizing some effects of fatigue. [ABSTRACT FROM AUTHOR]

People living with cerebral palsy (CP) exhibit motor and sensory impairments that affect unimanual and bimanual functions. The importance of sensory functions for motor control is well known, but the association between motor and sensory functions remains unclear in people living with CP. The objective of this systematic review was to characterize the relationship between sensory deficits and upper limb motor function in individuals living with CP. Methods: Five databases were screened. The inclusion criteria were: (1) including people living with CP, (2) reporting measurements of upper limb motor and sensory functions. A qualitative analysis of the studies’ level of evidence was done. Results: Thirty-three articles were included. Twenty-five articles evaluated tactile functions, 10 proprioceptive functions and 7 visual functions; 31 of the articles reported on unimanual functions and 17 of them reported on bimanual functions. Tactile functions showed a moderate to high association; it was not possible to reach definitive conclusions for proprioceptive and visual functions. Conclusions: The heterogeneity of the results limits the ability to draw definitive conclusions. Further studies should aim to perform more comprehensive assessments of motor and sensory functions, to determine the relative contribution of various sensory modalities to simple and more complex motor functions.

Background: A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms for pathological populations. The objectives of this research are to design and validate open-source algorithms for activity intensity quantification and classification. Methods: Two versions of a quantification algorithm are proposed (fixed [FB] and modifiable bandwidth [MB]) along with two versions of a classification algorithm (discrete [DM] vs. continuous methods [CM]). The results of these algorithms were compared to those of a commercial activity intensity count solution (ActiLife) with datasets from four activities (n = 24 participants). Results: The FB and MB algorithms gave similar results as ActiLife (r > 0.96). The DM algorithm is similar to a ActiLife (r ≥ 0.99). The CM algorithm differs (r ≥ 0.89) but is more precise. Conclusion: The combination of the FB algorithm with the DM results is a solution close to that of ActiLife. However, the MB version remains valid while being more adaptable, and the CM is more precise. This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions.

Inertial measurement units have recently shown great potential for the accurate measurement of joint angle movements in replacement of motion capture systems. In the race towards long duration tracking, inertial measurement units increasingly aim to ensure portability and long battery life, allowing improved ecological studies. Their main advantage over laboratory grade equipment is their usability in a wider range of environment for greater ecological value. For accurate and useful measurements, these types of sensors require a robust orientation estimation that remains accurate over long periods of time. To this end, we developed the Allumo software for the preprocessing and calibration of the orientation estimate of triaxial accelerometers. This software has an automatic orientation calibration procedure, an automatic erroneous orientation-estimate detection and useful visualization to help process long and short measurement periods. These automatic procedures are detailed in this paper, and two case studies are presented to showcase the usefulness of the software. The Allumo software is open-source and available online.

Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces such as joysticks that tend to be unintuitive and cumbersome to use. In contrast, vocal commands may represent a viable and intuitive alternative. This work represents an important step toward providing a viable vocal interface for people living with upper limb disabilities by proposing a novel lightweight vocal command recognition system. The proposed model leverages the MobileNet2 architecture, augmenting it with a novel approach to the self-attention mechanism, achieving a new state-of-the-art performance for Keyword Spotting (KWS) on the Google Speech Commands Dataset (GSCD). Moreover, this work presents a new dataset, referred to as the French Speech Commands Dataset (FSCD), comprising 4963 vocal command utterances. Using the GSCD as the source, we used Transfer Learning (TL) to adapt the model to this cross-language task. TL has been shown to significantly improve the model performance on the FSCD. The viability of the proposed approach is further demonstrated through real-life control of a robotic arm by four healthy participants using both the proposed vocal interface and a joystick. [ABSTRACT FROM AUTHOR]

Physical human–robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this article aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: (1) a statistical analysis of a sensor signal (force and velocity) and (2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations.

Background: Workplace adaptation is the preferred method of intervention to diminish risk factors associated with the development of work-related shoulder disorders. However, the majority of the workplace assessments performed are subjective (e.g., questionnaires). Quantitative assessments are required to support workplace adaptations. The aims of this study are to assess the concurrent validity of inertial measurement units (IMUs; MVN, Xsens) in comparison to a motion capture system (Vicon) during lifting tasks, and establish the discriminative validity of a wireless electromyography (EMG) system for the evaluation of muscle activity. Methods: Sixteen participants performed 12 simple tasks (shoulder flexion, abduction, scaption) and 16 complex lifting tasks (lifting crates of different weights at different heights). A Delsys Trigno EMG system was used to record anterior and middle deltoids’ EMG activity, while the Xsens and Vicon simultaneously recorded shoulder kinematics. Results: For IMUs, correlation coefficients were high (simple task: >0.968; complex task: >0.84) and RMSEs were low (simple task: Conclusions: These results suggest that wireless EMG and IMUs are valid units that can be used to measure physical demand in workplace assessments.

Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made inertial measurement sensors (M/IMU) are potentially good alternatives to the laboratory-based systems, and recent technology improvements required a synthesis of the current evidence. The aim of this systematic review was to determine the criterion validity and reliability of M/IMU for each body joint and for tasks of different levels of complexity. Five different databases were screened (Pubmed, Cinhal, Embase, Ergonomic abstract, and Compendex). Two evaluators performed independent selection, quality assessment (consensus-based standards for the selection of health measurement instruments [COSMIN] and quality appraisal tools), and data extraction. Forty-two studies were included. Reported validity varied according to task complexity (higher validity for simple tasks) and the joint evaluated (better validity for lower limb joints). More studies on reliability are needed to make stronger conclusions, as the number of studies addressing this psychometric property was limited. M/IMU should be considered as a valid tool to assess whole body range of motion, but further studies are needed to standardize technical procedures to obtain more accurate data.

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application. [ABSTRACT FROM AUTHOR]

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload.

A limit in the development of preventive interventions for musculoskeletal disorders (MSDs) of the upper limb is the lack of devices that can measure and process surface electromyography (sEMG) signals in order to provide real-time, reliable information on muscular fatigue in relation to the physical demands of the work being carried out. To keep track of the muscle fatigue level, the chosen fatigue indicator (FI) must not be sensitive to arm orientation (i.e., elevation angle and plane). The objective of this article is, therefore, to develop a calibration and adjustment algorithm to provide an FI that is not sensitive to arm orientation. First, the effect of arm orientation on our former FI indicator was verified by comparing initial (beginning of the task) FIs (IFIs) for static contractions in different arm elevation planes and angles. The results confirmed the hypothesis. A personalized calibration and FI adjustment based on current arm orientation were then proposed. The adjustment module’s efficiency was first verified for different elevation planes and elevation angles separately. Using paired t-tests, we compared the IFI deviations from the baseline with and without the adjustment module. It was shown that the proposed method significantly reduces the IFI deviations from the baseline for both the elevation plane (${p}=0.005, {d}=1.220$) and the elevation angle (${p}=0.002, {d}=1.455$). Finally, a combined adjustment is presented for both the elevation plane and angle at the same time. The IFI deviations from the baseline were statistically significantly lower with the adjustment module than without it (${p}< 0.001, {d}=1.461$). [ABSTRACT FROM AUTHOR]

Work-related upper extremity disorders are costly to society due to resulting medical costs, presenteeism and absenteeism. Although their aetiology is likely multifactorial, physical workplace factors are known to play an important role in their development. Promising options for preventing work-related upper extremity disorders include assistive technologies such as dynamic arm supports designed to follow the movement of the arm while compensating for its weight. The objective of this study was to assess the effects of a dynamic arm support on perceived exertion, muscle activity and movement patterns of the upper limb during repetitive manual tasks in healthy individuals. Thirty healthy right-handed individuals were allocated either a static or a dynamic task to perform with and without a dynamic arm support. During the task, surface electromyographic activity (anterior and middle deltoid, upper trapezius) and upper limb kinematics (elbow, shoulder, sternoclavicular) were measured using surface EMG and inertial sensors. Results showed that the dynamic arm support significantly reduced perceived exertion during the tasks and limited the development of muscular fatigue of the anterior and middle deltoid as demonstrated by EMG signal mean epoch amplitudes and median frequency of the EMG power spectrum. The dynamic arm support also prevented a decrease in shoulder elevation and an increase in total shoulder joint excursion during static and dynamic task, respectively. These results denote the potential benefits of dynamic arm supports in work environments. Further studies should focus on their efficacy, acceptability and implementability in work settings. [ABSTRACT FROM AUTHOR]

Many people living with neurological disorders, such as cerebral palsy, stroke, muscular dystrophy or dystonia, experience upper limb impairments (muscle spasticity, loss of selective motor control, muscle weakness or tremors) and are unable to eat independently. This article presents the development of a new device to assist with eating, aimed at stabilizing the movement of people who have movement disorders. The design was guided by insights gathered through focus groups, with occupational therapists and engineers, about the challenges faced by individuals who have movement disorders and difficulty in eating autonomously. The proposed assistive device prototype is designed to be fixed on a table and to support a spoon. The mechanism is designed so that the spoon maintains a position parallel to the ground for the user. Dampers and inertia allow stabilizing the user's motion. A preliminary trial with five individuals living with cerebral palsy is presented to assess the prototype's performance and to guide future iterations of the prototype. Task completion time generally decreased and movement fluidity generally improved when using the assistive device prototype. The prototype showed good potential in stabilizing the spoon for the user and improving movement fluidity. [ABSTRACT FROM AUTHOR]

Background: People with chronic shoulder pain have been shown to present with motor adaptations during arm movements. These adaptations may create abnormal physical stress on shoulder tendons and muscles. However, how and why these adaptations develop from the acute stage of pain is still not well‐understood. Objective: To investigate motor adaptations following acute experimental shoulder pain during upper limb reaching. Methods: Forty participants were assigned to the Control or Pain group. They completed a task consisting of reaching targets in a virtual reality environment at three time points: (1) baseline (both groups pain‐free), (2) experimental phase (Pain group experiencing acute shoulder pain induced by injecting hypertonic saline into subacromial space), and (3) Post experimental phase (both groups pain‐free). Electromyographic (EMG) activity, kinematics, and performance data were collected. Results: The Pain group showed altered movement planning and execution as shown by a significant increased delay to reach muscles EMG peak and a loss of accuracy, compared to controls that have decreased their mean delay to reach muscles peak and improved their movement speed through the phases. The Pain group also showed protective kinematic adaptations using less shoulder elevation and elbow flexion, which persisted when they no longer felt the experimental pain. Conclusion: Acute experimental pain altered movement planning and execution, which affected task performance. Kinematic data also suggest that such adaptations may persist over time, which could explain those observed in chronic pain populations. [ABSTRACT FROM AUTHOR]

Dynamic arm supports can be used to increase the autonomy of people with upper limb disabilities, but their usability is often poorly documented. The objective of this study is to evaluate the usability of an actuated arm support (AAS), namely the Gowing power-assisted arm support. Nine participants with neurological disorders restricting their upper limb capacities (DASH = 63.51 ± 7.72) completed various tasks (Upper Extremity Performance Test for the Elderly (TEMPA)) with and without the AAS. Users' satisfaction (Quebec User Evaluation of Satisfaction with assistive Technology (QUEST)) and perceived benefits of the device (semi-structured interviews) were assessed. Large (effect size ≥ 1.15) and statistically significant (p <.05) improvements were found in the TEMPA functional rating, range of motion, strength, precision of gross movements and prehension patterns subscales while using the AAS. Two third of the participants were quite or very satisfied with the arm support (QUEST > 4/5) and interviews were positive about its usefulness in daily living activities. Our study demonstrated that the use of an AAS could result in significant improvements in the autonomy of people with upper limb disabilities. [ABSTRACT FROM AUTHOR]

People with upper body disabilities may be limited in their activities of daily living. Robotic arms, such as JACO, are assistive devices that could improve their abilities, independent living, and social participation. However, performing complex tasks with JACO can be time-consuming or tedious. Therefore, some advanced functionalities have been developed to enhance the performance of users. The main objective of this study is to evaluate the performance, in terms of ease of use, task completion time, and participants' perception of usability, of three new algorithms applied to the JACO robotic arm: (1) predefined position, (2) fluidity filter, and (3) drinking mode. The secondary objective is to evaluate differences in performance variables between proportional and non-proportional control modes. Fourteen participants with upper body disabilities completed various tasks with and without these functionalities. Using JACO with the algorithms led to a significant decrease of up to 72% in task completion time and improvements of 2.3 and 2.9 on a 7-point Likert scale for perceived ease of use and usability, respectively. There was no significant difference between control modes. Our results demonstrate that algorithms could produce significant improvements in performing daily living activities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Robotic devices can be leveraged to raise the abilities of humans to perform demanding and complex tasks with less effort. Although the first priority of such human–robot interaction (HRI) is safety, robotic devices must also be intuitive and efficient in order to be adopted by a broad range of users. One challenge in the control of such assistive robots is the management of the end-effector orientation, that is not always intuitive for the human operator, especially for neophytes. This paper presents a novel orientation control algorithm designed for robotic arms in the context of HRI. This paper aims at making the control of the robot's orientation easier and more intuitive for the user, both in the fields of rehabilitation (in particular individuals living with upper limb disabilities) and industrial robotics. The performance and intuitiveness of the proposed orientation control algorithm is assessed and improved through two experiments with a JACO assistive robot with 25 able-bodied subjects, an online survey with 117 respondents via the Amazon Mechanical Turk and through two experiments with a UR5 industrial robot with 12 able-bodied subjects. [ABSTRACT FROM AUTHOR]

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper’s hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time. [ABSTRACT FROM AUTHOR]

This paper describes a multimodal body–machine interface (BoMI) to help individuals with upper-limb disabilities using advanced assistive technologies, such as robotic arms. The proposed system uses a wearable and wireless body sensor network (WBSN) supporting up to six sensor nodes to measure the natural upper-body gesture of the users and translate it into control commands. Natural gesture of the head and upper-body parts, as well as muscular activity, are measured using inertial measurement units (IMUs) and surface electromyography (sEMG) using custom-designed multimodal wireless sensor nodes. An IMU sensing node is attached to a headset worn by the user. It has a size of 2.9 cm $\times$ 2.9 cm, a maximum power consumption of 31 mW, and provides angular precision of 1 $^\circ$. Multimodal patch sensor nodes, including both IMU and sEMG sensing modalities are placed over the user able-body parts to measure the motion and muscular activity. These nodes have a size of 2.5 cm $\times$ 4.0 cm and a maximum power consumption of 11 mW. The proposed BoMI runs on a Raspberry Pi. It can adapt to several types of users through different control scenarios using the head and shoulder motion, as well as muscular activity, and provides a power autonomy of up to 24 h. JACO, a 6-DoF assistive robotic arm, is used as a testbed to evaluate the performance of the proposed BoMI. Ten able-bodied subjects performed ADLs while operating the AT device, using the Test d’Évaluation des Membres Supérieurs de Personnes Âgées to evaluate and compare the proposed BoMI with the conventional joystick controller. It is shown that the users can perform all tasks with the proposed BoMI, almost as fast as with the joystick controller, with only 30% time overhead on average, while being potentially more accessible to the upper-body disabled who cannot use the conventional joystick controller. Tests show that control performance with the proposed BoMI improved by up to 17% on average, after three trials. [ABSTRACT FROM AUTHOR]

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO. [ABSTRACT FROM AUTHOR]

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human-robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload. [ABSTRACT FROM AUTHOR]

This paper presents a cable-suspended crane system to assist operators in moving and lifting large payloads. The main objective of this work is to develop a simple and reliable system to help operators in industry to be more productive while preventing injuries. The system is based on the development of a precise and reliable cable angle sensor and a complete dynamic model of the system. Adaptive horizontal and vertical controllers designed for direct physical human–robot interaction are then proposed. Different techniques are then proposed to estimate the payload acceleration in order to increase the controller performances. Finally, experiments performed on a full-scale industrial system are presented. [ABSTRACT FROM PUBLISHER]