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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.