Optimizing Epoch Length and Activity Count Threshold Parameters in Accelerometry: Enhancing Upper Extremity Use Quantification in Cerebral Palsy.

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.

The effect of a task-specific training on upper limb performance and kinematics while performing a reaching task in a fatigued state.

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.

The REHAB-LAB model for individualized assistive device co-creation and production.

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.

EMaGer: A Wearable Full-Circumference HD-EMG Sensor and Data Augmentation Method for Robust Hand Gesture Recognition.

This work presents EMaGer, a new 360° 64-channel high-density electromyography (HD-EMG) bracelet combined with an original data augmentation method for improved robustness in gesture recognition. By leveraging homogeneous electrode density and powerful deep learning techniques, the sensor is capable of rotation invariance around the arm axis, thus increasing gesture recognition robustness to electrode movement and inter-session evaluation. The system is made of a 4x16 electrode array covering the full circumference of the limb, and uses a sampling frequency of 1 kHz and a 16-bit resolution. The sensor's uniform and adjustable geometry paired with an array barrel shifting data augmentation (ABSDA) technique allows a convolutional neural network to maintain a 76.98% inter-session classification accuracy for a 6 gestures dataset, from a baseline intra-session accuracy of 93.75%. High inter-session classification accuracy decreases the training burden for users of EMG control systems such as myoelectric prostheses by minimizing calibration requirements. The same methods applied with different state-of-the-art sensors are demonstrated to be less effective. Thus, this work evidences the importance of co-designing the EMG sensor system with the gesture inference algorithms to leverage synergistic properties and solve state-of-the-art challenges.Clinical relevance- This paper establishes a method that alleviates clinical manipulations in setting up and calibrating myoelectric prosthetic devices.

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.

Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control.

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.

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review.

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.

Physical and cognitive impairments in people suffering from long COVID: protocol for a longitudinal population-based cohort study.

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.

Use of a dynamic arm support to drive a power wheelchair: a case report.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.IMPLICATIONS FOR REHABILITATIONNo 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.

The use of a three-dimensional dynamic arm support prevents the development of muscle fatigue during repetitive manual tasks in healthy individuals.

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.

Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy.

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.

Mechanical design of a new device to assist eating in people with movement disorders.

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.

A Real-Time Algorithm to Estimate Shoulder Muscle Fatigue Based on Surface EMG Signal For Static and Dynamic Upper Limb Tasks.

Despite prevention efforts, the prevalence of workrelated upper extremity musculoskeletal disorders (WRUED) is increasing. A limit in the development of preventive interventions is the lack of devices that can measure and process sEMG signals in order to provide real-time reliable information on muscular fatigue of the upper limb in relation to the physical demands of the work. In this paper, the development and evaluation of a real-time muscle fatigue detection algorithm based on sEMG will be presented. The proposed algorithm uses the median frequency of sEMG power spectrum density (PSD) obtained with the Continuous Wavelet Transform (CWT) as an indicator of the muscle fatigue level. To extend the algorithm's efficiency to dynamic tasks, a muscle contraction detection module is added in order to remove the segments when the muscle is not contracting. To assess the algorithm's performance, eight healthy adults performed simple static and dynamic shoulder tasks using different loads. The results of the proposed time-frequency method (i.e. CWT) were first compared to those of the traditional Short Time Fourier Transform (STFT). It was shown that the CWT performs better than the STFT in both static and dynamic loading conditions. The validity of the algorithm's output as a muscle fatigue indicator was verified by comparing the output's decrease rate with different loads. As expected, the algorithm's fatigue indicator decreased faster over time with heavier loads. It was also shown that the initial muscle fatigue estimation output is independent of the load. Finally, we studied the proposed muscle contraction detection module's efficiency to overcome issues associated with dynamic tasks. We observed a substantial improvement of the smoothness of the fatigue indicator's evolution by using of the muscle contraction detection module.

The impact of experimental pain on shoulder movement during an arm elevated reaching task in a virtual reality environment.

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.

Impact of Sensory Deficits on Upper Limb Motor Performance in Individuals with Cerebral Palsy: A Systematic Review.

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.

Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning.

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.

Assistive robotic arm: Evaluation of the performance of intelligent algorithms.

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.

Evaluation of the usability of an actively actuated arm support.

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.

Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors.

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.

Alternative Representation of the Shoulder Orientation Based on the Tilt-and-Torsion Angles.

The International Society of Biomechanics (ISB) has proposed standardized recommendations for recording human joint motion. The Euler angles-the orientation representation currently proposed by the ISB-have two drawbacks, namely, the issue of singularities (gimbal lock) and the difficulty to obtain clinical and interpretable orientation representation for compound movements. The orientation representation of the shoulder joint with the Euler angles is particularly challenging due to its broad range of motion. This paper proposes and evaluates an alternative orientation representation for shoulder movement based on the tilt-and-torsion representation, a method that aims at providing a more clinically interpretable solution for describing joint movements compared to the standard Euler angles. Three studies were performed to compare the different orientation representation methods. The first two studies consist in simulations of arm elevation in different planes. The third study is an experiment using inertial-measurement-units with one test subject performing shoulder elevation movements in different planes. The tilt-and-torsion representation is then compared with different Euler angle conventions. The results show that Euler angles are biased or clinically uninterpretable for compound movements. Conversely, tilt-and-torsion representation does not suffer from these limitations. Although not extensive, the experiments suggest that the tilt-and-torsion representation has the potential to better represent human movements and provide more clinically interpretable results than the Euler angles.