Publications by authors named "Alexandre Campeau-Lecours"

This study focuses on the development of a wearable real-time Humming Detector Device (HDD) aimed at enhancing the control of assistive devices through humming. As the need for portable user-friendly tools in assistive technology grows, the HDD offers a non-invasive solution to detect vocal cord vibrations. Vibrations, detected thanks to an accelerometer worn on the neck, are processed in real time using a Fast Fourier Transform (FFT) to identify specific humming frequencies, which are then translated into commands for controlling assistive devices via Bluetooth Low Energy (BLE) transmission.

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The inability to use one's hands or arms greatly restricts the ability to perform daily activities. After a developmental or acquired injury, the intensity and frequency of rehabilitation exercises are essential. To alleviate the burden on the healthcare system, robotic systems have been developed to support clinicians' interventions.

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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.

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Purpose: To conduct a systematic review on the impacts of using mechanical assistive devices on function, performance in activities and participation of persons with upper extremity impairments, and to synthesize the strengths and limitations of these devices.

Method: Three independent reviewers conducted systematic searches of articles published between 2003 and 2023 in Compendex, Inspec, Embase, PubMed/Medline, IEEE Xplore, and Web of Science, as well as manual searches on the RESNA website for conference papers over the same period. The methodological quality of articles was appraised using the tool.

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Unlabelled: 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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Article Synopsis
  • Adults with cerebral palsy (CP) face challenges with both using one arm and both arms together, affecting daily activities.
  • Researchers aimed to determine how well accelerometer data can measure these motor skills compared to video analyses.
  • The study found that the accelerometry-based metrics were effective in distinguishing between different task performances and groups, suggesting they could be useful for evaluating arm functions in adults with CP.
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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.

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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.

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Unlabelled: 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.

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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.

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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.

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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.

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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.

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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.

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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.

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This paper presents the development of a new active assistive eating device, which aims to stabilize the movements of people living with movement disorders, such as spasticity and ataxia. Many people living with upper-body incapacities are unable to eat on their own, due to movement disorders (ex. tremors, spastic motions, lack of muscular tone), resulting from various ailments like Cerebral palsy, Parkinson's disease, Dystonia, Multiple sclerosis, strokes, and Muscular dystrophy).

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