L2M NSERC - An EMG-based biofeedback system to facilitate neuroplastic adaptation in healthy and clinical populations

L2M NSERC - 基于肌电图的生物反馈系统，可促进健康和临床人群的神经塑性适应

• 批准号: 576593-2022
• 负责人: Nelson, AimeeJ
• 金额: $ 1.46万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757717
• 关键词: L2M; NSERC; EMG; based; biofeedback

Exercise professionals, including physiotherapists and athletic coaches or trainers, work to modify human movement by identifying and addressing subtle deviations in movement strategy that are often difficult to detect visually. Useful information about muscle activation strategies can be obtained using electromyography (EMG), but this requires expert signal acquisition and interpretation. Biofeedback systems have been developed to enhance access to the valuable information contained in EMG signal by automating its translation into useful feedback for the client. However, the impact of these systems is limited because they integrate EMG from just one or two muscles. Postural adjustments and other distributed muscle activities are not captured and must still be addressed by verbal or physical cueing pursuant to visual detection by a trained exercise professional. To address this shortcoming, we have designed a system that integrates four or more muscle sources into useful EMG biofeedback. First, EMG signal is recorded during several repetitions of selected movements that are specific to the training intervention. These are performed under the supervision of an exercise professional who provides guiding cues to ensure optimal movement patterns are produced. This EMG data is used to build a machine learning model that can identify movements being performed in real-time. The model enables the user to control a Tetris game wherein movements correspond to block manipulations when the associated optimal EMG pattern has been matched. Predictions by the machine learning model integrate data from all sensors, and can thus capture much of the distributed muscle activations that contribute to trained movements. The NSERC Lab2Market program will accelerate the delivery of our product to those who can benefit by developing skills required to evaluate and maximize the commercial potential of our device. Our system has potential to benefit Canadians living with conditions that impact muscle control (e.g., stroke, spinal cord injury, multiple sclerosis etc.), and can also aid in the training of skilled athletes or workers where modifying movement patterns can improve performance or prevent injury.

运动专业人士，包括物理治疗师，运动教练或培训师，通过识别和解决运动策略中的细微偏差来修改人类运动，这些偏差通常很难在视觉上检测到。可以使用肌电图（EMG）获得有关肌肉激活策略的有用信息，但这需要专家信号获取和解释。已经开发了生物反馈系统，以通过将其翻译成对客户的有用反馈来增强对EMG信号中包含的有价值信息的访问。但是，这些系统的影响受到限制，因为它们仅从一两个肌肉中整合了EMG。 姿势调整和其他分布的肌肉活动未被捕获，并且必须通过训练有素的运动专业人员的视觉检测来解决口头或身体的提示。为了解决这一缺点，我们设计了一个系统，该系统将四个或多个肌肉源集成到有用的EMG生物反馈中。首先，在训练干预特定的选定运动的几次重复过程中记录了EMG信号。这些是在运动专业人员的监督下进行的，该运动专业人士提供指导提示，以确保产生最佳的运动模式。此EMG数据用于构建一个机器学习模型，该模型可以识别实时执行的运动。该模型使用户能够控制一个俄罗斯方块游戏，其中当相关的最佳EMG模式匹配时，动作对应于阻塞操作。 机器学习模型的预测会整合来自所有传感器的数据，因此可以捕获许多有助于训练运动的分布式肌肉激活。 NSERC LAB2Market计划将加速我们的产品向那些可以通过开发评估和最大化设备商业潜力的技能而受益的人的产品。我们的系统有可能使加拿大人受益于影响肌肉控制的疾病（例如，中风，脊髓损伤，多发性硬化症等），还可以帮助培训熟练运动员或工人，在这种情况下，改变运动模式可以改善性能或防止损伤。