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

包括物理治疗师和运动教练或训练师在内的运动专业人士，通过识别和解决运动策略中的细微偏差来改变人类的运动，这些偏差通常很难在视觉上发现。肌电图（EMG）可以获得有关肌肉激活策略的有用信息，但这需要专家信号采集和解释。生物反馈系统已经被开发出来，通过自动将肌电图信号转化为对客户有用的反馈，来增强对肌电图信号中有价值信息的获取。然而，这些系统的影响是有限的，因为它们只整合了一两个肌肉的肌电图。姿势调整和其他分布的肌肉活动没有被捕捉到，仍然必须由训练有素的运动专业人员通过视觉检测通过语言或身体提示来解决。为了解决这个缺点，我们设计了一个系统，将四个或更多的肌肉源集成到有用的肌电生物反馈中。首先，在多次重复选定的特定于训练干预的动作时记录肌电图信号。这些都是在运动专家的监督下进行的，他会提供指导提示，以确保产生最佳的运动模式。这些肌电图数据被用来建立一个机器学习模型，该模型可以实时识别正在进行的运动。该模型使用户能够控制俄罗斯方块游戏，其中当相关的最佳肌电图模式匹配时，动作对应于块操作。机器学习模型的预测整合了来自所有传感器的数据，因此可以捕获大部分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交付给那些可以通过开发评估和最大化我们设备的商业潜力所需的技能而受益的人。我们的系统有可能使患有影响肌肉控制的疾病（例如中风、脊髓损伤、多发性硬化症等）的加拿大人受益，也可以帮助训练熟练的运动员或工人，通过改变运动模式来提高表现或预防受伤。

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.