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Design Challenges for Wearable EMG Applications

机译:可穿戴EMG应用的设计挑战

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Wearable technologies are changing the way we deal with health and fitness in our daily life. Nevertheless, while MEMS-enabled inertial sensors have conquered the consumer market, physiological monitoring has still to face barriers due to the complexity and costs of physical interfaces (e.g. electrodes), the degree of intuitiveness of the interaction and the processing required to reach satisfying performance. These limitations are mitigated by the embedded systems' growing integration of interfacing capabilities and efficient computing power. In this paper, we describe the main applications and the related technologies for the acquisition and processing of myoelectric (EMG) signals. Starting from well established active sensors and bench-top setups, we introduce a recent design based on the combination of an integrated Analog Front End (AFE) and embedded processing. This solution provides high quality signal acquisition and on-board digital processing capabilities with a contained power consumption. The system was tested within the prosthesis control application scenario, one of the most stringent EMG applications, achieving a 90% gesture recognition accuracy with real time on-board processing at a power consumption of 30 mW. Such promising results highlight the current trend in shifting EMG applications from dedicated analog solutions towards integrated digital devices, favouring the development of advanced, modular and low-power wearable solutions.
机译:可穿戴技术正在改变我们在日常生活中处理健康和健康的方式。然而,虽然使MEMS的惯性传感器征服了消费者市场,但由于物理接口(例如电极)的复杂性和成本,相互作用的直观程度以及达到满足性能所需的处理的程度,生理监测仍然是面临障碍。 。通过嵌入式系统越来越多地集成接口能力和有效的计算能力,减轻了这些限制。在本文中,我们描述了用于获取和处理磁铁电(EMG)信号的主要应用和相关技术。从建立的有源传感器和台式设置开始,我们基于集成模拟前端(AFE)和嵌入式处理的组合引入了最近的设计。该解决方案提供高质量的信号采集和板载数字处理能力,具有包含的功耗。该系统在假肢控制应用方案中测试了最严格的EMG应用之一,实现了90%的手势识别精度,在电源消耗为30mW时具有实时地板处理。这种有希望的结果突出了从专用模拟解决方案转向集成数字设备的电流趋势,偏好开发先进,模块化和低功耗可穿戴解决方案。

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