IEEE Trans Biomed Circuits Syst. 2015 Oct;9(5):620-30. doi: 10.1109/TBCAS.2015.2476555. Epub 2015 Oct 26.
Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.
可穿戴设备具有成本低、用户友好等有趣的特点，但由于其提供的硬件资源有限，它们在医疗应用中的使用仍是一个开放的研究课题。在本文中，我们提出了一种基于嵌入式的实时肌电手势识别解决方案。该工作侧重于系统的多层次设计，集成硬件和软件组件，开发一种能够采集和处理肌电信号以进行实时手势识别的可穿戴设备。该系统结合了定制模拟前端的准确性和低功耗、高性能微控制器的灵活性，用于板载处理。我们的系统实现了与应用于对信号质量有严格要求的高端且更昂贵的主动肌电传感器相同的精度。同时，由于其灵活的配置，它可以与市场上为数有限的专为肌电手势识别设计的可穿戴平台相媲美。我们证明，在嵌入板载手势识别的同时，我们可以达到相似或更好的性能，同时降低成本。为了验证这种方法，我们从 4 位用户中收集了 7 个手势的数据集，用于评估肌电通道数量、识别的手势数量和数据率对识别精度和分类器计算需求的影响。结果，我们在提出的可穿戴平台上实现了一个能够实时执行的 SVM 识别算法，达到了 90%的分类率，这与最新的离线结果一致，同时功耗为 29.7mW，保证了在 400mAh 电池下可连续运行 44 小时。
