School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea.
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.
Sensors (Basel). 2023 Jan 28;23(3):1436. doi: 10.3390/s23031436.
Recently, human-machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW.
最近，许多领域都在研究方便生活的人机界面 (HMI)。特别是，手势识别 (HGR) 系统作为可穿戴系统具有优势，用户可以轻松直观地控制设备。在 HGR 系统中使用的各种传感器中，表面肌电图 (sEMG) 传感器不受采集环境的影响，易于佩戴，并且需要少量数据。基于这些优势，以前基于 sEMG 的 HGR 系统使用多个传感器或复杂的深度学习算法来实现高精度的分类。然而，使用多个传感器的系统体积庞大，而具有复杂深度学习算法的嵌入式平台难以实现。为了克服这些限制，我们提出了一种使用二进制神经网络 (BNN) 和轻量级卷积神经网络 (CNN) 的 HGR 系统，该系统仅使用一个干式 sEMG 传感器，在现场可编程门阵列 (FPGA) 上实现。所提出的 HGR 系统对九种动态手势进行分类，这些手势在现实生活中可能很有用，而不是相对容易分类的静态手势。从动态手势中采集的原始 sEMG 数据转换为具有时频域信息的频谱图，并传输到分类器。结果，所提出的 HGR 系统实现了 95.4%的分类准确率，计算时间为 14.1ms，功耗为 91.81mW。
