IEEE Trans Biomed Circuits Syst. 2019 Dec;13(6):1563-1574. doi: 10.1109/TBCAS.2019.2953998. Epub 2019 Nov 18.
This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision tree (GBDT) based hand gesture recognition. A hydrogel-silica gel based flexible surface electrode band is used as the tissue interface. The sEMG signal is collected using a neural signal acquisition analog front end (AFE) chip. A quantitative analysis method is proposed to balance the algorithm complexity and recognition accuracy. A parallel GBDT implementation is proposed featuring a low latency. The proposed GBDT based neural signal processing unit (NSPU) is implemented on an FPGA near the AFE. A RF module is used for wireless communication. A hand gesture set including 12 gestures is designed for human-computer interaction. Experimental results show an overall hand gesture recognition accuracy of 91%.
本文提出了一种基于梯度提升决策树（GBDT）的可穿戴智能表面肌电记录器的手势识别方法。使用水凝胶-硅胶基柔性表面电极带作为组织接口。使用神经信号采集模拟前端（AFE）芯片采集表面肌电信号。提出了一种定量分析方法来平衡算法复杂度和识别精度。提出了一种低延迟的并行 GBDT 实现方法。所提出的基于 GBDT 的神经信号处理单元（NSPU）在 AFE 附近的 FPGA 上实现。使用射频模块进行无线通信。设计了一个包括 12 个手势的手势集，用于人机交互。实验结果表明，整体手势识别准确率为 91%。
