A Smart Chair Sitting Posture Recognition System Using Flex Sensors and FPGA Implemented Artificial Neural Network
A Smart Chair Sitting Posture Recognition System Using Flex Sensors and FPGA Implemented Artificial Neural Network
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DOI：
10.1109/jsen.2020.2980207
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发表时间：
2020-07-15
影响因子：
4.3
通讯作者：
Tang, Wei
中科院分区：
文献类型：
--
作者：
Hu, Qisong;Tang, Xiaochen;Tang, Wei
坐是现代人最常见的状态。一些坐姿可能会带来健康问题。为了防止不良坐姿的危害，期望具有低功耗和低计算开销的本地坐姿识别系统。该系统还应提供具有准确性和隐私性的良好用户体验。本文报道了一种新的姿势识别系统的办公椅，可以分类七种不同的健康相关的坐姿。该系统使用六个柔性传感器，模数转换器（ADC）板和机器学习算法的两层人工神经网络（ANN）上实现的斯巴达-6现场可编程门阵列（FPGA）。该系统实现了97.78%的精度与浮点评估和97.43%的精度与9位定点实现。ADC控制逻辑和人工神经网络的最大传播延迟为8.714 ns。当采样速率为5Sample/s，时钟频率为5 MHz时，动态功耗为7.35mW。
Sitting is the most common status of modern human beings. Some sitting postures may bring health issues. To prevent the harm from bad sitting postures, a local sitting posture recognition system is desired with low power consumption and low computing overhead. The system should also provide good user experience with accuracy and privacy. This paper reports a novel posture recognition system on an office chair that can categorize seven different health-related sitting postures. The system uses six flex sensors, an Analog to Digital Converter (ADC) board and a Machine Learning algorithm of a two-layer Artificial Neural Network (ANN) implemented on a Spartan-6 Field Programmable Gate Array (FPGA). The system achieves 97.78% accuracy with a floating-point evaluation and 97.43% accuracy with the 9-bit fixed-point implementation. The ADC control logic and the ANN are constructed with a maximum propagation delay of 8.714 ns. The dynamic power consumption is 7.35 mW when the sampling rate is 5 Sample/second with the clock frequency of 5 MHz.