Energy efficiency and low latency are crucial requirements for designing wearable AI-empowered human activity recognition systems, due to the hard constraints of battery operations and closed-loop feedback. While neural network models have been extensively compressed to match the stringent edge requirements, spiking neural networks and event-based sensing are recently emerging as promising solutions to further improve performance due to their inherent energy efficiency and capacity to process spatiotemporal data in very low latency. This work aims to evaluate the effectiveness of spiking neural networks on neuromorphic processors in human activity recognition for wearable applications. The case of workout recognition with wrist-worn wearable motion sensors is used as a study. A multi-threshold delta modulation approach is utilized for encoding the input sensor data into spike trains to move the pipeline into the event-based approach. The spikes trains are then fed to a spiking neural network with direct-event training, and the trained model is deployed on the research neuromorphic platform from Intel, Loihi, to evaluate energy and latency efficiency. Test results show that the spike-based workouts recognition system can achieve a comparable accuracy (87.5\%) comparable to the popular milliwatt RISC-V bases multi-core processor GAP8 with a traditional neural network ( 88.1\%) while achieving two times better energy-delay product (0.66 \si{\micro\joule\second} vs. 1.32 \si{\micro\joule\second}).

翻译：能效与低延迟是设计基于人工智能的可穿戴人体活动识别系统的关键需求，这受到电池供电和闭环反馈的硬性约束。尽管神经网络模型已通过广泛压缩来满足严苛的边缘计算要求，但脉冲神经网络与事件驱动感知技术因其固有的能效优势以及处理时空数据的极低延迟能力，近期正成为进一步提升系统性能的可行方案。本研究旨在评估脉冲神经网络在神经形态处理器上用于可穿戴人体活动识别的有效性，以手腕佩戴式运动传感器的工作识别场景为例展开研究。采用多阈值增量调制方法将输入传感器数据编码为脉冲序列，从而将处理流程切换至事件驱动范式。脉冲序列随后被输入至经过直接事件训练的脉冲神经网络，训练完成的模型部署于英特尔研发的神经形态计算平台Loihi上，以评估其能效与延迟表现。测试结果表明，基于脉冲的工作识别系统可达到与采用传统神经网络的毫瓦级RISC-V多核处理器GAP8相当的准确率（87.5%对比88.1%），同时能效延迟乘积（0.66微焦耳·秒对比1.32微焦耳·秒）提升至两倍。