PROS: an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables with on-chip intelligence
PROS: an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables with on-chip intelligence
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DOI：
10.1145/3495243.3560533
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发表时间：
2022-10
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通讯作者：
Nhat Pham;Hong Jia;Minh Tran;Tuan Dinh;Nam Bui;Young D. Kwon;Dong Ma;Phuc Nguyen;C. Mascolo;Tam N. Vu
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作者：
Nhat Pham;Hong Jia;Minh Tran;Tuan Dinh;Nam Bui;Young D. Kwon;Dong Ma;Phuc Nguyen;C. Mascolo;Tam N. Vu
虽然近年来可穿戴设备的全球医疗保健市场一直在显着增长，预计到2028年将达到600亿美元，但由于电池寿命有限，响应速度慢和生物信号质量不足，许多重要的医疗保健应用，如癫痫监测，嗜睡检测等尚未部署。这项研究提出了PROS，一种有效的模式驱动的压缩感知框架，用于低功耗的基于生物电位的可穿戴设备。PROS通过引入微小的模式识别原语和利用生物信号稀疏性的模式驱动压缩感知技术，消除了信号质量，响应时间和功耗之间的传统权衡。具体来说，我们（i）开发微型机器学习模型来消除不相关的生物信号模式，（ii）使用适当的稀疏小波域有效地对相关生物信号进行压缩采样，以及（iii）优化硬件和操作系统操作以提高处理效率。PROS还提供了一个抽象层，因此应用程序只需要关心检测到的相关生物信号模式，而不需要知道下面的优化。我们已经实施和评估了两个开放的生物信号数据集与120个主题和6个生物信号模式的PROS。在癫痫发作监测等实际用例的未知主题上的实验结果非常令人鼓舞。PROS可以将流数据速率降低24倍，同时保持高保真信号。它将可穿戴设备的电源效率提高了1200%以上，并使设备能够立即对关键事件做出反应。PROS的内存和运行时开销最小，每个生物信号模式分别只有几KB和10毫秒。目前，PROS已被多所大学和医院的研究项目采用。
While the global healthcare market of wearable devices has been growing significantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. This study proposes PROS, an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-off between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) develop tiny machine learning models to eliminate irrelevant biosignal patterns, (ii) efficiently perform compressive sampling of relevant biosignals with appropriate sparse wavelet domains, and (iii) optimize hardware and OS operations to push processing efficiency. PROS also provides an abstraction layer, so the application only needs to care about detected relevant biosignal patterns without knowing the optimizations underneath. We have implemented and evaluated PROS on two open biosignal datasets with 120 subjects and six biosignal patterns. The experimental results on unknown subjects of a practical use case such as epileptic seizure monitoring are very encouraging. PROS can reduce the streaming data rate by 24X while maintaining high fidelity signal. It boosts the power efficiency of the wearable device by more than 1200% and enables the ability to react to critical events immediately on the device. The memory and runtime overheads of PROS are minimal, with a few KBs and 10s of milliseconds for each biosignal pattern, respectively. PROS is currently adopted in research projects in multiple universities and hospitals.