用共享残差模块让手环在低功耗下实现稳定手势识别
One Residual with Three Reuses: A Wristband Front End for Gesture Sensing
- 三类信号处理共用一个残差生成器，节省芯片资源
- 误报率1%时检测率达72%~80%，功耗降低47%
- 适合可穿戴设备、医疗监测等低功耗场景
为实现连续腕戴手势感知与运动症状监测，需设计一个始终开启的前端系统，在硬币电池功耗预算内，结合60 GHz FMCW雷达与微机电系统（MEMS）惯性测量单元（IMU），以应对遮挡和贴体漂移问题。本文提出一种设计研究，将分类器唤醒门控、毫米波与IMU路由选择、基于创新的扩展卡尔曼滤波（EKF）测量重加权三者共享一个片上残差生成器。该生成器占用14.4 KB程序内存与278 B状态空间，每帧运行11万次乘加运算，部署于Ambiq Apollo4 Blue Plus级边缘MCU。在四个公开数据集（IPN Hand、SHREC 2021、MiliPoint 60 GHz FMCW雷达、EAT-Radar）上，系统在1%误报率下达到0.72/0.80的检测概率，90%识别召回率下分类器调用能耗降低47%，且在测量偏差漂移下姿态跟踪均方根误差相比自适应卡尔曼滤波（带R膨胀）基线降低4.6倍。芯片实测功耗与佩戴性能留待后续硬件验证，本文贡献为设计研究。
原文摘要 · Abstract (English)
Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end that fits inside a coin-cell power budget while pairing a micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) with a 60 GHz frequency-modulated continuous-wave (FMCW) radar to stay robust under occlusion and on-body drift. We present a design study of such a wristband front end in which classifier wake-up gating, mmWave versus IMU routing, and innovation-based EKF measurement reweighting share a single on-chip residual generator. The shared generator occupies 14.4 KB of program memory and 278 B of state and runs at 110K multiply-accumulates (MACs) per frame on an Ambiq Apollo4 Blue Plus class edge microcontroller unit (MCU). Across four public sensor data corpora (IPN Hand, SHREC 2021, MiliPoint 60 GHz FMCW radar, EAT-Radar) the front end reaches detection probability $P_D = 0.72/0.80$ at a 1% false-alarm rate, sustains a 47% classifier invocation energy reduction at 90% gesture detection recall, and lowers pose tracking root-mean-square error by $4.6\times$ under measurement bias drift relative to an adaptive Kalman with $R$-inflation baseline. Measured silicon power and on-body capture are deferred to follow-on hardware; the contribution here is a design study.
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