SensWear：一个开放、模块化且AI就绪的可穿戴平台

中文摘要 AI 辅助

可穿戴AI/ML研究需要原始、同步且可重构的多模态数据，但消费级设备是封闭的，许多研究平台仍局限于单一形态或传感器组合。本文介绍了SensWear，一个开放、模块化且AI就绪的可穿戴平台，它将形态、传感、数据接口和学习解耦。一个紧凑的柔性印刷电路板（PCB）主板和可编程的1.2V至5.5V子板接口支持即插即用的光电容积脉搏波（PPG）、触摸、温度、触觉和LED模块，适用于多种可穿戴形态。Zephyr固件提供驱动程序、时间戳、原始流式传输/记录以及传感器存在元数据。案例研究展示了动脉PPG波形捕获和具有竞争力的心率准确性，同时保留了可检查的原始数据，以实现可复现的闭环实验。

英文摘要

Wearable AI/ML research needs raw, synchronized, and reconfigurable multimodal data, but consumer devices are closed and many research platforms remain tied to one embodiment or sensor set. This paper presents SensWear, an open, modular, and AI-ready wearable platform that decouples embodiment, sensing, data interfaces, and learning. A compact flexible-Printed Circuit Board (PCB) main board and programmable 1.2 V to 5.5 V daughter-board interface support plug-and-play Photo- PlethysmoGraphy (PPG), touch, temperature, haptic, and LED modules across wearable form factors. Zephyr firmware pro- vides drivers, timestamping, raw streaming/logging, and sensor- presence metadata. Case studies show arterial PPG waveform capture and competitive heart-rate accuracy while preserving inspectable raw data for reproducible closed-loop experiments.

发表机构

• MIT（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。