用振动传感器+音频合成数据，让老人居家活动识别更准更省力。
RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online
- 用近场音频合成数据预训练模型，减少对真实标注数据的依赖。
- 仅需少量新用户数据即可微调，实现跨环境泛化。
- 适合养老监护、远程健康监测等场景，隐私性好。
全球四分之一的痴呆患者独居，家属需远程照护。现有远程监控方案在隐私保护、活动识别及跨用户/环境泛化方面仍存局限。结构振动传感器作为无感方案，已在受控环境下成功实现人体识别与活动感知。但实际家庭部署中，现有方法需大量标注数据才能准确识别活动。本文提出可扩展的RARR框架：利用近场音频合成数据预训练模型，并仅用极少量真实数据微调，构建鲁棒的日常行为追踪系统。
原文摘要 · Abstract (English)
One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and activity recognition, in controlled settings by sensing surface vibrations generated by activities. However, when deploying in an end user's home, current solutions require a substantial amount of labeled data for accurate activity recognition. Our scalable solution adapts synthesized data from near-surface acoustic audio to pretrain a model and allows fine tuning with very limited data in order to create a robust framework for daily routine tracking.
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