公开了10位老人8周的多模态传感器数据，助远程监测骨折康复。
Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb Fractures in Community Settings
- 融合手机、手表、体动、睡眠垫等多源数据采集
- 覆盖560天24小时数据，含临床量表评估社交孤立与功能下降
- 提供可复现的机器学习基线模型，适合老龄化研究者使用
下肢骨折（LLF）是老年人的重大健康问题，常导致行动能力下降和长期恢复，可能影响日常活动与独立性。康复期间，老年人易出现社交孤立与功能衰退，加重康复难度并损害身心健康。多模态传感器平台可持续采集数据，并通过机器学习算法实现远程监测与健康状态推断，还可预警高风险个体。本文发布了一个公开可用的多模态传感器数据集MAISON-LLF，数据来自居家独居的老年患者在社区环境中康复期的监测。数据包含智能手机与智能手表传感器、运动检测器、睡眠追踪床垫及关于社交孤立与功能衰退的临床问卷。共采集10名受试者，每人8周，总计560天的24小时连续数据。为技术验证，基于传感器与问卷数据构建了监督式机器学习与深度学习模型，为研究社区提供基础比较基准。
原文摘要 · Abstract (English)
Lower-Limb Fractures (LLF) are a major health concern for older adults, often leading to reduced mobility and prolonged recovery, potentially impairing daily activities and independence. During recovery, older adults frequently face social isolation and functional decline, complicating rehabilitation and adversely affecting physical and mental health. Multi-modal sensor platforms that continuously collect data and analyze it using machine-learning algorithms can remotely monitor this population and infer health outcomes. They can also alert clinicians to individuals at risk of isolation and decline. This paper presents a new publicly available multi-modal sensor dataset, MAISON-LLF, collected from older adults recovering from LLF in community settings. The dataset includes data from smartphone and smartwatch sensors, motion detectors, sleep-tracking mattresses, and clinical questionnaires on isolation and decline. The dataset was collected from ten older adults living alone at home for eight weeks each, totaling 560 days of 24-hour sensor data. For technical validation, supervised machine-learning and deep-learning models were developed using the sensor and clinical questionnaire data, providing a foundational comparison for the research community.
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