Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb Fractures in Community Settings
This dataset addresses the problem of remote monitoring for older adults post-fracture to prevent isolation and decline, but it is incremental as it focuses on data collection rather than novel methods.
The paper introduces MAISON-LLF, a publicly available multimodal sensor dataset collected from ten older adults recovering from lower-limb fractures over 560 days, and validates it using supervised machine-learning and deep-learning models for monitoring health outcomes.
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.