# Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb Fractures in Community Settings

arXiv: https://arxiv.org/abs/2501.13888 · HTML: https://mlsanity.com/paper/2501.13888/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Bag of States: A Non-sequential Approach to Video-based Engagement Measurement](https://mlsanity.com/paper/2301.06730/) (2023)
- [MAISON -- Multimodal AI-based Sensor platform for Older Individuals](https://mlsanity.com/paper/2211.03615/) (2022)

## Built on by (most cited first, 3 of 3)

- [Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models](https://mlsanity.com/paper/2506.12156/) (2025)
- [A longitudinal geospatial multimodal dataset of post-discharge frailty, physiology, mobility, and neighborhoods](https://mlsanity.com/paper/2602.00060/) (2026)
- [Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement](https://mlsanity.com/paper/2608.23531/) (2026)

## Abstract (arXiv)

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.