ArticleACM transactions on computing for healthcare2022
Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction.
Article in ACM transactions on computing for healthcare, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Proactive concussion prediction using symmetry-aware multiscale explainable hybrid deep learning and multimodal data fusion.Scientific reports · 2026Article
- Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.The Clinical neuropsychologist · 2026Article
- Detecting the Impact of Older Adult Healthy Brain Aging Behaviour Adoption Using Smart Home Technology.Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024Article
- Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series.International journal of nursing studies advances · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this paper, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to ten clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from
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