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This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (1064) mHealth for Data Collection and Research (1677) Dementia and Cognitive Decline (866) Wearable Devices and Sensors (1521) Machine Learning (3049) Aging in Place (497) Digital Biomarkers and Digital Phenotyping (419) Sensors at Home and Domotics (106) Frailty Detection, Assessment and Prediction (215)

Published on in Vol 15 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79490, first published .
Elderly man reading a book in a comfortable armchair by a lamp

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor–Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor–Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study

Authors of this article:

James Patrick Moon1 Author Orcid Image ;   Khalid Abdul Jabbar2 Author Orcid Image ;   Tony Chin Ian Tay2 Author Orcid Image ;   Laura Tay3 Author Orcid Image ;   Rathi Mahendran4 Author Orcid Image ;   Tze Pin Ng5 Author Orcid Image ;   Shian Ming Tan1 Author Orcid Image ;   Wilbur Zhi Hao Koh6 Author Orcid Image ;   Weng Yan Ying7 Author Orcid Image ;   Ah-Hwee Tan7 Author Orcid Image ;   Tih-Shih Lee8 Author Orcid Image ;   Iris Rawtaer1, 2, 9 Author Orcid Image
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Journals

  1. Lin J, Kuok K, Mio W, Tan C, Tou P, Cheang C. Beyond single-modality screening: toward an EEG–IoT dual-stream framework for early detection of mild cognitive impairment in community-dwelling older adults. Frontiers in Aging Neuroscience 2026;18 View