Characterizing the Performance Gap in Human Activity Recognition for Older Adults
Human activity recognition (HAR) from wrist-worn accelerometers is increasingly used for health and behavioral tracking. Yet, most wearable HAR models are developed and evaluated on datasets dominated by younger adults, leaving it unclear whether benchmark progress generalizes across age groups. In this work, we leverage MyMove, our carefully annotated, free-living older-adult HAR dataset (mean age 71), to evaluate deep-learning architectures and training regimes under both leave-one-subject-out and cross-dataset transfer. We find that improvements on younger-adult benchmarks fail to transfer equally to data collected from older adults, resulting in a persistent and often widening perform...
| Authors | Hossein Khayami, Sungjin Hwang, Eshed Ohn-Bar et al. |
|---|---|
| Primary category | cs.HC |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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