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AI activity recognition models show performance gap for older adults

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv highlights a significant performance gap in human activity recognition (HAR) models when applied to older adults, despite advancements in deep learning. Researchers found that models trained primarily on younger adult data do not effectively generalize to older populations, leading to persistent accuracy disparities. The study suggests that utilizing richer representations, such as self-supervised features pre-trained on diverse datasets like the UK Biobank, can substantially improve HAR performance in older adults and narrow this gap. AI

IMPACT Highlights the need for diverse datasets and personalized adaptation in AI models to ensure equitable performance across different demographics.

RANK_REASON The cluster contains a research paper detailing findings on AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI activity recognition models show performance gap for older adults

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The cluster contains a research paper detailing findings on AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hossein Khayami, Sungjin Hwang, Eshed Ohn-Bar, David E. Conroy, Amanda Lazar, Eun Kyoung Choe, Hernisa Kacorri ·

    Characterizing the Performance Gap in Human Activity Recognition for Older Adults

    arXiv:2610.02711v1 Announce Type: cross Abstract: 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…

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