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Enabling Older Adults to Provide High-quality Activity Labels: Unpacking Accuracy, Precision, and Granularity in

Yiwen Wang¹, Hossein Khayami¹, Bongshin Lee²

¹University of Maryland, College Park, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

|March 5, 2026

Summary

Older adults prefer less intrusive ways to label their activity data for personalized trackers. User-initiated and machine-prompted labeling strategies reduce the burden of high-quality data annotation.

Keywords:

Activity tracking Co-design Data labeling Human activity recognition Label quality Machine teaching Older adults Personalization

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Area of Science:

Human-Computer Interaction
Gerontology
Machine Learning

Background:

High-quality labeled activity data is crucial for developing personalized activity recognition models for older adults.
Labeling real-world data poses challenges, burdening users and potentially impacting their engagement in activities.

Purpose of the Study:

To investigate older adults' perceptions of providing high-quality labels for training personalized activity trackers.
To explore user-centered strategies for efficient and less intrusive data labeling.

Main Methods:

A co-design study involving 12 older adults was conducted.
The teachable machines paradigm was used as a scaffold for envisioning the labeling process.
Thematic analysis was employed to understand perspectives on accuracy, precision, and granularity in activity labeling.

Main Results:

Older adults' definitions of labeling quality (accuracy, precision, granularity) were unpacked.
Preferred strategies included user-initiated labeling and machine-initiated prompting to reduce intrusiveness and burden.
Discrepancies between user perceptions and technical standards were identified.

Conclusions:

Design considerations for future data labeling tools should prioritize user preferences for reduced burden.
Balancing user experience with technical requirements is essential for effective personalized activity tracker development.
User-initiated and machine-prompted labeling show promise for improving data quality and user acceptance.