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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Dual-Model YOLOv7-Based Image Recognition System to Simplify Data Entry for the Elderly in Mobile Health.

Yu-Cheng Liu¹, Wei-Ting Lee¹, Chia-Chi Yin¹

¹National Yang Ming Chiao Tung University, Institute of Biomedical Informatics.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

A new dual-system model using YOLOv7 technology significantly improves medical equipment data entry for elderly users in long-term care, boosting efficiency by 92.5%. This innovation aims to ease user burden and ensure accurate health data collection.

Keywords:

Deep Learning Digit Recognition Medical Devices YOLOv7

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Area of Science:

Biomedical Engineering
Computer Vision
Gerontology

Background:

Data entry for medical equipment poses challenges, especially for elderly users.
Efficiency and accuracy in health data collection are critical in long-term care settings.

Purpose of the Study:

To develop an automated system for identifying medical equipment values.
To enhance data entry efficiency and accessibility for elderly users.

Main Methods:

A dual-system model was developed utilizing the YOLOv7 object detection algorithm.
The system was trained and validated using internal photographs in a long-term care environment.

Main Results:

The YOLOv7-based model achieved a 92.5% improvement in data entry efficiency.
The system demonstrated practical effectiveness in real-world implementation.

Conclusions:

The developed dual-system model enhances data entry efficiency and accessibility for elderly users.
The system contributes to accurate health data collection and has potential for expansion to mobile health devices.