Machine and Deep Learning for Detection of Moderate-to-Vigorous Physical Activity From Accelerometer Data: Systematic...

Area of Science:

• Wearable technology and sensor data analysis
• Biomedical engineering and public health research
• Artificial intelligence in health and fitness

Background:

• Accurate monitoring of moderate-to-vigorous physical activity (MVPA) is crucial for public health and personalized interventions.
• Traditional accelerometry methods struggle with accuracy and generalizability in free-living conditions.
• Machine learning (ML) and deep learning (DL) offer advanced automated MVPA detection capabilities.

Purpose of the Study:

• To conduct a scoping review of ML and DL techniques for MVPA estimation using accelerometer data.
• To analyze the performance, bias, sensor configurations, and translational potential of these advanced methods.
• To synthesize current evidence on AI-driven physical activity assessment.

Main Methods:

• Systematic literature search following PRISMA-ScR guidelines across major scientific databases (PubMed, IEEE Xplore, Web of Science).
• Screening of titles, abstracts, and full texts by two independent reviewers.
• Data extraction and narrative synthesis guided by predefined research questions, with rigorous author review.

Main Results:

• 40 studies met inclusion criteria; traditional ML models showed high lab performance but declined in real-world settings.
• Deep learning (DL) architectures demonstrated robust free-living performance, with hybrid models achieving state-of-the-art results.
• Wrist-worn sensors were common, but multi-sensor configurations (e.g., wrist + hip) showed higher accuracy; algorithmic bias and lack of data sharing were key challenges.

Conclusions:

• ML and DL significantly improve MVPA monitoring by automating feature extraction and adapting to real-world variability.
• Gaps in generalizability, validation consistency, and transparency impede the translation of these technologies.
• Future research should focus on inclusive training, standardized reporting, and open science to ensure equitable AI in physical activity assessment.