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Related Experiment Video

Updated: Jul 13, 2026

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

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LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing.

William Coates¹, Johan Wahlström²

¹Independent Researcher, Bath BA1 2TP, UK.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

This study introduces LEAN, a new resistance training app that uses machine learning for real-time exercise analysis. It offers advanced metrics beyond simple weight and reps, improving workout tracking.

Keywords:

exercise form classification mobile sensing repetition counting wearable computing

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Published on: February 21, 2025

Area of Science:

Sports Science
Wearable Technology
Machine Learning in Fitness

Background:

Fitness apps commonly track cardio but lack advanced features for resistance training.
Current resistance training apps primarily rely on manual user input for basic data like weight and repetitions.
There's a need for sophisticated, automated analysis in resistance exercise tracking.

Purpose of the Study:

To introduce LEAN, an iPhone and Apple Watch app for resistance training analysis.
To develop machine learning-driven features for real-time exercise form analysis and rep counting.
To incorporate advanced exercise metrics like range of motion and repetition time.

Main Methods:

Development of the LEAN app utilizing machine learning algorithms.
Implementation of lightweight inference methods for real-time feedback on resource-constrained devices (iPhone, Apple Watch).
Performance evaluation through user surveys and benchmarking against ground-truth data and commercial applications.

Main Results:

LEAN provides real-time form analysis, automatic repetition counting, and novel exercise metrics (range of motion, repetition time).
Lightweight inference enables effective on-device processing for immediate user feedback.
User survey and benchmarking data validate the app's performance and utility.

Conclusions:

The LEAN system offers a significant advancement in automated resistance training analysis.
Machine learning and efficient algorithms enable real-time, detailed exercise feedback on wearable devices.
This technology has the potential to enhance the effectiveness and accuracy of resistance training for users.