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Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments.

Afzaal Hussain^1,2, Kashif Zafar¹, Abdul Rauf Baig³

¹Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This study introduces an automated system for tracking free weight exercises using a chest-mounted accelerometer and LSTM neural networks. The approach enables accurate exercise recognition, aiding in health monitoring and personalized fitness.

Keywords:

Internet of Things (IoT)LSTM gym exercise recognition human activity recognition inertial sensor smart sensor

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Area of Science:

Sports Science
Biomedical Engineering
Machine Learning

Background:

Automated exercise tracking enhances motivation and health outcomes.
Aerobic exercise trackers are common, but free weight exercise tracking remains manual.
Accurate monitoring of weight training is crucial for balanced fitness programs.

Purpose of the Study:

To develop a novel method for recognizing various gym-based free weight exercises.
To utilize data from a single chest-mounted tri-axial accelerometer for exercise recognition.
To confirm the feasibility of an automated system for comprehensive gym exercise analysis.

Main Methods:

Data acquisition using a single chest-mounted tri-axial accelerometer.
Development and testing of Long Short-Term Memory (LSTM) neural network models for exercise recognition.
Experimentation with both single-muscle-group models and a universal model for all exercises.

Main Results:

Demonstrated the feasibility of the proposed LSTM-based approach for recognizing a wide range of free weight exercises.
Achieved promising results in distinguishing between different exercises using accelerometer data.
Validated the effectiveness of both specialized and universal exercise recognition models.

Conclusions:

The developed system offers a feasible solution for automated tracking and quantification of free weight exercises.
This technology can contribute to comprehensive monitoring and analysis of gym-based workouts.
Automated exercise recognition can enhance user experience by eliminating manual record-keeping.