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Recognition and Repetition Counting for ComplexPhysical Exercises with Deep Learning.

Andrea Soro¹, Gino Brunner², Simon Tanner³

¹ETH Zurich, Department of Information Technology and Electrical Engineering, 8092 Zurich, Switzerland. soroa@ethz.ch.

Sensors (Basel, Switzerland)

|February 13, 2019

Summary

This summary is machine-generated.

This study introduces a deep learning method for smartwatch-based activity recognition, achieving 99.96% accuracy for CrossFit exercises. The system also accurately counts exercise repetitions, demonstrating its versatility in human activity recognition.

Keywords:

deep learning exercise classification har human activity recognition imu repetition counting smartwatch sports analysis

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Area of Science:

Computer Science
Biomedical Engineering
Sports Science

Background:

Human activity recognition (HAR) is crucial for health monitoring and performance analysis.
Leveraging wearable sensors like smartwatches offers a non-intrusive method for data collection.
Accurate classification of complex physical activities and precise repetition counting remain challenging.

Purpose of the Study:

To develop an end-to-end deep learning approach for activity recognition using smartwatch sensor data.
To apply and evaluate the method on complex full-body exercises, specifically within the context of CrossFit.
To investigate the capability of the same model for accurate exercise repetition counting.

Main Methods:

An end-to-end deep learning model was designed to process raw sensor data from off-the-shelf smartwatches.
The model was trained and validated using data from 10 distinct CrossFit exercises.
The approach was assessed for both activity classification accuracy and repetition counting precision.

Main Results:

The deep learning model achieved a highly accurate classification rate of 99.96% for the 10 complex CrossFit exercises.
The same neural network demonstrated a novel and effective approach to repetition counting.
The repetition counting achieved an error of within 1 repetition for 91% of the performed sets.

Conclusions:

The proposed deep learning framework provides a highly accurate and versatile solution for activity recognition and repetition counting using smartwatches.
This approach has significant potential for applications in fitness tracking, sports analytics, and remote health monitoring.
The study highlights the effectiveness of deep learning in extracting meaningful insights from raw sensor data for complex human activities.