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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

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Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms.

Justyna Patalas-Maliszewska^1,2, Iwona Pajak¹, Pascal Krutz²

¹Institute of Mechanical Engineering, University of Zielona Góra, 65-417 Zielona Gora, Poland.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces a physical activity advisory system using inertial sensors and machine learning to accurately track exercises like squats and pull-ups. The system effectively recognizes activities and counts repetitions, aiding correct exercise implementation.

Keywords:

anchors convolutional neural network (CNN)fitness tracking mobile sensors (tags)personal training post-processing block (PPB)repetition counting sport activities sport activity advisory system

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Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Area of Science:

Sports Science and Technology
Biomedical Engineering
Machine Learning Applications

Background:

Correct implementation of sport exercises is crucial for effectiveness and injury prevention.
Existing methods for monitoring exercise form and repetitions can be subjective or lack precision.
Inertial sensors and machine learning offer a potential solution for objective exercise analysis.

Purpose of the Study:

To develop a physical activity advisory system using inertial sensors and machine learning.
To support the correct implementation of sport exercises, including squats, pull-ups, and dips.
To create modules for activity recognition and repetition counting.

Main Methods:

Utilized three mobile sensors (tags), six stationary anchors, and a server (gateway).
Developed an activity recognition module (ARM) and a repetition-counting module (RCM).
Employed convolutional neural networks (CNN) with a post-processing block (PPB), processing data using overlapping and non-overlapping windows with raw and normalized data.

Main Results:

The activity recognition module achieved high accuracy, with the best performance at 0.92 using an overlapping window and raw data.
Repetition counting accuracy reached 0.93 within an error of ±1 repetition and 0.97 within ±2 repetitions.
The system demonstrated satisfactory performance across various data processing techniques.

Conclusions:

The developed system effectively supports the correct implementation of sport exercises.
The proposed solution, integrating inertial sensors and machine learning, shows significant potential for accurate activity recognition and repetition counting.
Future implementation as a web application for real-time user sport activity detection is feasible.