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Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor.

Kyuwon Lee¹, Jeong-Hyun Kim¹, Hyeon Hong¹

¹Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.

Journal of Neuroengineering and Rehabilitation

|March 28, 2024

Summary

This study shows artificial intelligence (AI) can accurately classify shoulder pain rehabilitation exercises using IMU sensor data. This enables remote patient monitoring and improved feedback for effective recovery.

Keywords:

Deep learning model Deep neural networks (DNN)Exercise classification IMU sensors Machine learning Rehabilitation exercise Shoulder pain Wearable sensors

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Artificial Intelligence in Healthcare

Background:

Artificial intelligence (AI) is increasingly used in rehabilitation for monitoring exercise compliance via sensor technology.
Previous AI classification of shoulder exercises using IMU sensors was limited to pain-free subjects.
This study addresses the need to classify shoulder pain rehabilitation exercises in patients.

Purpose of the Study:

To classify 11 types of shoulder rehabilitation exercises using an AI algorithm in patients experiencing shoulder pain.
To demonstrate the feasibility of monitoring exercise compliance in a clinical population.
To validate the accuracy of AI-driven exercise classification.

Main Methods:

Collected data from 58 patients (37-82 years) with shoulder conditions like adhesive capsulitis and rotator cuff disease.
Patients performed 11 types of shoulder pain rehabilitation exercises 10 times each, wearing an IMU sensor.
Utilized Rectified Linear Unit (ReLU) and SoftMax activation functions within a deep neural network (DNN) model.

Main Results:

A deep neural network (DNN) model, employing a multilayer perceptron algorithm, was trained on the acquired sensor data.
The trained model achieved a high training accuracy of 0.975 and a test accuracy of 0.925 in classifying exercises.
Demonstrated effective classification of 11 distinct shoulder pain rehabilitation exercises.

Conclusions:

IMU sensor data, processed by AI, can accurately classify shoulder pain rehabilitation exercises, offering better patient feedback.
The developed model can support remote patient monitoring systems for exercise performance.
Deep learning holds significant potential for innovating healthcare service delivery in patient monitoring and rehabilitation.