Squat exercise abnormality detection by analyzing joint angle for knee osteoarthritis rehabilitation

Mohd Fadzil Abu Hassan, Mohd Asyraf Zulkifley, Aini Hussain

Research output: Contribution to journalArticle

Abstract

Normally, osteoarthritic knee patients experienced 1) difficulties controlling their fine motors, 2) lack of muscle strength, and 3) limited range of motion. The limitations can be improved by physiotherapy exercises to 1) enhance flexibility and mobility of joints and 2) increase strength and endurance of the muscles. However, the patients should be individually monitored so that the exercises are performed correctly, effectively and efficiently. This paper focuses on squat exercise monitoring for knee osteoarthritis rehabilitation. The patient’s movement is captured by using a low cost 3D camera, Kinect sensor for skeletal tracking to recognize and track people without using marker. 3D coordinates of each joint is retrieved from the skeleton data, where a joint angle is derived based on two intersecting human body segments. Time series of the joint angles during the squat exercise are recorded, which are then smoothed by Double Exponential Smoothing technique to find the variability between them. The proposed method is validated by using simulated videos of squat exercise performed by 10 healthy volunteers of various physiques and gender to simulate the normal and abnormal conditions. Mean Squared Error (MSE) is calculated between the measured and smoothed angles to classify the movement either normal or abnormal. The parameters for smoothing and trend control used are 0.8928 and 0.7256, respectively, which are derived based on optimal MSE of the 10 volunteers. The simulation results show that the average MSE for each 10 samples of normal and abnormal conditions are 3.1358 and 10.5205, respectively. Hence, a simple threshold method has been developed to detect movement abnormality while doing squat exercise.

Original languageEnglish
Pages (from-to)19-24
Number of pages6
JournalJurnal Teknologi
Volume77
Issue number7
DOIs
Publication statusPublished - 2015

Fingerprint

Patient rehabilitation
Muscle
Physical therapy
Time series
Durability
Cameras
Monitoring
Sensors
Costs

Keywords

  • Double exponential smoothing
  • Kinect sensor
  • Osteoarthritis
  • Rehabilitation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Squat exercise abnormality detection by analyzing joint angle for knee osteoarthritis rehabilitation. / Abu Hassan, Mohd Fadzil; Zulkifley, Mohd Asyraf; Hussain, Aini.

In: Jurnal Teknologi, Vol. 77, No. 7, 2015, p. 19-24.

Research output: Contribution to journalArticle

@article{0c74e3eec90a4bfcac164911cc90847d,
title = "Squat exercise abnormality detection by analyzing joint angle for knee osteoarthritis rehabilitation",
abstract = "Normally, osteoarthritic knee patients experienced 1) difficulties controlling their fine motors, 2) lack of muscle strength, and 3) limited range of motion. The limitations can be improved by physiotherapy exercises to 1) enhance flexibility and mobility of joints and 2) increase strength and endurance of the muscles. However, the patients should be individually monitored so that the exercises are performed correctly, effectively and efficiently. This paper focuses on squat exercise monitoring for knee osteoarthritis rehabilitation. The patient’s movement is captured by using a low cost 3D camera, Kinect sensor for skeletal tracking to recognize and track people without using marker. 3D coordinates of each joint is retrieved from the skeleton data, where a joint angle is derived based on two intersecting human body segments. Time series of the joint angles during the squat exercise are recorded, which are then smoothed by Double Exponential Smoothing technique to find the variability between them. The proposed method is validated by using simulated videos of squat exercise performed by 10 healthy volunteers of various physiques and gender to simulate the normal and abnormal conditions. Mean Squared Error (MSE) is calculated between the measured and smoothed angles to classify the movement either normal or abnormal. The parameters for smoothing and trend control used are 0.8928 and 0.7256, respectively, which are derived based on optimal MSE of the 10 volunteers. The simulation results show that the average MSE for each 10 samples of normal and abnormal conditions are 3.1358 and 10.5205, respectively. Hence, a simple threshold method has been developed to detect movement abnormality while doing squat exercise.",
keywords = "Double exponential smoothing, Kinect sensor, Osteoarthritis, Rehabilitation",
author = "{Abu Hassan}, {Mohd Fadzil} and Zulkifley, {Mohd Asyraf} and Aini Hussain",
year = "2015",
doi = "10.11113/jt.v77.6241",
language = "English",
volume = "77",
pages = "19--24",
journal = "Jurnal Teknologi",
issn = "0127-9696",
publisher = "Penerbit Universiti Teknologi Malaysia",
number = "7",

}

TY - JOUR

T1 - Squat exercise abnormality detection by analyzing joint angle for knee osteoarthritis rehabilitation

AU - Abu Hassan, Mohd Fadzil

AU - Zulkifley, Mohd Asyraf

AU - Hussain, Aini

PY - 2015

Y1 - 2015

N2 - Normally, osteoarthritic knee patients experienced 1) difficulties controlling their fine motors, 2) lack of muscle strength, and 3) limited range of motion. The limitations can be improved by physiotherapy exercises to 1) enhance flexibility and mobility of joints and 2) increase strength and endurance of the muscles. However, the patients should be individually monitored so that the exercises are performed correctly, effectively and efficiently. This paper focuses on squat exercise monitoring for knee osteoarthritis rehabilitation. The patient’s movement is captured by using a low cost 3D camera, Kinect sensor for skeletal tracking to recognize and track people without using marker. 3D coordinates of each joint is retrieved from the skeleton data, where a joint angle is derived based on two intersecting human body segments. Time series of the joint angles during the squat exercise are recorded, which are then smoothed by Double Exponential Smoothing technique to find the variability between them. The proposed method is validated by using simulated videos of squat exercise performed by 10 healthy volunteers of various physiques and gender to simulate the normal and abnormal conditions. Mean Squared Error (MSE) is calculated between the measured and smoothed angles to classify the movement either normal or abnormal. The parameters for smoothing and trend control used are 0.8928 and 0.7256, respectively, which are derived based on optimal MSE of the 10 volunteers. The simulation results show that the average MSE for each 10 samples of normal and abnormal conditions are 3.1358 and 10.5205, respectively. Hence, a simple threshold method has been developed to detect movement abnormality while doing squat exercise.

AB - Normally, osteoarthritic knee patients experienced 1) difficulties controlling their fine motors, 2) lack of muscle strength, and 3) limited range of motion. The limitations can be improved by physiotherapy exercises to 1) enhance flexibility and mobility of joints and 2) increase strength and endurance of the muscles. However, the patients should be individually monitored so that the exercises are performed correctly, effectively and efficiently. This paper focuses on squat exercise monitoring for knee osteoarthritis rehabilitation. The patient’s movement is captured by using a low cost 3D camera, Kinect sensor for skeletal tracking to recognize and track people without using marker. 3D coordinates of each joint is retrieved from the skeleton data, where a joint angle is derived based on two intersecting human body segments. Time series of the joint angles during the squat exercise are recorded, which are then smoothed by Double Exponential Smoothing technique to find the variability between them. The proposed method is validated by using simulated videos of squat exercise performed by 10 healthy volunteers of various physiques and gender to simulate the normal and abnormal conditions. Mean Squared Error (MSE) is calculated between the measured and smoothed angles to classify the movement either normal or abnormal. The parameters for smoothing and trend control used are 0.8928 and 0.7256, respectively, which are derived based on optimal MSE of the 10 volunteers. The simulation results show that the average MSE for each 10 samples of normal and abnormal conditions are 3.1358 and 10.5205, respectively. Hence, a simple threshold method has been developed to detect movement abnormality while doing squat exercise.

KW - Double exponential smoothing

KW - Kinect sensor

KW - Osteoarthritis

KW - Rehabilitation

UR - http://www.scopus.com/inward/record.url?scp=84947051679&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947051679&partnerID=8YFLogxK

U2 - 10.11113/jt.v77.6241

DO - 10.11113/jt.v77.6241

M3 - Article

AN - SCOPUS:84947051679

VL - 77

SP - 19

EP - 24

JO - Jurnal Teknologi

JF - Jurnal Teknologi

SN - 0127-9696

IS - 7

ER -