Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions

The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the pati...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7605
Main Authors: Franco, Tiago, Sestrem, Leonardo, Henriques, Pedro Rangel, Alves, Paulo, Varanda Pereira, Maria João, Brandão, Diego, Leitão, Paulo, Silva, Alfredo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2022
MDPI
Subjects:
Accuracy
algorithmic complexity
Arthritis
Biomechanics
Electromyography
Gait
Human mechanics
IMU sensor
knee angle
Literature reviews
Metabolism
Mobile communications networks
muscle rehabilitation
Older people
Osteoarthritis
Patients
Physical therapy
Rehabilitation
Sensors
Wearable computers
wearable system
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O(1) were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22197605