Data Acquisition, Conditioning and Processing System for a Wearable-based Biostimulation

Sestrem, Leonardo; Kaizer, Raul; Gonçalves, João; Leitão, Paulo; Teixeira, João Marcelo X. N.; Lima, José; Franco, Tiago; Carvalho, José

doi:10.5220/0011002300003123

Cited by 5 publications

(2 citation statements)

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“…Considering the scenario described, the wearable systems, capable of performing the electrostimulation sessions, Ref. [ 7 ] were refactored with the addition of IMU data acquisition. To simulate the designed treatment activities, the refactored software was installed in one of the wearable modules.…”

Section: Wearable Acquisition Systemmentioning

confidence: 99%

“…In addition, the use of electromyography can be considered common in physiotherapy clinics, reducing the learning curve of professionals in interpreting the proposed treatment. As part of the NanoStim project, the implementation of a wearable system capable of acquiring EMG signals and performing electrostimulation simultaneously can be found in the article [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions

Franco

Sestrem

Henriques

et al. 2022

Sensors

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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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Section: Wearable Acquisition Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%