New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems DOI Creative Commons
Ahmed W. Moawad,

Mohamed A. El-Khoreby,

Shereen I. Fawaz

и другие.

Applied System Innovation, Год журнала: 2025, Номер 8(2), С. 53 - 53

Опубликована: Апрель 15, 2025

This paper presents a novel Human Activity Recognition (HAR) framework using wearable sensors, specifically targeting applications in gait rehabilitation and assistive robots. The new methodology includes the usage of an open-source dataset. dataset surface electromyography (sEMG) inertial measurement units (IMUs) signals for lower limb 22 healthy subjects. Several activities daily living (ADLs) were included, such as walking, stairs up/down ramp walking. A signal conditioning, denoising, filtering, feature extraction activity classification is proposed. After testing several conditioning approaches, Wavelet transform (WT), Principal Component Analysis (PCA) Empirical Mode Decomposition (EMD), autocepstrum analysis (ACA)-based approach chosen. Such complex effective enables supervised classifiers like K-nearest neighbor (KNN), neural networks (NN) random forest (RF). classifier has shown best results with accuracy 97.63% EMG extracted from soleus muscle. Additionally, RF IMU 98.52%. These emphasize potential HAR systems rehabilitation, paving way real-time implementation devices.

Язык: Английский

Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle DOI Creative Commons
Shing-Hong Liu, Alok Kumar Sharma,

Bo-Yan Wu

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 12, 2025

Язык: Английский

Процитировано

0
New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems DOI Creative Commons
Ahmed W. Moawad,

Mohamed A. El-Khoreby,

Shereen I. Fawaz

и другие.

Applied System Innovation, Год журнала: 2025, Номер 8(2), С. 53 - 53

Опубликована: Апрель 15, 2025

This paper presents a novel Human Activity Recognition (HAR) framework using wearable sensors, specifically targeting applications in gait rehabilitation and assistive robots. The new methodology includes the usage of an open-source dataset. dataset surface electromyography (sEMG) inertial measurement units (IMUs) signals for lower limb 22 healthy subjects. Several activities daily living (ADLs) were included, such as walking, stairs up/down ramp walking. A signal conditioning, denoising, filtering, feature extraction activity classification is proposed. After testing several conditioning approaches, Wavelet transform (WT), Principal Component Analysis (PCA) Empirical Mode Decomposition (EMD), autocepstrum analysis (ACA)-based approach chosen. Such complex effective enables supervised classifiers like K-nearest neighbor (KNN), neural networks (NN) random forest (RF). classifier has shown best results with accuracy 97.63% EMG extracted from soleus muscle. Additionally, RF IMU 98.52%. These emphasize potential HAR systems rehabilitation, paving way real-time implementation devices.

Язык: Английский

Процитировано

0