Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals

Nazari, Farhad; Nahavandi, Darius; Mohajer, Navid; Khosravi, Abbas

doi:10.1109/smc53654.2022.9945472

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…In another study, researchers compared different deep-learning techniques for HAR using ankle inertial signals [19]. They proposed several models based on artificial neural networks and achieved an average classification accuracy of up to 92.8%.…”

Section: Introductionmentioning

confidence: 99%

“…By utilizing statistical feature extraction, the authors effectively discriminated between static and dynamic activities. They employed Random Forest (RF) and CNN for specific activity classification, achieving high accuracy while reducing computational and memory consumption [19]. Another research effort explored the use of logistic regression with hyperparameter addition for HAR using smartphone sensors [20].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

Shakerian,

Douet,

Shoaraye Nejati

et al. 2023

Sensors

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This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject’s chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer’s activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

Shakerian,

Douet,

Shoaraye Nejati

et al. 2023

Sensors

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“…Various methods can be implemented for detecting gait phase by training processed input signals. Lately, artificial neural networks (ANNs) ( Choi et al, 2022 ) and deep learning (DL) ( Nazari et al, 2022 ) techniques have seen a marked increase in popularity, driven by improvements in computational power and data accessibility. Prominent examples of these techniques include the convolutional neural network (CNN) ( Shi et al, 2022 ), long short-term memory (LSTM) ( Tran et al, 2021 ), and CNN-LSTM ( Zhu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of gait phases based on a machine learning approach using muscle synergy

Park

Han

Sung

et al. 2023

Front. Hum. Neurosci.

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The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

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“…In recent years, with the increase in computation power and data availability, Artificial Neural Networks (ANN) like Shallow Learning [31] and Deep Learning (DL) [32] techniques have gained popularity. Multi-layer structures of DL networks help extract higher-level features from the input signal.…”

Section: Introductionmentioning

confidence: 99%

Comparison of gait phase detection using traditional machine learning and deep learning techniques

Nazari

Mohajer

Nahavandi

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Human walking is a complex activity with a high level of cooperation and interaction between different systems in the body. Accurate detection of the phases of the gait in realtime is crucial to control lower-limb assistive devices like exoskeletons and prostheses. There are several ways to detect the walking gait phase, ranging from cameras and depth sensors to the sensors attached to the device itself or the human body. Electromyography (EMG) is one of the input methods that has captured lots of attention due to its precision and time delay between neuromuscular activity and muscle movement. This study proposes a few Machine Learning (ML) based models on lower-limb EMG data for human walking. The proposed models are based on Gaussian Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA) and Deep Convolutional Neural Networks (DCNN). The traditional ML models are trained on hand-crafted features or their reduced components using Principal Component Analysis (PCA). On the contrary, the DCNN model utilises convolutional layers to extract features from raw data. The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model. The highest achieved accuracy in 50 trials of the training DL model is 89.5%.

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Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals

Cited by 6 publications

References 41 publications

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

Classification of gait phases based on a machine learning approach using muscle synergy

Comparison of gait phase detection using traditional machine learning and deep learning techniques

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