The Effects of Different Body Positions on Human Physiological Responses Using Universal Motion Simulator

Alsanwy, Shehab; Asadi, Houshyar; Alqumsan, Ahmad Abu; Mohamed, Shady; Nahavandi, Darius; Nahavandi, Saeid

doi:10.1109/smc52423.2021.9658601

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…For future work, the sensor type and location [28] can be optimised for better performance, while the potential for detailed phase recognition can be studied for the target activity [29]. Human body position [30], EEG methodologies [31] and emotion recognition [32] can be used as complementary to the aforementioned models. Furthermore, these models could be used to activate the proper control system for robotic devices [33]- [37].…”

Section: Resultsmentioning

confidence: 99%

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

Nazari

Nahavandi

Mohajer

et al. 2022

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

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Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user's skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracy.

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Section: Resultsmentioning

confidence: 99%

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

Nazari

Nahavandi

Mohajer

et al. 2022

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

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“…For future work and directions, we suggest to implementing and asses these methods on simalution platforms [44] and robotics [45], [46]. Furthermore, they can be paired with haptic feedback [47] and emotion recognition methodologies [48], body position [49],…”

Section: Discussionmentioning

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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Cybersickness Measurement and Evaluation During Flying a Helicopter in Different Weather Conditions in Virtual Reality

Asadi

Mohamed

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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The Effects of Different Body Positions on Human Physiological Responses Using Universal Motion Simulator

Cited by 9 publications

References 34 publications

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

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

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

Cybersickness Measurement and Evaluation During Flying a Helicopter in Different Weather Conditions in Virtual Reality

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