Performance Improvement of Human Activity Recognition based on Ensemble Empirical Mode Decomposition (EEMD)

Sinuraya, Enda Wista; Rizal, Aminuddin; Soetrisno, Yosua Alvin Adi; Denis, Denis

doi:10.1109/icitacee.2018.8576916

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…The modal decomposition can imply innovative features for recognizing the various motions . The EMD assisted in extracting the frequency-domain features, which could comprise local characteristics for the allowable ROM due to various subjects, for the modeling [ 49 ]. The model enabled the clinical practice to explore the same exercise pattern to recognize different people’s movements.…”

Section: Discussionmentioning

confidence: 99%

AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

Lai

Kan

Lin

et al. 2021

Sensors

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Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.

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Section: Discussionmentioning

confidence: 99%

AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

Lai

Kan

Lin

et al. 2021

Sensors

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“…Then time and frequency domain features are extracted by applying Hilbert-Huang Transform. [2] In this, wearable devices and smartphone are widely used for detecting human activity recognition. In this paper they have used CNN-LSTM network which tackles all the activities which are captured by digital devices they have used.…”

Section: IImentioning

confidence: 99%

Review on Human Activity Recognition for Military Restricted Areas

Patil¹,

Shelke²,

Joldapke³

et al. 2022

IJRASET

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Human Activity Recognition(HAR) is an active field of research and scientific development in which various models have been proposed using different methods for identification and categorization of activities using Machine Learning. HAR has reached a remarkable milestone in the area of computer vision. Except for applications in human-computer interactions, surveillance systems and robotics, lately it has extended its applicability in the fields of healthcare, multimedia retrieval, social networking, and education as well. It aims to bring latest technologies together to develop complex assistive system with adaptive capability and learning behaviour. HAR interprets human motion using computer and machine vision technologies to identify and detect simple and complex actions in real-world. This paper presents research made for surveillance of restricted military areas. Our scope is to develop a live monitoring system for tracking the illegal activities done in the restricted area for border security, which is an issue of concern since decades. In this we have introduced a deep learning model that learns to classify human actions without having prior knowledge. The features of image or video set are extracted and detected for detected for classifying whether the activity is illegal or not. Many harmful actions can be avoided or at least have their negative effects reduced as a result of the adoption of this concept. Finally, the activity recognition rate showed a good performance as a result of these findings

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“…Hilbert Huang Transform (HHT) is considered to be very useful for non-linear and varied signal data [35]. In our case, three inertial sensors are attached to the human body.…”

Section: ) Hilbert Huang Transformmentioning

confidence: 99%

Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors

et al. 2021

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The monitoring of human physical activities using wearable sensors, such as inertial-based sensors, plays a significant role in various current and potential applications. These applications include physical health tracking, surveillance systems, and robotic assistive technologies. Despite the wide range of applications, classification and recognition of human activities remains imprecise and this may contribute to unfavorable reactions and responses. To improve the recognition of human activities, we designed a dataset in which ten participants (five male and five female) performed 11 different activities wearing three bodyworn inertial sensors in different locations on the body. Our model extracts data via a hierarchical featurebased technique. These features include time, wavelet, and time-frequency domains, respectively. Stochastic gradient descent (SGD) is then introduced to optimize selective features. The selected features with optimized patterns are further processed by multi-layered kernel sliding perceptron to develop adaptive learning for the classification of physical human activities. Our proposed model was experimentally evaluated and applied on three benchmark datasets: IM-WSHA, a self-annotated dataset, PAMAP2 dataset which is comprised of daily living activities, and an HuGaDB, a dataset which contains physical activities for aging people. The experimental results show that the proposed method achieves better results and outperforms others in terms of recognition accuracy, achieving an accuracy rate of 83.18%, 94.16%, and 92.50% respectively, when IM-WSHA, PAMAP2, and HuGaDB datasets are applied.INDEX TERMS body-worn sensors, kernel sliding perceptron, real-time personal locomotion behaviors (RPLB), stochastic gradient descent

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Performance Improvement of Human Activity Recognition based on Ensemble Empirical Mode Decomposition (EEMD)

Cited by 9 publications

References 10 publications

AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

Review on Human Activity Recognition for Military Restricted Areas

Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors

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