Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

Issa, Mohamed; Helmi, Ahmed; Al-qaness, Mohammed A. A.; Dahou, Abdelghani; Elaziz, Mohamed Abd; Damaševičius, Robertas

doi:10.3390/healthcare10061084

Cited by 34 publications

(34 citation statements)

References 61 publications

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“…The RF algorithm is one of a supervised learning method that includes a series of tree predictors, where each tree is based on the values of a randomly sampled vector with the same distribution across all trees in the forest. Thus, the results of each of these trees are calculated separately and then combined to provide a favorable prediction [ 54 , 55 ]. RF is an enhanced version of the decision tree algorithm, considering that the classification capacity of a single tree may be small.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Astolfi

Silva

Guedes

et al. 2023

Sensors

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Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.

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Section: Methodsmentioning

confidence: 99%

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Astolfi

Silva

Guedes

et al. 2023

Sensors

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“…An internet of healthcare things model is proposed in [42]. The IMU signals have been registered and filtered using Butterworth low-pass filter.…”

Section: B Imu Sensors For Outdoor-environmentsmentioning

confidence: 99%

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.

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“…Human activity recognition (HAR) is the foundation of many felds and has become a research hotspot in the past decade on account of its signifcance. At present, this technology has been widely applied in the felds of smart homes [1], indoor navigation [2], identity recognition [3], human-machine interaction [4], gait analysis [5], and the Internet of Healthcare Tings [6,7]. Te identifcation accuracy of corresponding activities has signifcant efects on these applications.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on a Modified Capsule Network

Zhu

Chen

Liu

et al. 2023

Mobile Information Systems

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Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.

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Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

Cited by 34 publications

References 61 publications

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

Human Activity Recognition Based on a Modified Capsule Network

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