An Efficient Human Activity Recognition Framework Based on Wearable IMU Wrist Sensors

Ayman, Ahmed; Attalah, Omneya; Shaban, Heba

doi:10.1109/ist48021.2019.9010115

Cited by 27 publications

(16 citation statements)

References 8 publications

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“…Hsu et al presents a wearable inertial sensor network that can effectively identify 11 kinds of sports activities; however, its results are still affected by various activity datasets, and the resource consumption in the process of data transmission and processing is also high [9]. Ayman et al established a new HAR framework based on the IMU sensor, which forms an efficient HAR system [15]. Wu et al proposed a wireless vital signs monitoring system, which can be applied to daily medical care applications [16].…”

Section: Related Workmentioning

confidence: 99%

“…Tools such as machine learning (ML) and deep learning (DL) act as core learning algorithms, allowing raw data on human activity recognition to be generalized in various domains after training and testing. Wearable sensing devices typically combine embedded systems with inertial, physiological or environmental sensors to identify ambulation, exercise and daily activities, thus enabling HAR technology not only to perform monitoring and activity prediction but also to provide personalized service and decision support under certain circumstances [4,[9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Zhang

Zhu

Jiang

et al. 2021

Sensors

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Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Zhang

Zhu

Jiang

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…However, existing machine learning, especially convolutional neural network (CNN) models, induces overwhelming training data collection overhead. The problems caused by massive samples' algorithm requirement to learning for the feature are not suitable for small datasets, which greatly reduced machine learning algorithm practicability, for example, Ayman et al (2019) realized multi-activity recognition by a support vector machine (SVM) algorithm, with preprocessed activity data, and Chavarriaga et al (2013) used cluster recognition activities by naive Bayes (NB) and K-nearest neighbors (KNN) algorithm. However, these are all traditional machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

IMU Sensing–Based Hopfield Neuromorphic Computing for Human Activity Recognition

Zahid

Ansari

et al. 2022

Front. Comms. Net.

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Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy.

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“…ey also revealed remarkable success in healthcare applications such as telerehabilitation [23], health monitoring [24], and assisting people with disabilities [25,26]. Furthermore, the latest surveys [10,27,28] have proven the achievement of AI-based dermatologist tools in the automatic diagnosis and detection of SC diseases.…”

Section: Introductionmentioning

confidence: 99%

“…AI techniques have shown impressive outcomes in numerous medical fields including breast cancer diagnosis [ 13 , 14 ], brain tumors [ 15 , 16 ], gastrointestinal diseases [ 17 ], lung diseases [ 18 ], and heart complications [ 19 – 22 ]. They also revealed remarkable success in healthcare applications such as telerehabilitation [ 23 ], health monitoring [ 24 ], and assisting people with disabilities [ 25 , 26 ]. Furthermore, the latest surveys [ 10 , 27 , 28 ] have proven the achievement of AI-based dermatologist tools in the automatic diagnosis and detection of SC diseases.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Attallah

Sharkas

2021

Contrast Media & Molecular Imaging

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The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

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An Efficient Human Activity Recognition Framework Based on Wearable IMU Wrist Sensors

Cited by 27 publications

References 8 publications

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

IMU Sensing–Based Hopfield Neuromorphic Computing for Human Activity Recognition

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

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