A Comparative Research on Human Activity Recognition Using Deep Learning

Tufek, Nilay; Ozkaya, Ozen

doi:10.1109/siu.2019.8806395

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…Deep learning is a research direction in the field of ML that accomplishes more complex classification tasks through feature learning [25][26][27]. DL emphasizes the depth of the model structure and clarifies the importance of feature learning.…”

Section: Deep Learningmentioning

confidence: 99%

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: Deep Learningmentioning

confidence: 99%

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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“…Important recent studies of HAR [17] have revealed certain problems associated with conventional machine learning techniques which ultimately influence the ability to recognize human activity. This limitation concerns the choice of hand-crafted features since the selection is dependent upon the skills and knowledge of the person taking the decisions [26].…”

Section: Deep Learning With Harmentioning

confidence: 99%

“…Today's greater availability of wearable technology has brought about increased interest in HAR in order to bring about benefits for people's health and well-being [16]. During the past five years there has been a notable increase in the number of research papers published in this particular field [17], with a majority of these studies focusing on the applications of HAR in conventional machine learning (ML) models. Such models typically employ ML algorithms such as support vector machines, naive Bayes, decision trees, hidden Markov, and k-nearest neighbor models.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs

et al. 2020

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The creation of the Internet of Things (IoT), along with the latest developments in wearable technology, has provided new opportunities in human activity recognition (HAR). The modern smartwatch offers the potential for data from sensors to be relayed to novel IoT platforms, which allow the constant tracking and monitoring of human movement and behavior. Recently, traditional activity recognition techniques have done research in advance by choosing machine learning methods such as artificial neural network, decision tree, support vector machine, and naive Bayes. Nonetheless, these conventional machine learning techniques depend inevitably on heuristically handcrafted feature extraction, in which human domain knowledge is normally limited. This work proposes a hybrid deep learning model called CNN-LSTM that employed Long Short-Term Memory (LSTM) networks for activity recognition with the Convolution Neural Network (CNN). The study makes use of HAR involving smartwatches to categorize hand movements. Using the study based on the Wireless Sensor Data Mining (WISDM) public benchmark dataset, the recognition abilities of the deep learning model can be accessed. The accuracy, precision, recall, and F-measure statistics are employed using the evaluation metrics to assess the recognition abilities of LSTM models proposed. The findings indicate that this hybrid deep learning model offers better performance than its rivals, where the achievement of 96.2% accuracy, while the f-measure is 96.3%, is obtained. The results show that the proposed CNN-LSTM can support an improvement of the performance of activity recognition.

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“…Many authors have attempted to detect abnormal behaviour in overcrowded environments using texturebased information, such as time gradients [4], dynamic texture characteristics [5] and the spatiotemporal frequency properties [6], [7]. Other groups concentrate on optical flows, which recognize motion features in video frames directly, such as multi-scale pedestrian features [8], fuzzy clustering based features [9], behavioural model for pedestrian detection [10], convolutional neural networks (CNN) features [11], weighted autoencoder based features [12], trajectory based features [13], student object behavioral features [14], multi-target association based features [15], [16]. Previous research has shown that the technique of motion is beneficial, and we believe that the present methods can still be improved.…”

Section: Introductionmentioning

confidence: 99%

Novel approach for pedestrian unusual activity detection in academic environment

Hajari

Gawande

Golhar

2022

IJ-AI

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<span>In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, velocity, and direction, as well as their intercommunication within a frame. We also use these motion features to detect both global and local anomalous behaviors within the frame. The proposed approach is validated on a newly built proposed student behavior database and three additional publicly available benchmark datasets. When compared to state-of-the-art techniques, the experimental results reveal a considerable performance improvement in anomalous activity recognition. Finally, we summarize and discuss future research directions.</span>

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A Comparative Research on Human Activity Recognition Using Deep Learning

Cited by 17 publications

References 15 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

Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs

Novel approach for pedestrian unusual activity detection in academic environment

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