Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Yousefi, Bardia; Loo, Chu Kiong

doi:10.3390/electronics8101169

Cited by 6 publications

(3 citation statements)

References 181 publications

(309 reference statements)

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“…There are five main tasks performed by HAR, as shown in Figure 1a, namely recognition of basic activities [14], recognition of daily activities [15], recognition of unusual events [16], identification of biometric subjects [17], and prediction of energy expenditures [18]. As illustrated in Figure 1b, various sensors are employed for the performance of these tasks, such as video cameras, circumstantial sensors that measure temperature, relative humidity, light, pressure, and wearable sensors.…”

Section: Human Activity Recognition Via Machine Learning and Deep Leamentioning

confidence: 99%

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

2021

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Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.

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Section: Human Activity Recognition Via Machine Learning and Deep Leamentioning

confidence: 99%

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

2021

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“…These include: by specialised approach [13]; by algorithm type [14], by sensor type [1,15,16]; by fuse type [17] or by device type [18], although other analyses have been carried out more generally by the HAR categories [19,20]. HAR accomplished five primary tasks, namely the recognition of the fundamental activities [21], the recognition of everyday activities [22], uncommon events [23], biometric subjects [24], and energy expenditure predictions [25]. Different sensors such as video cameras, ambient temperature sensors, relative humidity, light, pressure and wearable sensors are used.…”

Section: Related Workmentioning

confidence: 99%

Study on Temperature (τ) Variation for SimCLR based Activity Recognition

Kumar¹

2021

Preprint

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Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

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“…This study had the main focus on HAR in indoor atmospheres. The indoor HAR systems gain more significance in numerous fields, like body motion analysis in sports, assisted living, and healthcare, monitoring safety (injuries, collisions, and falls) in the IoT environments, biometric user identification for security, assessing employee performances in smart factories for Industry 4.0, and wellbeing in smart homes [2]. Activity recognition was an important indicator of lifestyle, participation, and quality of life.…”

Section: Introductionmentioning

confidence: 99%

Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

Tahir¹,

Ageed²,

Hasan³

et al. 2023

Computers, Materials &Amp; Continua

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Traditional indoor human activity recognition (HAR) is a timeseries data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning (DL). The study develops a Modified Wild Horse Optimization with DL Aided Symmetric Human Activity Recognition (MWHODL-SHAR) model. The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities, namely jogging, walking, standing, sitting, etc. In the presented MWHODL-SHAR technique, the human activities data is pre-processed in various stages to make it compatible for further processing. A convolution neural network with an attention-based long short-term memory (CNN-ALSTM) model is applied for activity recognition. The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm. The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset. An extensive comparison study revealed the betterment of the MWHODL-SHAR technique over other recent approaches.

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Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Cited by 6 publications

References 181 publications

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

Study on Temperature (τ) Variation for SimCLR based Activity Recognition

Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

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