Performance-enhanced real-time lifestyle tracking model based on human activity recognition (PERT-HAR) model through smartphones

• Published in: The Journal of supercomputing Vol. 78; no. 4; pp. 5241 - 5268
• Main Authors: Ishwarya, K., Alice Nithya, A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Artificial neural networks; Automation; Compilers; Computer Science; Deep learning; Economic models; Human activity recognition; Human performance; Interpreters; Lifestyles; Machine learning; Mobile and Intelligent Sensing on High Performance Computing; Model accuracy; Processor Architectures; Programming Languages; Real time; Representations; Sensors; Smart buildings; Smartphones; Tracking systems; Deep learning; Long short-term memory; Human activity recognition; PERT-HAR; Convolutional neural network
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-021-04065-z

The identification of human actions and their representation and categorization in an automated system through training and learning is considered the human activity recognition (HAR) process. Tracking systems capture and read human actions ranging from the identification of plain movements to the comprehension of habits and practices. As such, HARs have found their use in areas such as health care, with a special focus on elderly patients’ care, safety arrangements and supervision areas and in applications designed for smart homes. Sensor and visual devices enable HAR, and there is a multitude of sensor classifications, such as sensors that can be worn, sensors tagged to a target and sensors tagged to the background. The automated learning methodologies in HAR are either handcrafted or deep learning or a combination of both. Handcrafted models can be regional or wholesome recognition models such as RGB, 3D mapping and skeleton data models, and deep learning models are categorized into generative models such as LSTM (long short-term memory), discriminative models such as convolutional neural networks (CNNs) or a synthesis of such models. Several datasets are available for undertaking HAR analysis and representation. The hierarchy of processes in HAR is classified into gathering information, preliminary processing, property derivation and guiding based on framed models. The proposed study considers the role of smartphones in HARs with a particular interest in keeping a tab on the lifestyle of subjects. Smartphones act as HAR devices with inbuilt sensors with custom-made applications, and the merits of both handcrafted and deep learning models are considered in framing a model that can enable lifestyle tracking in real time. This performance-enhanced real-time tracking human activity recognition (PERT-HAR) model is economical and effective in accurate identification and representation of actions of the subjects and thereby provides more accurate data for real-time investigation and remedial measures. This model achieves an accuracy of 97–99% in a properly controlled environment.