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• W4385957043 abstract "The HAR refers to a system that monitors human behaviors and activities by collecting data from a wide range of distinct kinds of wearable sensors. This system is known as human activity recognition. HAR enables in improving Health, Quality of life, Biomedical, and Mental Health. Recently, there has been an increase in demand for a range of HAR techniques, including those that make use of deep learning, machine learning, and CNN algorithms. Specifically, this need has been on the rise in the United States. In the most recent few years, this field of research has achieved major advancements, and there is still a substantial amount of research being carried out. Human activity recognition (HAR) has seen tremendous growth in the disciplines of pervasive computing, human–computer interaction, and human behavior analysis. Recently, machine learning (ML) algorithms have proven effective to predict a variety of human actions using time-series data from wearable sensors and cell phones. Even though ML-based techniques were excellent at identifying activities, managing time series data remains difficult. Time-series data still has a lot of problems, including hard feature extraction, extremely biased data, etc. Furthermore, manual feature engineering is a key component of the bulk of HAR approaches. Since each measurement made by wearable sensors like gyroscopes and accelerometers has a timestamp associated with it, time series data is the format utilized for data on human activity. The raw sensor data must be processed to obtain the necessary temporal properties for HAR. In-depth feature engineering and data pre-processing are required for the majority of HAR approaches, which in turn necessitates subject-matter expertise. These techniques are time-consuming and application-specific. This paper begins with an in-depth discussion of HAR, then moves on to a discussion of the various machine learning methods, and then concludes with a discussion of wearable sensors and the applications that can be used with them." @default.
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• W4385957043 date "2023-01-01" @default.
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• W4385957043 title "Machine Learning Techniques for Human Activity Recognition Using Wearable Sensors" @default.
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