Apu Sarder
M. Raihan
ABSTRACT
Human activity recognition (HAR) is the classification of different human activities influenced by their behavior and movements. The number of smartphone users and capacity sensors is increasing, and most users carry their phones. Smartphone data boosts HAR’s prominence and appeal. The gadget is used to recognize gestures or actions and perform specified activities in response to the information acquired by these recognition systems. Our study will examine several literary survey approaches. The KU-HAR dataset has utilized containing sensors data like accelerometers, and gyroscopes in various locations. This research’s goal is to identify human activity in smartphone sensors using machine learning classification algorithms. Sensors data from smartphone accelerometers and gyroscope identify human activity. These data samples are utilized using smartphone sensors. Different machine learning techniques such as Decision Tree, Random Forest (RF), Gradient Boosting, Gaussian NB, KNN, Logistic Regression, and SVC has been applied to classify the human activity and achieved satisfactory outcome. This research can helps researchers to establish an expert system to detect any movements of human.
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Index Terms
- Human Activity Recognition using Time Series and Machine Learning Techniques
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