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Human Activity Recognition using Time Series and Machine Learning Techniques

Published: 11 August 2022 Publication History

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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cover image ACM Other conferences
ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
March 2022
543 pages
ISBN:9781450397346
DOI:10.1145/3542954
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 11 August 2022

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Author Tags

  1. Algorithms
  2. HAR
  3. Predictions
  4. Sensors Data
  5. Smartphone Activity

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