Human Activity Recognition using Supervised Learning
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Abstract
Human activity recognition, or HAR for short, is a broad field of study concerned with identifying the
specific movement or action of a person based on sensor data.The sensor data may be remotely
recorded, such as video, radar, or other wireless methods. It contains data generated from
accelerometer, gyroscope and other sensors of smart phone to train supervised predictive models
using machine learning (ML) techniques like logistic regression, decision tree and support vector
machine (SVM) to generate a model. These ML techniques can be used to predict the kind of
movement being carried out by the person, which is divided into six categories walking, walking
upstairs, walking down-stairs, sitting, standing and laying.Results show that the SVM approach is a
promising alternative to activity recognition on smart phones compared to other ML techniques.
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