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The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Human activity recognition using wearable sensors: a deep learning approach
Author: Xue, Jialun
View Online: njit-etd2020-081
(x, 65 pages ~ 0.8 MB pdf)
Department: Department of Electrical and Computer Engineering
Degree: Master of Science
Program: Computer Engineering
Document Type: Thesis
Advisory Committee: Zhou, MengChu (Committee chair)
Hou, Edwin (Committee member)
Guo, Xiwang (Committee member)
Date: 2020-12
Keywords: Human activity recognition
Deep learning
Wearable sensors
Availability: Unrestricted
Abstract:

In the past decades, Human Activity Recognition (HAR) grabbed considerable research attentions from a wide range of pattern recognition and human–computer interaction researchers due to its prominent applications such as smart home health care. The wealth of information requires efficient classification and analysis methods. Deep learning represents a promising technique for large-scale data analytics. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among them, physical human activity recognition through wearable sensors provides valuable information about an individual's degree of functional ability and lifestyle. There is abundant research that works upon real time processing and causes more power consumption of mobile devices. Mobile phones are resource-limited devices. It is a thought-provoking task to implement and evaluate different recognition systems on mobile devices.

This work proposes a Deep Belief Network (DBN) model for successful human activity recognition. Various experiments are performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN performs competitively in comparison with other algorithms and achieves satisfactory activity recognition performance. Some open problems and ideas are also presented and should be investigated as future research.


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