SENSOR BASED REAL-TIME HUMAN ACTIVITY RECOGNITION IN WIRELESS MULTIMEDIA SENSOR NETWORK
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Date
2021-07
Authors
Almakhan, Serik
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Human Activity Recognition (HAR) can be widely used in medicine and military
applications. Primarily, it can be used as an assistive technology for the healthcare
of older people. In general, HAR can be performed using the data collected from
the various type of sensors: body, object and ambient sensors. There are two types
of sensors: external and internal. A typical example of applications, which use external
sensors, is intelligent home implementations that contain many sensors, such
as temperature, pressure, light, ultrasonic sensors, and cameras. In this study, data
from internal sensors were used for HAR of activities including walking, walking up
or downstairs, sitting, standing, and falling from the collected data of smartphones.
In addition, all the HAR models experimented with by Wireless Multimedia Sensor
Network (WMSN) with the real-time collected dataset. For the recognition, we use
various neural network algorithms as CNN, LSTM and traditional Machine learning
classification algorithms such as SVM, KNN, and Random Forest Classifier. In
addition, we implemented dimensionality reduction to decrease the number of features,
which helped reduce computational time and energy consumption, and transfer
learning with different scenarios to increase accuracy, and all these functions are
implemented and compared in two WMSN architectures.
Description
Keywords
WMSN, HAR, Type of access: Gated Access, Human Activity Recognition, KNN
Citation
Almakhan, S. (2021). Sensor based Real-time Human Activity Recognition in Wireless Multimedia Sensor Network (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan