Hybrid deep learning approaches for smartphone sensor-based human activity recognition

Human Activity Recognition (HAR) has become one of the most important research fields to achieve real-time monitoring of human activities for timely decision making in various applications like fall detection, elderly care etc. Now-a-days, most people use smartphones which come with various embedded...

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Vydané v:Multimedia tools and applications Ročník 80; číslo 28-29; s. 35585 - 35604
Hlavní autori: Ghate, Vasundhara, C, Sweetlin Hemalatha
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.11.2021
Springer Nature B.V
Predmet:
1166: Advances of machine learning in data analytics and visual information processing
Acceleration
Accelerometers
Activities of daily living
Angular velocity
Artificial neural networks
Computer Communication Networks
Computer Science
Data mining
Data Structures and Information Theory
Datasets
Decision making
Decision trees
Deep learning
Fall detection
Human activity recognition
Inertial sensing devices
Machine learning
Moving object recognition
Multimedia Information Systems
Neural networks
Sensors
Smartphones
Special Purpose and Application-Based Systems
CNN
Inertial sensors
LSTM
HAR
ADL
GRU
DeepCNN-RF
ISSN:1380-7501, 1573-7721
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Popis
Shrnutí:Human Activity Recognition (HAR) has become one of the most important research fields to achieve real-time monitoring of human activities for timely decision making in various applications like fall detection, elderly care etc. Now-a-days, most people use smartphones which come with various embedded inertial sensors like accelerometer and gyroscope to monitor acceleration and angular velocity. These smartphone-based sensors have proven to be cost-effective solution in identification of activities belonging to ADL (Activities of Daily Living). Various Machine Learning, Deep learning and hybrid models have been proposed and implemented for HAR. This paper also proposes various hybrid deep learning approaches which combine Deep Neural Networks with other models like LSTM (Long Short Term Memory) Model and GRU (Gated Recurrent Unit) for effective classification of engineered features from CNN (Convolutional Neural Network) Model. A novel architecture that integrates CNN with Random Forest Classifier ( DeepCNN-RF ) is proposed to add randomness to the model. The proposed models have been tested on publicly available HAR Datasets like UCI HAR and WISDM Activity Recognition Datasets. Experimental results show that the hybrid models outperform the state-of-the-art data mining, machine learning techniques in UCI HAR and WISDM with an overall maximum accuracy of 97.77% and 98.2% respectively.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-10478-4