Multi-input CNN-GRU based human activity recognition using wearable sensors

Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope an...

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Veröffentlicht in:Computing Jg. 103; H. 7; S. 1461 - 1478
Hauptverfasser: Dua, Nidhi, Singh, Shiva Nand, Semwal, Vijay Bhaskar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.07.2021
Springer Nature B.V
Schlagworte:
Accelerometers
Artificial Intelligence
Artificial neural networks
Classification
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Feature extraction
Human activity recognition
Information Systems Applications (incl.Internet)
Moving object recognition
Neural networks
Regular Paper
Sensors
Software Engineering
Wearable technology
Long short term memory (LSTM)
CNN
Deep neural networks
68W
Primary Classification: 68T
GRU
Human activity recognition
62H
15A
ISSN:0010-485X, 1436-5057
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Zusammenfassung:Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope and accelerometer is in the form of time series data, as each reading has a timestamp associated with it. For HAR, it is important to extract the relevant temporal features from raw sensor data. Most of the approaches for HAR involves a good amount of feature engineering and data pre-processing, which in turn requires domain expertise. Such approaches are time-consuming and are application-specific. In this work, a Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well. The experiments in this work were carried out using the raw data obtained from wearable sensors with nominal pre-processing and don’t involve any handcrafted feature extraction techniques. The accuracies obtained on UCI-HAR, WISDM, and PAMAP2 datasets are 96.20%, 97.21%, and 95.27% respectively. The results of the experiments establish that the proposed model achieved superior classification performance than other similar architectures.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-021-00928-8