Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2023.2290736
HARNet: automatic recognition of human activity from mobile health data using CNN and transfer learning of LSTM with SVM
R. Anandha Praba
; Department of ECE, Meenakshi College of Engineering, Chennai, India
*
L. Suganthi
; Department of BME, SSN College of Engineering, Chennai, India
* Dopisni autor.
Sažetak
Human Activity Recognition (HAR) system is analysing human behaviour using mobile health
technology. Mobile Health data (MHEALTH) uses electronic devices to collect data and identify
the activity of the patient in real-time. Recordings of 10 patients’ vital signs from various circumstances are included in the dataset. With a sensor attached to their bodies, they were required
to carry out a number of physical tasks. Due to the lack of accuracy in the other state-of-theart algorithms, we proposed Human Activity Recognition Neural Network (HARNet) architecture
for automatic recognition of human activity using CNN and LSTM with the transfer learning of
SVM. Here, the human health behaviour was analysed and classified using different ML and DL
algorithms. The hybrid techniques of CNN and LSTM are selected across the different DL algorithms and it is used to extract independent and discriminating features, which aids the SVM
classifier to attain good classification. When compared to other DL methods, HARNet performed
better, achieving 99.8% accuracy. Overall, HAR systems have many potential applications in various fields, including healthcare, wellness, sports and surveillance. They have relationships to
many different academic disciplines, including sociology, human–computer interaction, medical
and may offer individualized help for different domains.
Ključne riječi
Human Activity Recognition; Mobile Health data (MHEALTH); Convolutional Neural Network (CNN); Long Short Term Memory (LSTM); Support Vector machine (SVM); deep learning
Hrčak ID:
322959
URI
Datum izdavanja:
10.12.2023.
Posjeta: 0 *