Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85163296576
Journal Title
8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023
Start Page
469
End Page
473
Rights Holder(s)
SCOPUS
Bibliographic Citation
8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023 (2023) , 469-473
Suggested Citation
Sanpote W., Jantawong P., Hnoohom N., Jitpattanakul A., Mekruksavanich S. Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors. 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023 (2023) , 469-473. 473. doi:10.1109/ECTIDAMTNCON57770.2023.10139507 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87878
Title
Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors
Author's Affiliation
Other Contributor(s)
Abstract
Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively.