Multi-Sensors for Human Activity Recognition
1. Introduction
2. Overview of Contribution
Author Contributions
Funding
Conflicts of Interest
References
- Ciampi, L.; Foszner, P.; Messina, N.; Staniszewski, M.; Gennaro, C.; Falchi, F.; Serao, G.; Cogiel, M.; Golba, D.; Szczęsna, A.; et al. Bus Violence: An Open Benchmark for Video Violence Detection on Public Transport. Sensors 2022, 22, 8345. [Google Scholar] [CrossRef] [PubMed]
- Bouchabou, D.; Nguyen, S.M.; Lohr, C.; LeDuc, B.; Kanellos, I. A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning. Sensors 2021, 21, 6037. [Google Scholar] [CrossRef] [PubMed]
- Lentzas, A.; Dalagdi, E.; Vrakas, D. Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms. Sensors 2022, 22, 2353. [Google Scholar] [CrossRef] [PubMed]
- Echeverria, J.; Santos, O.C. Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation. Sensors 2021, 21, 8378. [Google Scholar] [CrossRef] [PubMed]
- Saia, R.; Podda, A.S.; Pompianu, L.; Reforgiato Recupero, D.; Fenu, G. A Blockchain-Based Distributed Paradigm to Secure Localization Services. Sensors 2021, 21, 6814. [Google Scholar] [CrossRef] [PubMed]
- Tsai, T.-H.; Tsai, Y.-R. Architecture Design and VLSI Implementation of 3D Hand Gesture Recognition System. Sensors 2021, 21, 6724. [Google Scholar] [CrossRef] [PubMed]
- Salman, N.; Khan, M.W.; Lim, M.; Khan, A.; Kemp, A.H.; Noakes, C.J. Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour. Sensors 2021, 21, 6204. [Google Scholar] [CrossRef] [PubMed]
- Stavropoulos, T.G.; Meditskos, G.; Lazarou, I.; Mpaltadoros, L.; Papagiannopoulos, S.; Tsolaki, M.; Kompatsiaris, I. Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis. Sensors 2021, 21, 6230. [Google Scholar] [CrossRef] [PubMed]
- Oh, S.; Bae, C.; Cho, J.; Lee, S.; Jung, Y. Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction. Sensors 2021, 21, 3906. [Google Scholar] [CrossRef] [PubMed]
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Tsanousa, A.; Meditskos, G.; Vrochidis, S.; Kompatsiaris, I. Multi-Sensors for Human Activity Recognition. Sensors 2023, 23, 4617. https://doi.org/10.3390/s23104617
Tsanousa A, Meditskos G, Vrochidis S, Kompatsiaris I. Multi-Sensors for Human Activity Recognition. Sensors. 2023; 23(10):4617. https://doi.org/10.3390/s23104617
Chicago/Turabian StyleTsanousa, Athina, Georgios Meditskos, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2023. "Multi-Sensors for Human Activity Recognition" Sensors 23, no. 10: 4617. https://doi.org/10.3390/s23104617
APA StyleTsanousa, A., Meditskos, G., Vrochidis, S., & Kompatsiaris, I. (2023). Multi-Sensors for Human Activity Recognition. Sensors, 23(10), 4617. https://doi.org/10.3390/s23104617