Low-Cost, Edge-Cloud, End-to-End System Architecture for Human Activity Data Collection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48121-5_64
Reference10 articles.
1. Ali I, Ahmedy I, Gani A, Munir MU, Anisi MH (2022) Data collection in studies on internet of things (IoT), wireless sensor networks (WSNs), and sensor cloud (SC): similarities and differences. IEEE Access 10:33909–33931. https://doi.org/10.1109/ACCESS.2022.3161929
2. Berta R, Mazzara A, Bellotti F, De Gloria A, Lazzaroni L (2021) Edgine, a runtime system for IoT edge applications. In: Saponara S, De Gloria A (eds) Applications in electronics pervading industry, environment and society. Springer International Publishing, Cham, pp 261–266. https://doi.org/10.1007/978-3-030-66729-0_31
3. Berta R, Bellotti F, De Gloria A, Lazzaroni L (2022) Assessing versatility of a generic end‐to‐end platform for IoT ecosystem applications. Sensors 22. https://doi.org/10.3390/s22030713
4. Kos A, Umek A (2019) Wearable sensor devices for prevention and rehabilitation in healthcare: swimming exercise with real-time therapist feedback. IEEE Internet Things J 6:1331–1341. https://doi.org/10.1109/JIOT.2018.2850664
5. Alemayoh TT, Lee JH, Okamoto S (2020) A new motion data structuring for human activity recognition using convolutional neural network. In: 2020 8th IEEE RAS/EMBS international conference for biomedical robotics and biomechatronics (BioRob), pp 187–192. https://doi.org/10.1109/BioRob49111.2020.9224310
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