State-of-the-art survey on activity recognition and classification using smartphones and wearable sensors

Author:

Chaurasia Sunita KumariORCID,Reddy S. R. N.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference139 articles.

1. Agarwal P, Alam M (2020) A lightweight deep learning model for human activity recognition on edge devices. Procedia Comput Sci 167(2019):2364–2373. https://doi.org/10.1016/j.procs.2020.03.289

2. Almaslukh B, Almuhtadi J, Artoli A (2017) An effective deep autoencoder approach for online smartphone-based human activity recognition. IJCSNS Int J Comput Sci Netw Secur 17(4):160–16 [Online]. Available: http://paper.ijcsns.org/07_book/201704/20170423.pdf

3. Almaslukh B, Artoli AM, Al-Muhtadi J (2018) A robust deep learning approach for position-independent smartphone-based human activity recognition, Sensors (Switzerland), 18(11). https://doi.org/10.3390/s18113726

4. Alshurafa N, Member S, Xu W, Liu JJ, Member S (2014) Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors. 18(5):1636–1646

5. Analog Devices, “ADXL335 - Small, Low Power, 3-Axis ±3 g Accelerometer.” p. 16, 2010, [Online]. Available: http://www.analog.com/media/en/technical-documentation/data-sheets/ADXL335.pdf

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