Activities of Daily Living Recognition With Binary Environment Sensors Using Deep Learning: A Comparative Study

• Published in: IEEE sensors journal Vol. 21; no. 4; pp. 5423 - 5433
• Main Authors: Wang, Aiguo, Zhao, Shenghui, Zheng, Chundi, Yang, Jing, Chen, Guilin, Chang, Chih-Yung
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Artificial neural networks; Comparative studies; Data models; Deep learning; Feature encoding; feature learning; Hidden Markov models; Human activity recognition; Human influences; Intelligent sensors; Irregular sampling; Machine learning; Mathematical analysis; Matrix algebra; Matrix methods; Moving object recognition; Segments; Sensor phenomena and characterization; Sensors; Smart buildings; smart home

The power of end-to-end deep learning techniques to automatically learn latent high-level features from raw signals has been demonstrated in numerous application fields, however, few studies systematically investigate how to properly encode the time-series firings of binary environment sensors that...