Wearable Sensors for Human Activity Monitoring: A Review

By Subhas Chandra Mukhopadhyay. IEEE Sensors Journal, 2015

1,285 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b866362af6313291354f467bd5679483c6bd9a0c

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In Biomedical Engineering

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Citations in the corpus

A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption (2017, background): “The increasing usage of WPM systems will lead to the generation of a huge streams of new data sources, which will ultimately amplify the data-oriented medical knowledge base and, will complement and improve the electronic health record [32].”
Embracing Complexity: Reviewing the Past Decade of Team Effectiveness Research (2019, positive): “…is increasing in importance, with more sophisticated CATA algorithms consistently emerging, such as scoring multiword phrases and deriving semantic meanings from larger passages (e.g., Carley et al. 2013). propinquity and their body movements, as well as their speech patterns (Mukhopadhyay 2015).”
Wireless Sensing for Human Activity: A Survey (2020, background): “sensing technologies, including motion sensors [1], visionbased sensors [2], acoustic-based sensors [3] and pyroelectric infrared (PIR) sensors [4], are deployed to inspect different”
Improving predicted mean vote with inversely determined metabolic rate (2020, background): “But, their accuracy requires to be improved for thermal comfort evaluation [32].”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, background): “Mukhopadhyay [203] provides a recent review of wear-”
Wearable Environmental Sensors and Infrastructure for Mobile Large-Scale Urban Deployment (2016, background): “In particular, the survey in [11] explores various communication protocols on their suitability for monitoring human activities continuously.”
IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment (2019, methodology): “connectivity on body area networks (BANs) [15], with the sensor connected through a low power protocol (e.”