A Survey on Activity Detection and Classification Using Wearable Sensors

By Maria Cornacchia, Koray Ozcan, Yu Zheng, Senem Velipasalar. IEEE Sensors Journal, 2017

378 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/46c73ac90c8107e9580950eef9df6834edccd183

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Hanyu Wang
2. Kun Xia
3. Jianguang Huang

In Computer Vision and Pattern Recognition

1. Hanyu Wang
2. Kun Xia
3. Jianguang Huang

Citations in the corpus

• LSTM-CNN Architecture for Human Activity Recognition (2020, background): “According to the manner in which sensors are employed in an environment, the technologies of human activity recognition could be widely divided into two categories: approaches based on fixed sensors and approaches based on mobile sensors [6].”
• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, neutral): “[33] 2016 Wearable based HAR Wearable, sensors, survey, activity detection, activity classification, monitoring 5 2005/2016 225”
• Parallel spatio-temporal attention-based TCN for multivariate time series prediction (2021, background): “No area of activity escapes our desire to prepare, profit or prevent through forecasting, be it finance forecasting [3], weather forecasting [4], [5], human activity detection [8], energy consumption prediction [10], industrial fault diagnosis [41] and etc..”