Sensor-Based Abnormal Human-Activity Detection

By Jie Yin, Qiang Yang, Jeffrey Junfeng Pan. IEEE Transactions on Knowledge and Data Engineering, 2008

398 citations recorded by the source corpus.

How researchers see this paper

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

A Survey on Ambient Intelligence in Health Care (2013, neutral): “Others tag events as anomalies if they occur rarely and they are not anticipated for the current context [99].”
Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning (2013, neutral): “ACCELERATION sensor-based activity recognition is usefulin practical applications [1], [2], [3], [4].”
Sensor-Based Activity Recognition (2012, background): “[131] defined abnormal activities as events that occur rarely and have not been expected in advance.”
A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “Likewise, patients with dementia and other mental pathologies could be monitored to detect abnormal activities and thereby prevent undesirable consequences [3].”
A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, methodology): “Applying learning techniques, such as Support Vector Machines (SVMs) [65] or dictionaries [66], can help mitigate this problem.”
Video-Based Abnormal Human Behavior Recognition—A Review (2012, methodology): “tool for such binary classification is support vector machines (SVMs) [15], [30], [125].”
Wireless sensor networks for healthcare: A survey (2010, neutral): “In [95], machine learning techniques are employed for categorizing the abnormal human activity based on body-worn sensor data.”