Statistical data mining of streaming motion data for activity and fall recognition in assistive environments

• Published in: Neurocomputing (Amsterdam) Vol. 107; pp. 87 - 96
• Main Authors: Tasoulis, S.K., Doukas, C.N., Plagianakos, V.P., Maglogiannis, I.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2013; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Cumulative sum (CUSUM) algorithm; Data processing. List processing. Character string processing; Exact sciences and technology; Fall detection; Memory organisation. Data processing; Software; Streaming motion data; Visual data; Cumulative sum (CUSUM) algorithm; Streaming motion data; Fall detection; Visual data; Classification scheme; Mobile phone; Event detection; Mobility; Activity; Data mining; Statistical data; CUSUM method; Displacement measurement; Human; Multimedia; Streaming; Data analysis; Statistical analysis; Motion estimation; Measurement sensor; Distributed system; Real time; Physical handicap; Body area network; Scene analysis; Alarm; Body movement; Emergency; Motion analysis; Elderly; Central unit; Mobile computing

The analysis of human motion data is interesting in the context of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either...