An optimized low computational algorithm for human fall detection from depth images based on Support Vector Machine classification

Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approa...

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Veröffentlicht in:	2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) S. 407 - 412
Hauptverfasser:	Mohd, Mohd Norzali Haji, Nizam, Y., Suhaila, S., Jamil, M. Mahadi Abdul
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.09.2017
Schlagworte:	Acceleration Accelerometers Assitive Technology Classification algorithms Daily Activities Depth Images Floors Head Human Posture Robot sensing systems Support vector machines SVM
Online-Zugang:	Volltext
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