A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
3D motion retrieval with motion index tree
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Locomotion monitoring using body sensor networks
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Transmission of patient vital signs using wireless body area networks
Mobile Networks and Applications - Special issue on Wireless and Personal Communications
Human motion recognition using a wireless sensor-based wearable system
Personal and Ubiquitous Computing
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Significant research has been done on recognizing the daily activities using acceleration data but few works have focused on classifying the movements comprising an activity due to the shorter time scales of the movements compared to that of an activity. Recognizing the individual movements within an activity can help improve the activity recognition on the whole by using the extra information from the movement granularity. Also, for many applications such as rehabilitation, sports medicine, geriatric care, and health/fitness monitoring the importance of movement recognition cannot be overlooked. Hence, in this paper a novel machine learning algorithm using body area networks is proposed that can on the fly, jointly classify the type of movements, and starting and finishing instant of each movement within an activity. A case study on the best set of features and minimum number of accelerometers needed to correctly classify movements within a smoking activity is also presented.