Gesture recognition for interactive controllers using MEMS motion sensors
NEMS '09 Proceedings of the 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems
Mobile physical activity recognition of stand-up and sit-down transitions for user behavior analysis
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
IEEE Transactions on Information Technology in Biomedicine
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Analysis of human movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson's disease (PD) or stroke patients, it is crucial to monitor their daily life activities. The main goal of this work is to characterize basic activities and their transitions using a single sensor located at the waist. This paper presents a novel postural detection algorithm which is able to detect and identify 6 different postural transitions, sit to stand, stand to sit, bending up/down and lying to sit and sit to lying transitions with a sensitivity of 86.5% and specificity of 95%. The algorithm has been tested on 31 healthy volunteers and 8 PD patients who performed a total of 545 and 176 transitions respectively. The proposed algorithm is suitable to be implemented in real-time systems for on-line monitoring applications.