4-Page Contributed Papers (Poster)
12:00 - 13:45 | Mon 6 Nov | Auditorium Foyer, E1/E2, Upper Atrium Space | MLunch_Break
This work presents a novel algorithm for recognizing activity states which are of interest for assessing the general well-being of cancer, frail and elderly patients. Using the novel idea of two-level classification, misclassification due to unwanted hand motion noise, which is a common source of error in wrist-worn sensing systems, is mitigated. The algorithm is verified using data from 20 subjects performing a sequence of related activities. It is shown that the proposed algorithm improves the accuracy value for the activity state which includes sit, stand and move by up to 8%.
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