Bipart: Learning Block Structure for Activity Detection

• Published in: IEEE transactions on knowledge and data engineering Vol. 26; no. 10; pp. 2397 - 2409
• Main Authors: Yang Mu, Lo, Henry Z., Wei Ding, Amaral, Kevin, Crouter, Scott E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2014
• Subjects: Accelerometers; Data models; Distance learning; Semisupervised learning; Accelerometers; semisupervised learning; distance learning
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2014.2300480

Physical activity consists of complex behavior, typically structured in bouts which can consist of one continuous movement (e.g., exercise) or many sporadic movements (e.g., household chores). Each bout can be represented as a block of feature vectors corresponding to the same activity type. This paper introduces a general distance metric technique to use this block representation to first predict activity type, and then uses the predicted activity to estimate energy expenditure within a novel framework. This distance metric, dubbed Bipart, learns block-level information from both training and test sets, combining both to form a projection space which materializes block-level constraints. Thus, Bipart provides a space which can improve the bout classification performance of all classifiers. We also propose an energy expenditure estimation framework which leverages activity classification in order to improve estimates. Comprehensive experiments on waist-mounted accelerometer data, comparing Bipart against many similar methods as well as other classifiers, demonstrate the superior activity recognition of Bipart, especially in low-information experimental settings.