title: Multi-sensor physical activity recognition in free-living.
abstract: Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions. [Formula: see text].
authors.affiliation: UC San Diego, Electrical and Computer Engineering, 9500 Gilman Drive, La Jolla, CA 92023 USA, kellis@ucsd.edu.
authors.forename: Katherine
authors.initials: K
authors.lastname: Ellis
authors.affiliation: UC San Diego, Family and Preventive Medicine, 9500 Gilman Drive, La Jolla, CA 92023 USA, sgodbole@ucsd.edu.
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.affiliation: UC San Diego, Family and Preventive Medicine, 9500 Gilman Drive, La Jolla, CA 92023 USA, jkerr@ucsd.edu.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
authors.affiliation: UC San Diego, Electrical and Computer Engineering, 9500 Gilman Drive, La Jolla, CA 92023 USA, gert@ece.ucsd.edu.
authors.forename: Gert
authors.initials: G
authors.lastname: Lanckriet
keywords: Accelerometer; Activity recognition; Codebook; GPS; Linear dynamical system