CLASSIFIER PERSONALIZATION FOR ACTIVITY RECOGNITION USING WRIST ACCELEROMETERS

Abstract

Inter-subject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this work we propose an approach for personalizing classification rules to a single person. We demonstrate that the method improves activity detection from wrist-worn accelerometer data on a four-class recognition problem of interest to the exercise science community, where classes are ambulation, cycling, sedentary, and other. We extend a previously published activity classification method based on support vector machines so that it estimates classification uncertainty. Uncertainty is used to drive data label requests from the user, and the resulting label information is used to update the classifier. Two different datasets – one from 33 adults with 26 activity types, and another from 20 youth with 23 activity types – were used to evaluate the method using leave-one-subject-out and leave-one-group-out cross validation. The new method improved overall recognition accuracy up to 11% on average, with some large person-specific improvements (ranging from -2% to +36%). The proposed method is suitable for online implementation supporting real-time recognition systems.

Existing System

Semi-supervised learning (SSL), active learning (AL)

Proposed System

The proposed work demonstrated activity recognition using SVM classifiers and wrist-worn accelerometer data from adult and youth data. Four activity classes were recognized (ambulation, cycling, sedentary, and other) based on windowed accelerometer of data, using nine frequency- and time-based features.

Architecture

BLOCK DIAGRAM