Investigating Inter-Subject and Inter-Activity Variations in Activity Recognition Using Wearable Motion Sensors
| Autor: | Billur Barshan, Aras Yurtman |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Technology
Similarity (geometry) General Computer Science Activity recognition and classification Computer science Feature vector Feature reduction TIME-SERIES Wearable computer Inertial sensors 02 engineering and technology Variation (game tree) Machine learning computer.software_genre Distance measures Set (abstract data type) Activity recognition Consistency (database systems) Computer Science Theory & Methods 0202 electrical engineering electronic engineering information engineering Inter-activity variation Dynamic time warping Computer Science Hardware & Architecture Science & Technology Computer Science Information Systems business.industry 020206 networking & telecommunications Motion sensors Computer Science Software Engineering Gyroscope Magnetometers Inter-subject variation Wearable sensing CAPTURE Accelerometer PHYSICAL-ACTIVITY Computer Science magnetometer Feature extraction 020201 artificial intelligence & image processing Artificial intelligence business Motion capture computer |
| Zdroj: | Computer Journal |
| ISSN: | 1460-2067 0010-4620 |
| DOI: | 10.1093/comjnl/bxv093 |
| Popis: | This work investigates inter-subject and inter-activity variability of a given activity dataset and provides some new definitions to quantify such variability. The definitions are sufficiently general and can be applied to a broad class of datasets that involve time sequences or features acquired using wearable sensors. The study is motivated by contradictory statements in the literature on the need for user-specific training in activity recognition. We employ our publicly available dataset that contains 19 daily and sports activities acquired from eight participants who wear five motion sensor units each. We pre-process recorded activity time sequences in three different ways and employ absolute, Euclidean and dynamic time warping distance measures to quantify the similarity of the recorded signal patterns. We define and calculate the average inter-subject and inter-activity distances with various methods based on the raw and pre-processed time-domain data as well as on the raw and pre-processed feature vectors. These definitions allow us to identify the subject who performs the activities in the most representative way and pinpoint the activities that show more variation among the subjects. We observe that the type of pre-processing used affects the results of the comparisons but that the different distance measures do not alter the comparison results as much. We check the consistency of our analysis and results by highlighting some of our activity recognition rates based on an exhaustive set of sensor unit, sensor type and subject combinations. We expect the results to be useful for dynamic sensor unit/type selection, for deciding whether to perform user-specific training and for designing more effective classifiers in activity recognition. |
| Databáze: | OpenAIRE |
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