Periodic quick test for classifying long-term activities

A novel method to classify long-term human activities is presented in this study. The method consists of two parts: quick test and periodic classification. The quick test uses temporal information to improve recognition accuracy, while the periodic classification is based on the assumption that reco...

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Bibliographic Details
Main Authors: Siirtola, P., Koskimaki, H., Roning, J.
Format: Conference Proceeding
Language:English
Subjects:
Accelerometer
Accuracy
activity recognition
classification
Computational efficiency
Feature extraction
Hidden Markov models
Humans
Legged locomotion
Sensors
Online Access:Request full text
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Summary:A novel method to classify long-term human activities is presented in this study. The method consists of two parts: quick test and periodic classification. The quick test uses temporal information to improve recognition accuracy, while the periodic classification is based on the assumption that recognized activities are long-term. Periodic quick test (PQT) classification was tested using a data set consisting of six long-term sports exercises. The data were collected from six persons wearing a two-dimensional accelerometer on their wrist. The results show that the presented method is not only faster than a normal method, that does not use temporal information and does not assume that activities are long-term, but also more accurate. The results were compared with a normal sliding window technique which divides signal into smaller sequences and classifies each sequence into one of the six classes. The classification accuracy using a normal method was around 84% while using PQT the recognition rate was over 90%. In addition, the number of classified sequences using a normal method was over six times higher than using PQT.
DOI:10.1109/CIDM.2011.5949426