Multi-Event Naive Bayes Classifier for Activity Recognition in the UCAmI Cup

Proceedings

Seco, Fernando¹  Jiménez, Antonio R.² 

[1]Author to whom correspondence should be addressed.

[2]Centre for Automation and Robotics (CAR), Consejo Superior de Investigaciones Científicas (CSIC)-UPM, Ctra, Campo Real km 0.2, 28500 La Poveda, Arganda del Rey, Madrid, Spain

关键词: competition;    activity recognition;    naive bayes classifier;    real-time classifier;    bluetooth proximity;    acceleration;    binary sensors;    capacitive floor;   

DOI  :  10.3390/proceedings2191264

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

This short paper presents the activity recognition results obtained from the CAR-CSIC team for the UCAmI’18 Cup. We propose a multi-event naive Bayes classifier for estimating 24 different activities in real-time. We use all the sensorial information provided for the competition, i.e., binary sensors fixed to everyday objects, proximity BLE-based tags, location-aware smart floor sensing and the wrist’s acceleration. The results using training data-sets of 7 days show accuracies (true positives) about 68%; however for the three extra data-sets of the competition we were able to reach a 60.5% accuracy.

【 授权许可】

CC BY