Event-Driven Real-Time Location-Aware Activity Recognition in AAL Scenarios

Proceedings

Seco, Fernando¹  Jiménez, Antonio² 

[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, La Poveda, Arganda del Rey, 28500 Madrid, Spain

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

DOI  :  10.3390/proceedings2191240

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

The challenge of recognizing different personal activities while living in an apartment is of great interest for the AAL community. Many different approaches have been presented trying to achieve good accuracies in activity recognition, combined with different heuristics, windowing and segmentation methods. In this paper we want to revisit the basic methodology proposed by a naive Bayes implementation with emphasis on multi-type event-driven location-aware activity recognition. Our method combines multiple events generated by binary sensors fixed to everyday objects, a capacitive smart floor, the received signal strength (RSS) from BLE beacons to a smart-watch and the sensed acceleration of the actor’s wrist. Our new method does not use any segmentation phase, it interprets the received events as soon as they are measured and activity estimations are generated in real-time without any post-processing or time-reversal re-estimation. An activity prediction model is used in order to guess the more-likely next activity to occur. The evaluation results show an improved performance when adding new sensor type events to the activity engine estimator. Classification results achieve accuracies of about 68%, which is a good figure taking into account the high number of different activities to classify (24).

【 授权许可】

CC BY