2019/05/29 by Pekka Siirtola, Siirtola, Pekka, Heli Koskimäki +3 · 1 citation
Computer Science · Engineering · #Activity recognition #Artificial intelligence #Computer science #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Green IT and Sustainability #Human-Computer Interaction (cs.HC) #Human–computer interaction #Internet privacy #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.1905.12285
From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone, European Symposium on Artificial Neural Networks, Compu-tational Intelligence and Machine Learning, ESANN 2016., Bruges, Belgium 27-29 April 2016, 471--476
arxiv created 2019/05/29 · openalex publication_date 2019/05/29 · arxiv updated 2019/05/30 · openalex created_date 2019/06/07 · openalex updated_date 2026/07/28
In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by exploiting the sensors of a smartphone is presented. The recognition consists of two models: sensor fusion-based user-independent model for data labeling and single sensor-based user-dependent model for final recognition. The functioning of the presented method is tested with human activity data set, including data from accelerometer and magnetometer, and with two classifiers. Comparison of the detection accuracies of the proposed method to traditional user-independent model shows that the presented method has potential, in nine cases out of ten it is better than the traditional method, but more experiments using different sensor combinations should be made to show the full potential of the method.