Dirgová Luptáková, I.; Kubovčík, M.; Pospíchal, J. Wearable Sensor-Based Human Activity Recognition with Transformer Model. Sensors2022, 22, 1911.
Dirgová Luptáková, I.; Kubovčík, M.; Pospíchal, J. Wearable Sensor-Based Human Activity Recognition with Transformer Model. Sensors 2022, 22, 1911.
Dirgová Luptáková, I.; Kubovčík, M.; Pospíchal, J. Wearable Sensor-Based Human Activity Recognition with Transformer Model. Sensors2022, 22, 1911.
Dirgová Luptáková, I.; Kubovčík, M.; Pospíchal, J. Wearable Sensor-Based Human Activity Recognition with Transformer Model. Sensors 2022, 22, 1911.
Abstract
This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.
Keywords
Transformer; human activity recognition; time series; sequence-to-sequence prediction
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
