Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Wearable Sensor Based Human Activity Recognition with Transformer

Version 1 : Received: 4 February 2022 / Approved: 8 February 2022 / Online: 8 February 2022 (13:09:21 CET)

A peer-reviewed article of this Preprint also exists.

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. Sensors 2022, 22, 1911.

Journal reference: Sensors 2022, 22, 1911
DOI: 10.3390/s22051911

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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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