converted the features in a time series into a grayscale image with shades of gray expressing the measured value from the sensor. The results suggest the expected future relevance of the transformer model for human activity recognition.Īlemayoh et al. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human–robot interaction.