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Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors

Authors
Kim, TaehwanPark, JeonghoLee, JuwonPark, Jooyoung
Issue Date
12월-2021
Publisher
MDPI
Keywords
Fourier neural operator; human motion; prediction; recurrence plot; wearable sensors
Citation
SENSORS, v.21, no.24
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
21
Number
24
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/135682
DOI
10.3390/s21248270
ISSN
1424-8220
Abstract
The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.
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