Journal
SENSORS
Volume 18, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/s18113910
Keywords
human activity recognition; convolutional neural network; encoder; signal transformation; smartphone; smartwatch; accelerometer
Funding
- Institute for Information & communications Technology Promotion (IITP) grant - Korea government (MSIT) [2017-0-00655]
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) [IITP-2017-0-01629]
- MSIT (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW [2017-0-00093]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2017-0-00093-002, 2017-0-01629-002, 2017-0-00655-002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [21A20131612192] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The most significant barrier to success in human activity recognition is extracting and selecting the right features. In traditional methods, the features are chosen by humans, which requires the user to have expert knowledge or to do a large amount of empirical study. Newly developed deep learning technology can automatically extract and select features. Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper, we propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal to Image), a novel encoding technique for transforming an inertial sensor signal into an image with minimum distortion and a CNN model for image-based activity classification. Iss2Image converts real number values from the X, Y, and Z axes into three color channels to precisely infer correlations among successive sensor signal values in three different dimensions. We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches on the tested datasets.
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