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Article

An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning

Human Enhancement & Assistive Technology Research Section, Artificial Intelligence Research Lab., Electronics Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
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Sensors 2019, 19(17), 3688; https://doi.org/10.3390/s19173688
Received: 28 June 2019 / Revised: 19 August 2019 / Accepted: 23 August 2019 / Published: 25 August 2019
(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)
Human activity recognition (HAR), which is important in context awareness services, needs to occur continuously in daily life, owing to which an energy-efficient method is needed. However, because human activities have a longer cycle than HAR methods, which have analysis cycles of a few seconds, continuous classification of human activities using these methods is computationally and energy inefficient. Therefore, we propose segment-level change detection to identify activity change with very low computational complexity. Additionally, a fully convolutional network (FCN) with a high recognition rate is used to classify the activity only when activity change occurs. We compared the accuracy and energy consumption of the proposed method with that of a method based on a convolutional neural network (CNN) by using a public dataset on different embedded platforms. The experimental results showed that, although the recognition rate of the proposed FCN model is similar to that of the CNN model, the former requires only 10% of the network parameters of the CNN model. In addition, our experiments to measure the energy consumption on the embedded platforms showed that the proposed method uses as much as 6.5 times less energy than the CNN-based method when only HAR energy consumption is compared. View Full-Text
Keywords: human activity recognition; fully convolutional network; segment-level change detection; energy-efficient method; deep learning human activity recognition; fully convolutional network; segment-level change detection; energy-efficient method; deep learning
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MDPI and ACS Style

Jeong, C.Y.; Kim, M. An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning. Sensors 2019, 19, 3688. https://doi.org/10.3390/s19173688

AMA Style

Jeong CY, Kim M. An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning. Sensors. 2019; 19(17):3688. https://doi.org/10.3390/s19173688

Chicago/Turabian Style

Jeong, Chi Y., and Mooseop Kim. 2019. "An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning" Sensors 19, no. 17: 3688. https://doi.org/10.3390/s19173688

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