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Open AccessArticle

A CNN Based Automated Activity and Food Recognition Using Wearable Sensor for Preventive Healthcare

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Electronic Engineering Department, QUEST Campus Larkana, Larkana 77150, Pakistan
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Department of Electrical Engineering, Bahria University, Karachi 75260, Pakistan
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IBA Community College Naushahro Feroze, Sukkur IBA University, Sindh 65200, Pakistan
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College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea
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Author to whom correspondence should be addressed.
Electronics 2019, 8(12), 1425; https://doi.org/10.3390/electronics8121425
Received: 12 November 2019 / Revised: 25 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Human Computer Interaction and Its Future)
Recent developments in the field of preventive healthcare have received considerable attention due to the effective management of various chronic diseases including diabetes, heart stroke, obesity, and cancer. Various automated systems are being used for activity and food recognition in preventive healthcare. The automated systems lack sophisticated segmentation techniques and contain multiple sensors, which are inconvenient to be worn in real-life settings. To monitor activity and food together, our work presents a novel wearable system that employs the motion sensors in a smartwatch together with a piezoelectric sensor embedded in a necklace. The motion sensor generates distinct patterns for eight different physical activities including eating activity. The piezoelectric sensor generates different signal patterns for six different food types as the ingestion of each food is different from the others owing to their different characteristics: hardness, crunchiness, and tackiness. For effective representation of the signal patterns of the activities and foods, we employ dynamic segmentation. A novel algorithm called event similarity search (ESS) is developed to choose a segment with dynamic length, which represents signal patterns with different complexities equally well. Amplitude-based features and spectrogram-generated images from the segments of activity and food are fed to convolutional neural network (CNN)-based activity and food recognition networks, respectively. Extensive experimentation showed that the proposed system performs better than the state of the art methods for recognizing eight activity types and six food categories with an accuracy of 94.3% and 91.9% using support vector machine (SVM) and CNN, respectively.
Keywords: preventive healthcare; activity recognition; food recognition; support vector machine; convolutional neural network; signal segmentation; accelerometers; gyroscope; piezoelectric sensor preventive healthcare; activity recognition; food recognition; support vector machine; convolutional neural network; signal segmentation; accelerometers; gyroscope; piezoelectric sensor
MDPI and ACS Style

Hussain, G.; Maheshwari, M.K.; Memon, M.L.; Jabbar, M.S.; Javed, K. A CNN Based Automated Activity and Food Recognition Using Wearable Sensor for Preventive Healthcare. Electronics 2019, 8, 1425.

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