Nutrition Monitoring and Calorie Estimation using Internet of Things (IoT)
P. Kamakshi Priyaa1, S. Sathyapriya2, L. Arockiam3
1S. Sathyapriya, Ph.D. Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India.
2S. Sathyapriya, Ph.D. in Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India.
3Dr. L. Arockiam, Associate Professor Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India.
Manuscript received on 25 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 2669-2672 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20720981119/2019©BEIESP | DOI: 10.35940/ijitee.K2072.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diet observation is one of the principal aspect in precautionary health care that aims to cut back varied health risks. The various recent advancements in smartphone and wearable sensing element technologies have paved way to a proliferation of food observation applications that are based on automated image processing and intake detection, with an aim to beat drawbacks of the standard manual food journaling that’s time overwhelming, inaccurate, underreporting, and low adherent. The currently developed food logging methods are very much time consuming and inconvenient that limits their effectiveness. The proposed work presents an Internet of Things (IoT) based mobile-controlled calorie estimation system to make technical advancements in healthcare industry. The proposed system operates on mobile environment, which allow the user to acquire the food image and quantify the calorie intake mechanically. The Mqtt protocol based MyMqtt broker is used to connect the application and the edge device and also to store the data in the Thingspeak cloud. A deep convolutional network is employed to classify the food accurately within the system. The volume estimation is done using sensors and the calorie approximation is done using formula.
Keywords: Calorie Estimation, Convolutional Neural Network, , Deep Learning, IoT.
Scope of the Article: Internet of Things (IoT) & IoE & Edge Computing