Eating Activity Detection and Meal Time Estimation Using Structure Features From 6-axis Inertial Sensor


KIPS Transactions on Computer and Communication Systems, Vol. 7, No. 8, pp. 211-218, Aug. 2018
10.3745/KTCCS.2018.7.8.211, Full Text:
Keywords: Meal time, Eating Activity, Structural Feature, Activity recognition
Abstract

In this study, we propose an algorithm to detect eating activity and estimation mealtime using 6-axis inertial sensor. The eating activity is classified into three types: food picking, food eating, and lowering. The feature points of the gyro signal are selected for each gesture, and the eating activity is detected when each feature point appears in the sequence. Morphology technique is used to post-process to detect meal time. The proposed algorithm achieves the accuracy of 94.3% and accuracy of 84.1%.


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Cite this article
[IEEE Style]
J. H. Kim, S. Choi, J. H. Ha and W. Cho, "Eating Activity Detection and Meal Time Estimation Using Structure Features From 6-axis Inertial Sensor," KIPS Transactions on Computer and Communication Systems, vol. 7, no. 8, pp. 211-218, 2018. DOI: 10.3745/KTCCS.2018.7.8.211.

[ACM Style]
Jun Ho Kim, Sun-Tak Choi, Jeong Ho Ha, and We-Duke Cho. 2018. Eating Activity Detection and Meal Time Estimation Using Structure Features From 6-axis Inertial Sensor. KIPS Transactions on Computer and Communication Systems, 7, 8, (2018), 211-218. DOI: 10.3745/KTCCS.2018.7.8.211.