Research Article
Pervasive Eating Habits Monitoring and Recognition through a Wearable Acoustic Sensor
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2014.255423, author={Yin Bi and Wenyao Xu and Nan Guan and Yangjie Wei and Wang Yi}, title={Pervasive Eating Habits Monitoring and Recognition through a Wearable Acoustic Sensor}, proceedings={8th International Conference on Pervasive Computing Technologies for Healthcare}, publisher={ICST}, proceedings_a={PERVASIVEHEALTH}, year={2014}, month={7}, keywords={eating habit feature extraction knn svm}, doi={10.4108/icst.pervasivehealth.2014.255423} }- Yin Bi
Wenyao Xu
Nan Guan
Yangjie Wei
Wang Yi
Year: 2014
Pervasive Eating Habits Monitoring and Recognition through a Wearable Acoustic Sensor
PERVASIVEHEALTH
ACM
DOI: 10.4108/icst.pervasivehealth.2014.255423
Abstract
Eating habits provide clinical diagnosis evidences of lifestyle related diseases, such as dysphagia and indigestion. However, it is costly to obtain eating habit information of common people in terms of both time and expenses. This paper presents a pervasive approach for eating habit monitoring and recognition by a necklace-like device and a smartphone communicating via bluetooth. The necklace-like device acquires acoustic signals from the throat, and the data are processed in the smartphone to recognize important features. With complex acoustic signals collected from the throat, our method comprehensively analyzes and recognizes different events including chewing, swallowing, and breathing in the smartphone. Experiments show that the proposed approach can recognize different acoustic events effectively, and the recognition accuracy with K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) is 86.82% and 98.35%, respectively. Finally, a real eating case study is conducted to validate the proposed approach.
