Automatic Measurement of Chew Count and Chewing Rate during Food Intake

Abstract

Research suggests that there might be a relationship between chew count as well as chewing rate and energy intake. Chewing has been used in wearable sensor systems for the automatic detection of food intake, but little work has been reported on the automatic measurement of chew count or chewing rate. This work presents a method for the automatic quantification of chewing episodes captured by a piezoelectric sensor system. The proposed method was tested on 120 meals from 30 participants using two approaches. In a semi-automatic approach, histogram-based peak detection was used to count the number of chews in manually annotated chewing segments, resulting in a mean absolute error of 10.40% ± 7.03%. In a fully automatic approach, automatic food intake recognition preceded the application of the chew counting algorithm. The sensor signal was divided into 5-s non-overlapping epochs. Leave-one-out cross-validation was used to train a artificial neural network (ANN) to classify epochs as “food intake” or “no intake” with an average F1 score of 91.09%. Chews were counted in epochs classified as food intake with a mean absolute error of 15.01% ± 11.06%. The proposed methods were compared with manual chew counts using an analysis of variance (ANOVA), which showed no statistically significant difference between the two methods. Results suggest that the proposed method can provide objective and automatic quantification of eating behavior in terms of chew counts and chewing rates.

Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor

“…In order to automate these systems/approaches, there is a need to developed methods for accurate estimation of chew counts. For current methods (both automatic and semi-automatic), the chew count estimation error varies between 8 – 20%, while tested in laboratory conditions [12]–[15], [37]. However, the segmentation and chew count estimation procedure proposed provides a better estimation of chew counts (overall error rate of 3.83%, aggregated over laboratory and free-living experiments), which should be in acceptable range for developing methods both for eating behavioral modification as well as energy estimation from chew counts/rate.…”

Influence of the difference between implant body and screw materials on abutment screw loosening

“…Cyclic loading tests have been used in biomechanical studies on implants 18) , although the conditions varied with each study. The present study considered the situation just after the superstructure had been set and then after one month 9) of standard chewing (assuming 100,000 cycles at a frequency of 2 Hz 19) ). The loading points used were those shown in Fig.…”

Recent Advances on Wearable Electronics and Embedded Computing Systems for Biomedical Applications

“…In the frame of wearable electronics and embedded computing systems for biomedical applications, this Special Issue of Electronics includes a total of 14 papers, including two review papers and twelve research articles [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The issue spans a wide range of topics, including smart sensing footwear systems, human-body hydration monitoring, textile-based ECG monitoring in human and horses, biotelemetry and telemedicine, support for surgical navigation, wearable autonomic nervous system activity monitoring, and hand posture and tactile pressure sensing [2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

“…The issue spans a wide range of topics, including smart sensing footwear systems, human-body hydration monitoring, textile-based ECG monitoring in human and horses, biotelemetry and telemedicine, support for surgical navigation, wearable autonomic nervous system activity monitoring, and hand posture and tactile pressure sensing [2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

“…Farooq et al [10] present a method for the automatic quantification of chewing episodes captured by a piezoelectric sensor system. Experimental results were related to the estimation of the number of chews as compared to manually annotated chewing segments, and an artificial neural network-based automatic classification of "food intake" or "no intake" classes.…”