Automatic Measurement of Chew Count and Chewing Rate during Food Intake
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Electronics |
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| Online Access: | http://www.mdpi.com/2079-9292/5/4/62 |
| _version_ | 1798025890725101568 |
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| author | Muhammad Farooq Edward Sazonov |
| author_facet | Muhammad Farooq Edward Sazonov |
| author_sort | Muhammad Farooq |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:26:13Z |
| format | Article |
| id | doaj.art-383640e424454bb6a5abe01feca94034 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T18:26:13Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-383640e424454bb6a5abe01feca940342022-12-22T04:09:37ZengMDPI AGElectronics2079-92922016-09-01546210.3390/electronics5040062electronics5040062Automatic Measurement of Chew Count and Chewing Rate during Food IntakeMuhammad Farooq0Edward Sazonov1Department of Electrical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USADepartment of Electrical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USAResearch 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.http://www.mdpi.com/2079-9292/5/4/62chewing ratefood intake detectionpiezoelectric sensorartificial neural networkfeature computationchew countingpeak detection |
| spellingShingle | Muhammad Farooq Edward Sazonov Automatic Measurement of Chew Count and Chewing Rate during Food Intake Electronics chewing rate food intake detection piezoelectric sensor artificial neural network feature computation chew counting peak detection |
| title | Automatic Measurement of Chew Count and Chewing Rate during Food Intake |
| title_full | Automatic Measurement of Chew Count and Chewing Rate during Food Intake |
| title_fullStr | Automatic Measurement of Chew Count and Chewing Rate during Food Intake |
| title_full_unstemmed | Automatic Measurement of Chew Count and Chewing Rate during Food Intake |
| title_short | Automatic Measurement of Chew Count and Chewing Rate during Food Intake |
| title_sort | automatic measurement of chew count and chewing rate during food intake |
| topic | chewing rate food intake detection piezoelectric sensor artificial neural network feature computation chew counting peak detection |
| url | http://www.mdpi.com/2079-9292/5/4/62 |
| work_keys_str_mv | AT muhammadfarooq automaticmeasurementofchewcountandchewingrateduringfoodintake AT edwardsazonov automaticmeasurementofchewcountandchewingrateduringfoodintake |