{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:55:33Z","timestamp":1778262933901,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Several studies have shown the importance of proper chewing and the effect of chewing speed on the human health in terms of caloric intake and even cognitive functions. This study aims at designing algorithms for determining the chew count from video recordings of subjects consuming food items. A novel algorithm based on image and signal processing techniques has been developed to continuously capture the area of interest from the video clips, determine facial landmarks, generate the chewing signal, and process the signal with two methods: low pass filter, and discrete wavelet decomposition. Peak detection was used to determine the chew count from the output of the processed chewing signal. The system was tested using recordings from 100 subjects at three different chewing speeds (i.e., slow, normal, and fast) without any constraints on gender, skin color, facial hair, or ambience. The low pass filter algorithm achieved the best mean absolute percentage error of 6.48%, 7.76%, and 8.38% for the slow, normal, and fast chewing speeds, respectively. The performance was also evaluated using the Bland-Altman plot, which showed that most of the points lie within the lines of agreement. However, the algorithm needs improvement for faster chewing, but it surpasses the performance of the relevant literature. This research provides a reliable and accurate method for determining the chew count. The proposed methods facilitate the study of the chewing behavior in natural settings without any cumbersome hardware that may affect the results. This work can facilitate research into chewing behavior while using smart devices.<\/jats:p>","DOI":"10.3390\/s21206806","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T21:48:39Z","timestamp":1634161719000},"page":"6806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration"],"prefix":"10.3390","volume":"21","author":[{"given":"Sana","family":"Alshboul","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6352-5275","authenticated-orcid":false,"given":"Mohammad","family":"Fraiwan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/S0140-6736(03)12378-1","article-title":"Eating disorders","volume":"361","author":"Fairburn","year":"2003","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.appet.2014.11.003","article-title":"Energy intake estimation from counts of chews and swallows","volume":"85","author":"Fontana","year":"2015","journal-title":"Appetite"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farooq, M., and Sazonov, E. (2016). Automatic Measurement of Chew Count and Chewing Rate during Food Intake. Electronics, 5.","DOI":"10.3390\/electronics5040062"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s40519-020-00899-3","article-title":"Body mass index and potential correlates among elementary school children in Jordan","volume":"26","author":"Fraiwan","year":"2021","journal-title":"Eat. Weight.-Disord.-Stud. Anorexia Bulim. Obes."},{"key":"ref_5","first-page":"403","article-title":"Anatomical, functional, physiological and behavioural aspects of the development of mastication in early childhood","volume":"111","author":"Edelson","year":"2013","journal-title":"Br. J. Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1111\/j.1753-4887.2010.00361.x","article-title":"Genetics of eating behavior: Established and emerging concepts","volume":"69","author":"Grimm","year":"2011","journal-title":"Nutr. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0939-4753(03)80010-8","article-title":"Why should we study human food intake behaviour?","volume":"13","author":"Bellisle","year":"2003","journal-title":"Nutr. Metab. Cardiovasc. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Okubo, H., Murakami, K., Masayasu, S., and Sasaki, S. (2018). The Relationship of Eating Rate and Degree of Chewing to Body Weight Status among Preschool Children in Japan: A Nationwide Cross-Sectional Study. Nutrients, 11.","DOI":"10.3390\/nu11010064"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"709","DOI":"10.3945\/ajcn.111.015164","article-title":"Improvement in chewing activity reduces energy intake in one meal and modulates plasma gut hormone concentrations in obese and lean young Chinese men","volume":"94","author":"Li","year":"2011","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1016\/j.jand.2013.08.020","article-title":"Increasing the Number of Chews before Swallowing Reduces Meal Size in Normal-Weight, Overweight, and Obese Adults","volume":"114","author":"Zhu","year":"2014","journal-title":"J. Acad. Nutr. Diet."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"295","DOI":"10.2319\/061109-333.1","article-title":"Masticatory Performance and Chewing Cycle Kinematics\u2014Are They Related?","volume":"80","author":"Lepley","year":"2010","journal-title":"Angle Orthod."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0149-7634(99)00076-7","article-title":"Rate of intake, bites, and chews\u2014The interpretation of lean\u2013obese differences","volume":"24","author":"Spiegel","year":"2000","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"502","DOI":"10.7150\/ijms.11911","article-title":"Chewing Maintains Hippocampus-Dependent Cognitive Function","volume":"12","author":"Chen","year":"2015","journal-title":"Int. J. Med. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1111\/idj.12486","article-title":"Mastication as a protective factor of the cognitive decline in adults: A qualitative systematic review","volume":"69","author":"Chuhuaicura","year":"2019","journal-title":"Int. Dent. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12877-017-0693-z","article-title":"Revisiting the link between cognitive decline and masticatory dysfunction","volume":"18","author":"Lin","year":"2018","journal-title":"BMC Geriatr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1111\/eos.12060","article-title":"Relationship between natural teeth and memory in a healthy elderly population","volume":"121","author":"Hansson","year":"2013","journal-title":"Eur. J. Oral Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vu, T., Lin, F., Alshurafa, N., and Xu, W. (2017). Wearable Food Intake Monitoring Technologies: A Comprehensive Review. Computers, 6.","DOI":"10.3390\/computers6010004"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.25122\/jml-2019-0028","article-title":"Masticatory function parameters in patients with removable dental prosthesis","volume":"12","author":"Moraru","year":"2019","journal-title":"J. Med. Life"},{"key":"ref_19","first-page":"5","article-title":"A study to investigate reproducibility of chewing behaviour of human subjects within session recordings for different textured Indian foods using electromyography","volume":"7","author":"Rustagi","year":"2018","journal-title":"Pharma Innov. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.appet.2011.02.003","article-title":"Does prolonged chewing reduce food intake? Fletcherism revisited","volume":"57","author":"Smit","year":"2011","journal-title":"Appetite"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.physbeh.2016.07.010","article-title":"Adaptation of mastication mechanics and eating behaviour to small differences in food texture","volume":"165","author":"Saucy","year":"2016","journal-title":"Physiol. Behav."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Farooq, M., and Sazonov, E. (2015, January 25\u201329). Comparative testing of piezoelectric and printed strain sensors in characterization of chewing. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7320136"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1109\/TBME.2009.2015873","article-title":"Bite Weight Prediction From Acoustic Recognition of Chewing","volume":"56","author":"Amft","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130902","article-title":"EarBit","volume":"1","author":"Bedri","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Papapanagiotou, V., Diou, C., Zhou, L., van den Boer, J., Mars, M., and Delopoulos, A. (2016, January 16\u201320). A novel approach for chewing detection based on a wearable PPG sensor. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7592214"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101934","DOI":"10.1109\/ACCESS.2020.2998716","article-title":"Automatic Count of Bites and Chews From Videos of Eating Episodes","volume":"8","author":"Hossain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s00779-011-0425-x","article-title":"Exploiting visual quasi-periodicity for real-time chewing event detection using active appearance models and support vector machines","volume":"16","author":"Cadavid","year":"2011","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nyamukuru, M.T., and Odame, K.M. (2020, January 21). Tiny Eats: Eating Detection on a Microcontroller. Proceedings of the 2020 IEEE Second Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML), Sydney, Australia.","DOI":"10.1109\/SenSysML50931.2020.00011"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Little, M.A., Varoquaux, G., Saeb, S., Lonini, L., Jayaraman, A., Mohr, D.C., and Kording, K.P. (2017). Using and understanding cross-validation strategies. Perspectives on Saeb et al. GigaScience, 6.","DOI":"10.1093\/gigascience\/gix020"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2466\/pr0.1966.19.1.3","article-title":"The Intraclass Correlation Coefficient as a Measure of Reliability","volume":"19","author":"Bartko","year":"1966","journal-title":"Psychol. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","article-title":"Intraclass correlations: Uses in assessing rater reliability","volume":"86","author":"Shrout","year":"1979","journal-title":"Psychol. Bull."},{"key":"ref_32","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, HI, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kazemi, V., and Sullivan, J. (2014, January 23\u201328). One millisecond face alignment with an ensemble of regression trees. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.241"},{"key":"ref_34","first-page":"127","article-title":"Facial feature detection using Haar classifiers","volume":"21","author":"Wilson","year":"2006","journal-title":"J. Comput. Sci. Coll."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TAU.1973.1162510","article-title":"Approximate design relationships for low-pass FIR digital filters","volume":"21","author":"Rabiner","year":"1973","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1109\/78.157290","article-title":"The discrete wavelet transform: Wedding the a trous and Mallat algorithms","volume":"40","author":"Shensa","year":"1992","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s12652-019-01339-6","article-title":"Smartphone-based respiratory rate estimation using photoplethysmographic imaging and discrete wavelet transform","volume":"11","author":"Alafeef","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ren, J., Kehtarnavaz, N., and Estevez, L. (2008, January 19\u201320). Real-time optimization of Viola-Jones face detection for mobile platforms. Proceedings of the 2008 IEEE Dallas Circuits and Systems Workshop: System-on-Chip- Design, Applications, Integration, and Software, Richardson, TX, USA.","DOI":"10.1109\/DCAS.2008.4695921"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bodini, M. (2019). A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3010014"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s11554-020-00995-8","article-title":"Real-time wavelet transform for infinite image strips","volume":"18","author":"Barina","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical methods for assessing agreement between two methods of clinical measurement","volume":"327","author":"Bland","year":"1986","journal-title":"Lancet"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Farooq, M., and Sazonov, E. (2016, January 11\u201313). Linear regression models for chew count estimation from piezoelectric sensor signals. Proceedings of the 2016 10th International Conference on Sensing Technology (ICST), Nanjing, China.","DOI":"10.1109\/ICSensT.2016.7796222"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/1838563","article-title":"Earable RCC: Development of an Earphone-Type Reliable Chewing-Count Measurement Device","volume":"2018","author":"Taniguchi","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.smhl.2018.07.004","article-title":"Eating detection and chews counting through sensing mastication muscle contraction","volume":"9","author":"Wang","year":"2018","journal-title":"Smart Health"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust Real-Time Face Detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6806\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:12:40Z","timestamp":1760166760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,13]]},"references-count":45,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21206806"],"URL":"https:\/\/doi.org\/10.3390\/s21206806","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,13]]}}}