Bite Weight Prediction From Acoustic Recognition of Chewing

Amft, Oliver; Kusserow, Martin; Tröster, Gerhard

doi:10.1109/tbme.2009.2015873

Cited by 107 publications

(83 citation statements)

References 27 publications

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“…Moreover, the greater the number of foods, the more complicated the signal processing scheme becomes and the less accurate the system is. For example, Amft et al [77] are able to classify between three types of food with 94% accuracy. However, Amft [49], categorizing between four types of food, obtained an accuracy of only 86.6%.…”

Section: Discussionmentioning

confidence: 99%

“…Amft [49] specified their vibration-based detecting system using a condenser microphone embedded in an ear pad for food classification, which is demonstrated in Figure 4a. Amft et al [77] further expanded the food classification to chewing and bite weight recognition. The chewing recognition procedure employs the chewing sequences, waveforms of sounds indicating chewing captured by ear pad sensors.…”

Section: Acoustic Approachmentioning

confidence: 99%

“…They are dependent on the number of food types, dataset and approach; however, we can consider the signal processing introduced by Amft et al [77] for acoustic food type recognition, which is shown in Figure 8. The recognition procedure starts with data collection as usual.…”

Section: Food Type Classificationmentioning

confidence: 99%

“…Generally speaking, the FFS is utilized to detect the food intake event (chewing) for later food type classification, as well as to eliminate overlapping temporal events and to retain the events with the largest food classification confidences. A Fisher's linear discriminant feature transformation-based classifier is trained for the classification step [77,142]. A majority vote among all classified events in one chewing sequence will later determine the final food type.…”

Section: Food Type Classificationmentioning

confidence: 99%

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Wearable Food Intake Monitoring Technologies: A Comprehensive Review

et al. 2017

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Wearable devices monitoring food intake through passive sensing is slowly emerging to complement self-reporting of users' caloric intake and eating behaviors. Though the ultimate goal for the passive sensing of eating is to become a reliable gold standard in dietary assessment, it is currently showing promise as a means of validating self-report measures. Continuous food-intake monitoring allows for the validation and refusal of users' reported data in order to obtain more reliable user information, resulting in more effective health intervention services. Recognizing the importance and strength of wearable sensors in food intake monitoring, there has been a variety of approaches proposed and studied in recent years. While existing technologies show promise, many challenges and opportunities discussed in this survey, still remain. This paper presents a meticulous review of the latest sensing platforms and data analytic approaches to solve the challenges of food-intake monitoring, ranging from ear-based chewing and swallowing detection systems that capture eating gestures to wearable cameras that identify food types and caloric content through image processing techniques. This paper focuses on the comparison of different technologies and approaches that relate to user comfort, body location, and applications for medical research. We identify and summarize the forthcoming opportunities and challenges in wearable food intake monitoring technologies.

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Section: Discussionmentioning

confidence: 99%

Section: Acoustic Approachmentioning

confidence: 99%

Section: Food Type Classificationmentioning

confidence: 99%

Section: Food Type Classificationmentioning

confidence: 99%

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Wearable Food Intake Monitoring Technologies: A Comprehensive Review

et al. 2017

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“…The vibration of broken foods is conducted through mandible and skull to the ear canal. Scenario and analysis using a static spotting algorithm had been previously reported for this dataset in [23]. Here the dataset properties are briefly summarised.…”

Section: ) Dataset 2: Spotting Chewing Strokesmentioning

confidence: 99%

Adaptive Activity Spotting Based on Event Rates

Amft

2010

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

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Abstract-To date many activity spotting approaches are static: once the system is trained and deployed it does not change anymore. There are substantial shortcomings of this approach, specifically spotting performance is hampered when patterns or sensor noise level changes.In this work an unsupervised sensitivity adaptation mechanism is proposed for activity event spotting based on expected activity event rates. The expected event rate for activity spotting was derived from the generalisation metric used in information retrieval. To illustrate generalisation effects and depict relations of spotting performance and event rate, different event rates were simulated and their precision-recall spotting performance analysed. Subsequently, the sensitivity adaptation concept is presented and evaluated. For this purpose two large datasets from personal healthcare applications were considered to explore benefits and limitations of this adaptation approach: recognition of drinking motions from inertial sensors and chewing strokes from sound. Results showed up to 28% spotting performance increase for event rate adapted operation, confirming performance benefits for sensitivity adaptation. The approach will be most applicable in situations, where estimated event rate statistics show low variance and long monitoring durations allow effective sensitivity adaptations.

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Assessment of Dietary Habits

2017

Public Health Nutrition 2nd Edition

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Bite Weight Prediction From Acoustic Recognition of Chewing

Cited by 107 publications

References 27 publications

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

Adaptive Activity Spotting Based on Event Rates

Assessment of Dietary Habits

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