Automatic Identification of Temporal Sequences in Chewing Sounds

Magnan, Christophe; Capponi, Cécile; Denis, François

doi:10.1109/bibm.2007.18

Cited by 7 publications

(6 citation statements)

References 47 publications

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“…This observation was later confirmed using an automatic unsupervised sequence searching technique to group chewing strokes [19]. In contrast, the current work aims to establish the viability of a reduced occlusion sensor prototype.…”

Section: Acoustic Food Intake Monitoringmentioning

confidence: 69%

A wearable earpad sensor for chewing monitoring

Amft

2010

2010 IEEE Sensors

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Abstract-Today dietary assessments require manual information sampling in paper or electronic questionnaires on food type and other diet-related details. Low accuracies of 50% are confirmed for self-reporting, which weakens diet coaching effectiveness and is a major limitation for today's diet programs. Automatic Dietary Monitoring (ADM) using ubiquitous sensors was proposed to alleviate this problem.In this work, we present implementation and analysis results of a novel acoustic earpad sensor device to capture air-conducted vibrations of food chewing. In contrast to previous works, our new device reduces ear occlusion compared to laboratory setups by using wearable earpad headphones. We investigate the sensing principle, perform a spectral sound analysis, and compare food classification performance to a classic lab-based sensor setup. We present novel food texture clustering results for 19 foods, spurring the understanding of food texture structure. In addition, we detail findings of a recent exhibition installation, were 375 food samples were analysed using the new sensor prototype.

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Section: Acoustic Food Intake Monitoringmentioning

confidence: 69%

A wearable earpad sensor for chewing monitoring

Amft

2010

2010 IEEE Sensors

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“…This effect was attributed to the low signal to noise ratio of these sounds. Moreover, the chewing sequence is not consistent over the entire intake cycle as assumed in the current approach [42]. This is observed as a variability in the detection confidences and hinders fusion methods such as LR to achieve a higher performance.…”

Section: Chewing Recognitionmentioning

confidence: 91%

Recognition of dietary activity events using on-body sensors

Amft

Tröster

2008

Artificial Intelligence in Medicine

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193

148

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SummaryObjective: An imbalanced diet elevates health risks for many chronic diseases including obesity. Dietary monitoring could contribute vital information to lifestyle coaching and diet management, however, current monitoring solutions are not feasible for a long-term implementation. Towards automatic dietary monitoring, this work targets the continuous recognition of dietary activities using on-body sensors. Methods: An on-body sensing approach was chosen, based on three core activities during intake: arm movements, chewing and swallowing. In three independent evaluation studies the continuous recognition of activity events was investigated and the precision-recall performance analysed. An event recognition procedure was deployed, that addresses multiple challenges of continuous activity recognition, including the dynamic adaptability for variable-length activities and flexible deployment by supporting one to many independent classes. The approach uses a sensitive activity event search followed by a selective refinement of the detection using different information fusion schemes. The method is simple and modular in design and implementation. Results: The recognition procedure was successfully adapted to the investigated dietary activities. Four intake gesture categories from arm movements and two food groups from chewing cycle sounds were detected and identified with a recall of 80-90% and a precision of 50-64%. The detection of individual swallows resulted in 68% recall and 20% precision. Sample-accurate recognition rates were 79% for movements, 86% for chewing and 70% for swallowing. Conclusions: Body movements and chewing sounds can be accurately identified using on-body sensors, demonstrating the feasibility of on-body dietary monitoring. Further investigations are needed to improve the swallowing spotting performance. #

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“…In addition, they found that chewing sound patterns changed during the breakdown process of several chewing cycles. This observation was later confirmed using an automatic unsupervised sequence searching technique to group chewing strokes [3]. Lopez-Meyer et al used microphones to record chewing and swallowing sounds and detect periods of food intake by evaluation of the instantaneous swallowing frequency [17].…”

Section: Related Workmentioning

confidence: 93%

Pervasive Eating Habits Monitoring and Recognition through a Wearable Acoustic Sensor

Guan

et al. 2014

Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare

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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.

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Automatic Identification of Temporal Sequences in Chewing Sounds

Cited by 7 publications

References 47 publications

A wearable earpad sensor for chewing monitoring

A wearable earpad sensor for chewing monitoring

Recognition of dietary activity events using on-body sensors

Pervasive Eating Habits Monitoring and Recognition through a Wearable Acoustic Sensor

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