Principal component analysis of chewing sounds to detect differences in apple crispness

Belie, Nele De; Smedt, Valentijn De; Baerdemaeker, Josse De

doi:10.1016/s0925-5214(99)00070-8

Cited by 51 publications

(44 citation statements)

References 27 publications

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“…in [17]. Attempts to classify foods using pattern recognition techniques were initially performed by DeBelie et al [18] and in the scope of ADM in [7]. These latter works showed that a low number of foods (below ten) can be classified in a laboratory setting using foam-based ear sensors, which result in high ear occlusion.…”

Section: Acoustic Food Intake Monitoringmentioning

confidence: 99%

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: 99%

A wearable earpad sensor for chewing monitoring

Amft

2010

2010 IEEE Sensors

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“…De Belie et al (2000) stated that higher frequency can be important since human hearing can be characterized by a logarithmic scale. Piezoelectric sensors in general are capable of sensing high frequency vibrations.…”

Section: Discussionmentioning

confidence: 99%

Development of Method for Quantifying Food Texture Using Blanched Bunching Onions

Taniwaki¹,

Hanada²,

Sakurai³

2006

J. Japan. Soc. Hort. Sci.

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A method for quantifying the texture of fresh commodities was developed. The texture data were obtained by acoustic measurements using a piezoelectric sensor and knife edge probe. The signal amplitude density was computed by summing up the amplitude of the texture signals and dividing by the data length. We designated this value as the texture index. An octave multi-filter was used to analyze the texture signals in the frequency domain between 0 Hz and 6400 Hz. Also, a technique of reducing periodical noise in the texture signals was developed. The noise data before the probe was inserted into a sample were subtracted from the raw data that consisted of both texture signals and noise. The methodology was applied to blanched bunching onion samples. Their signal amplitude densities showed the characteristics of blanched bunching onion texture. Also, analyzing the texture signals in the lowest frequency band (0-50 Hz) revealed the leaf sheath structure of the blanched bunching onion tissue.

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“…Initial attempts were made by DeBelie et al [27] to discriminate two classes of crispness in apples by analysing principal components in the sound spectrum of the initial bite. In a followup work DeBelie et al [28] classified the sound emissions from the initial bite of different dry-crisp snacks.…”

Section: Chewing Recognitionmentioning

confidence: 99%

Recognition of dietary activity events using on-body sensors

Amft

Tröster

2008

Artificial Intelligence in Medicine

195

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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Principal component analysis of chewing sounds to detect differences in apple crispness

Cited by 51 publications

References 27 publications

A wearable earpad sensor for chewing monitoring

A wearable earpad sensor for chewing monitoring

Development of Method for Quantifying Food Texture Using Blanched Bunching Onions

Recognition of dietary activity events using on-body sensors

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