Monitoring of Infant Feeding Behavior Using a Jaw Motion Sensor

Farooq, Muhammad; Chandler‐Laney, Paula; Hernandez‐Reif, Maria; Sazonov, Edward

doi:10.1260/2040-2295.6.1.23

Cited by 19 publications

(6 citation statements)

References 28 publications

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“…Thus, there is a need to develop methods to provide objective and automatic estimation of chew counts and chewing rates. In recent studies, semi-automatic chew counting systems utilizing piezoelectric strain sensors have been proposed [ 23 – 25 ]. In [ 23 , 24 ], a modified form of the sensor system proposed in [ 13 ] was used to quantify the sucking count of 10 infants by using zero crossing.…”

Section: Introductionmentioning

confidence: 99%

“…In recent studies, semi-automatic chew counting systems utilizing piezoelectric strain sensors have been proposed [ 23 – 25 ]. In [ 23 , 24 ], a modified form of the sensor system proposed in [ 13 ] was used to quantify the sucking count of 10 infants by using zero crossing. In [ 25 ], an algorithm was proposed for counting chews from a piezoelectric strain sensor and printed sensor signals.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Measurement of Chew Count and Chewing Rate during Food Intake

Farooq

Sazonov

2016

Electronics

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Measurement of Chew Count and Chewing Rate during Food Intake

Farooq

Sazonov

2016

Electronics

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“…In some studies [38,41,42], flexible sensors are also used to monitor infant movement, such as bend sensors, which are usually used to acquire joint angle data, the Jew motion sensor is used to capture the action information of mouth mastication. Flexible sensors can acquire knowledge about the posture of static objects, which is beyond the ability of IMUs.…”

Section: Discussionmentioning

confidence: 99%

A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates

Chen

Xue

Mei

et al. 2016

Sensors

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Characteristics of physical movements are indicative of infants’ neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design.

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“…This approach can potentially be used in conjunction with any wearable sensor for monitoring food intake such as chewing and swallowing monitoring systems (piezoelectric strain sensor, swallowing microphones, and electroglottography) [8,21,22,23,24,25,26], and wrist monitoring systems for tracking bites (for example MEMS gyroscope based system for tracking wrist movements [27], accelerometer present in smart-watches [8]). Video-based annotation methodology has also been utilized in the studies [25,26] for monitoring the feeding behavior of infants in laboratory conditions. A common theme among all the studies which relied on the video observation is the use of a single camera fixated on the participant.…”

Section: Introductionmentioning

confidence: 99%

Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment

et al. 2019

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Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants’ food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light’s kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters’ annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.

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Monitoring of Infant Feeding Behavior Using a Jaw Motion Sensor

Cited by 19 publications

References 28 publications

Automatic Measurement of Chew Count and Chewing Rate during Food Intake

Automatic Measurement of Chew Count and Chewing Rate during Food Intake

A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates

Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment

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