Detecting Mastication

Bedri, Abdelkareem; Verlekar, Apoorva; Thomaz, Edison; Avva, Valerie; Starner﻿﻿, Thad

doi:10.1145/2818346.2820767

Cited by 50 publications

(10 citation statements)

References 13 publications

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“…The optical sensor is the VCNL4020 fully-integrated proximity sensor with an infrared emitter. Bedri et al have used this sensor to track jaw motion for detecting chewing in a controlled environment [6, 7]. The sensor is placed at the entrance of the ear canal and measures the degree of deformation at the canal caused by the movement of the mandibular bone.…”

Section: System Descriptionmentioning

confidence: 99%

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EarBit

Bedri

Haynes

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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157

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Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants’ behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F1-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F1-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal.

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Section: System Descriptionmentioning

confidence: 99%

“…We used the approach suggested by Bedri et al to develop the processing pipeline for the IMU and proximity sensor ([6], see Figure 5). Bedri et al also recommended using Hidden Markov Models (HMMs) with 10 minute segments for the final classification.…”

Section: System Descriptionmentioning

confidence: 99%

EarBit

Bedri

Haynes

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

Self Cite

157

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“…There are various methods of measuring and recording daily dietary information. Different researchers have developed automated non-invasive food monitoring methods using different sensors such as accelerometers [29], microphones [4], [5], [7]- [9], [30], [31], cameras [35], gyroscopes [36]- [39], proximity sensor [17], textile pressure sensors [21], strain gauges [40], piezoelectric sensors [6], [13], [15], [16], [19], [22], [23], [26], [41], orientation sensors [10], [42], [43], electromyography [18], electroglottography [44], and wrist band [45].…”

Section: Related Workmentioning

confidence: 99%

Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System

Hussain

Javed

Cho

et al. 2018

IEICE Trans. Inf. & Syst.

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Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklacetype wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous stateof-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.

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“…Results varied significantly by individual, with 11/38 participants having 100% precision for identifying meal periods and 9 participants having 0% precision. Other work used sensors in the ear to measure jaw movement [5] while eating three different foods and performing other activities, and classified meal periods with high accuracy.…”

Section: Contributionsmentioning

confidence: 99%

Recognizing Eating from Body-Worn Sensors

Mirtchouk

Lustig

Smith

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Automated dietary monitoring solutions that can find when, what, and how much individuals consume are needed for many applications such as providing feedback to individuals with chronic disease. Advances in body-worn sensors have led to systems with high accuracy for finding meals and even which foods are consumed in each bite. However, most tests are done in controlled lab settings with restricted meal choices, little background noise, and subjects focused on eating. For these systems to be adopted by users it is critical that they work well in realistic situations and be able to handle confounding factors such as background noise, shared meals, and multi-tasking. Work in realistic environments usually has lower accuracy, but has challenges in determining ground truth. Most critically, there has been a significant gap between lab and free-living environments. This is compounded by data usually being collected for different individuals in each setting, making it difficult to determine how the accuracy gap can be closed. We present a multi-modality study on eating recognition, using bodyworn motion (head, wrists) and audio (earbud microphone) sensors for 12 participants (6 from the lab study, 6 new to test generalizability). In contrast to the lab, where audio alone has the highest accuracy, we find now that a combination of sensing modalities (audio, motion) is needed; yet sensor placement (head vs. wrist) is not critical. We further find that lab data does generalize to other participants, but while personal free-living data improves accuracy, more data from others can actually lead to worse performance. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing;

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Detecting Mastication

Cited by 50 publications

References 13 publications

EarBit

EarBit

Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System

Recognizing Eating from Body-Worn Sensors

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