iHear Food: Eating Detection Using Commodity Bluetooth Headsets

Gao, Yang; Zhang, Ning; Ding, Xiang; Ye, Xu; Chen, Guanling; Cao, Yu

doi:10.1109/chase.2016.14

Cited by 51 publications

(34 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the last decade, a significant amount of research has explored various approaches to fully automate food intake monitoring. Devices ranging from a microphone on the neck [38] to EMG-measuring eyeglasses [40] to in-ear microphones [16] have been explored. Since an important first step in research is to achieve reasonable lab-controlled performance, most work so far has thus occurred in laboratory settings with reasonable results [3, 17, 36].…”

Section: Introductionmentioning

confidence: 99%

EarBit

Bedri

Haynes

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

158

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

EarBit

Bedri

Haynes

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

158

View full text Add to dashboard Cite

show abstract

“…Six of the studies had participants self-report all daily activities, including eating, via a log or diary 33,38,40,53,60,64 , while six studies had participants self-report just eating activity 37,39,51,54,58,63 . In one study, participants were asked to record their eating episodes with a smartphone front-facing video camera to obtain ground-truth eating 47 . In another study, participants used a push button, located on a wireless device, as the primary method for self-reporting food intake; participants pressed and held a button during chewing to indicate the start and end of a chewing bout 44 .…”

Section: Ground-truth Methodsmentioning

confidence: 99%

“…Approximately 63% (N = 25) of the 40 studies utilized an accelerometer (device that determines acceleration) either by itself (N = 4) or incorporated into a sensor system (N = 21) to detect eating activity 18,[37][38][39][40][41]43,45,46,48,49,51,53,[56][57][58][59]62 (Table 2). The second most frequently utilized wearable sensor was a gyroscope (device that determines orientation) (N = 15) 33,38,39,46,48,49,51,53,[56][57][58] , followed by a microphone (N = 8) 34,35,47,52,54,60,61 , a piezoelectric sensor (N = 7) 18,40-42,44,45 , a RF transmitter and receiver (N = 6) 18,40,41,44,45 , and a smartwatch camera (N = 5) 56,57 (Table 2). EMG electrodes 36,63,…”

Section: Wearable Sensorsmentioning

confidence: 99%

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

et al. 2020

View full text Add to dashboard Cite

Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review included N = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors' ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.npj Digital Medicine (2020) 3:38 ; https://doi.

show abstract

“…Ordóñez and Roggen architect an advanced ConvLSTM to fuse data gathered from multiple sensors and perform activity recognition [112]. By leveraging CNN and LSTM structures, ConvLSTMs can automatically compress spatio-temporal sensor data into low-dimensional [236] Mobile ear Edge-based CNN Jindal [237] Heart rate prediction Cloud-based DBN Kim et al [238] Cytopathology classification Cloud-based CNN Sathyanarayana et al [239] Sleep quality prediction Cloud-based MLP, CNN, LSTM Li and Trocan [240] Health conditions analysis Cloud-based Stacked AE Hosseini et al [241] Epileptogenicity localisation Cloud-based CNN Stamate et al [242] Parkinson's symptoms management Cloud-based MLP Quisel et al [243] Mobile health data analysis Cloud-based CNN, RNN Khan et al [244] Respiration [250] Facial recognition Cloud-based CNN Wu et al [291] Mobile visual search Edge-based CNN Rao et al [251] Mobile augmented reality Edge-based CNN Ohara et al [290] WiFi-driven indoor change detection Cloud-based CNN,LSTM Zeng et al [252] Activity recognition Cloud-based CNN, RBM Almaslukh et al [253] Activity recognition Cloud-based AE Li et al [254] RFID-based activity recognition Cloud-based CNN Bhattacharya and Lane [255] Smart watch-based activity recognition Edge-based RBM Antreas and Angelov [256] Mobile surveillance system Edge-based & Cloud based CNN Ordóñez and Roggen [112] Activity recognition Cloud-based ConvLSTM Wang et al [257] Gesture recognition Edge-based CNN, RNN Gao et al [258] Eating detection Cloud-based DBM, MLP Zhu et al [259] User energy expenditure estimation Cloud-based CNN, MLP Sundsøy et al [260] Individual income classification Cloud-based MLP Chen and Xue [261] Activity recognition Cloud-based CNN Ha and Choi [262] Activity recognition Cloud-based CNN Edel and Köppe [263] Activity recognition Edge-based Binarized-LSTM Okita and Inoue [266] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al…”

Section: Mobilementioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,335

838

View full text Add to dashboard Cite

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

show abstract

iHear Food: Eating Detection Using Commodity Bluetooth Headsets

Cited by 51 publications

References 41 publications

EarBit

EarBit

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

Deep Learning in Mobile and Wireless Networking: A Survey

Contact Info

Product

Resources

About