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A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches

Konstantinos Kyritsis, Christos Diou, Anastasios Delopoulos
2020 IEEE journal of biomedical and health informatics  
available smartwatches.  ...  This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals, from raw inertial data collected in-the-wild using commercially  ...  ACKNOWLEDGMENTS The work leading to these results has received funding from the EU Commission under Grant Agreement No. 727688 (, H2020).  ... 
doi:10.1109/jbhi.2020.2984907 pmid:32750897 fatcat:gjkagdrtqrbypma5tsdda3lb7q

Intake monitoring in free-living conditions: Overview and lessons we have learned

Christos Diou, Konstantinos Kyritsis, Vasileios Papapanagiotou, Ioannis Sarafis
2022 Appetite  
We also present evaluation results of these methods in challenging, real-world datasets.  ...  In this paper, we present a high-level overview of our recent work on intake monitoring using a smartwatch, as well as methods using an in-ear microphone.  ...  Dong et al. (2012) presented a method that uses a single channel of the gyroscope sensor (roll of the wrist) for detecting bites using a smartwatch.  ... 
doi:10.1016/j.appet.2022.106096 pmid:35644308 fatcat:yz3xftdiv5awrbppslsuhprija

Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors

Konstantinos Kyritsis, Petter Fagerberg, Ioannis Ioakimidis, K. Ray Chaudhuri, Heinz Reichmann, Lisa Klingelhoefer, Anastasios Delopoulos
2021 Scientific Reports  
Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session.  ...  This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior  ...  Acknowledgements The authors would like to thank Eva Rotter from the Department of Neurology, Technical University Dresden (TUD), Germany for her support during the collection of the EaC and EaH datasets  ... 
doi:10.1038/s41598-020-80394-y pmid:33452324 fatcat:2emoeepvnvg4lm4g73noj6ct64

Preventing Undesired Face-Touches With Wearable Devices and Haptic Feedback

N. D'Aurizio, T. Lisini Baldi, G. Paolocci, D. Prattichizzo
2020 IEEE Access  
We developed two gesture detection approaches compatible with sensors embedded in recent smartwatches, i.e. inertial and magnetic sensors.  ...  In its complete setup, the system consists of an application running on the smartwatch and a wearable accessory.  ...  For each phase, a different software has been developed to collect data from inertial and/or magnetic sensors.  ... 
doi:10.1109/access.2020.3012309 pmid:34812343 pmcid:PMC8545332 fatcat:medmou2lxrdh5kjwqjzcgi5rki

I am a Smartwatch and I can Track my User's Arm

Sheng Shen, He Wang, Romit Roy Choudhury
2016 Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys '16  
Using Kinect 2.0 as ground truth, we achieve around 9.2 cm of median error for free-form postures; the errors increase to 13.3 cm for a real time version.  ...  This paper aims to track the 3D posture of the entire arm -both wrist and elbow -using the motion and magnetic sensors on smartwatches.  ...  We evaluate ArmTrak using Samsung Gear Live smartwatches, with the sensor data processed on the watch (in real time) as well as on the cloud (running MATLAB).  ... 
doi:10.1145/2906388.2906407 dblp:conf/mobisys/ShenWC16 fatcat:n2vkm76gozfqrl5our3nyidkby

Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data

Konstantinos Kyritsis, Christos Diou, Anastasios Delopoulos
2019 Zenodo  
In this paper we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch.  ...  We use 5 specific wrist micromovements to model the series of actions leading to and following an intake event (i.e. bite). Food intake detection is performed in two steps.  ...  ACKNOWLEDGMENTS The work leading to these results has received funding from the EU Commission under Grant Agreement No. 727688 (, H2020), and Grant Agreement No. 690494 (  ... 
doi:10.5281/zenodo.3676581 fatcat:4mymrol4f5fctnkrlvturpwgny

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

Brooke M. Bell, Ridwan Alam, Nabil Alshurafa, Edison Thomaz, Abu S. Mondol, Kayla de la Haye, John A. Stankovic, John Lach, Donna Spruijt-Metz
2020 npj Digital Medicine  
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.  ...  long periods of time, and with minimal user interaction.  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper.  ... 
doi:10.1038/s41746-020-0246-2 pmid:32195373 pmcid:PMC7069988 fatcat:ja2ev5ljhrbtdpytq52fgntozy

The case for smartwatch-based diet monitoring

Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee
2015 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)  
We show, using smallscale user studies, how it is possible to use the accelerometer and gyroscope data from a smartwatch to accurately separate eating episodes from similar non-eating activities, and to  ...  We explore the use of gesture recognition on a wrist-worn smartwatch as an enabler of an automated eating activity (and diet monitoring) system.  ...  Detecting eating gestures To detect an eating activity, we captured the accelerometer and the gyroscope sensor data from the smart-watch.  ... 
doi:10.1109/percomw.2015.7134103 dblp:conf/percom/SenSMBL15 fatcat:uuaqibsa3zcoznjadwsjvu2exe

Toothbrushing Monitoring using Wrist Watch

Hua Huang, Shan Lin
2016 Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM - SenSys '16  
The toothbrushing gestures are recognized based on inertial sensing data from the wrist watch.  ...  The toothbrush is modified by attaching small magnets to the handle, so that its orientation and motion can be captured by the magnetic sensor in the wrist watch.  ...  For each time window, this feature is extracted from the accelerometer sensor data [Accx, Accy, Accz].  ... 
doi:10.1145/2994551.2994563 dblp:conf/sensys/HuangL16 fatcat:u2hox4ft5zhfzeut3zmksqyegu

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

Kawisara Ueafuea, Chiraphat Boonnag, Thapanun Sudhawiyangkul, Pitshaporn Leelaarporn, Ameen Gulistan, Wei Chen, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn, Supanida Piyayotai
2020 IEEE Sensors Journal  
part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization.  ...  Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.  ...  raw data from the sensors.  ... 
doi:10.1109/jsen.2020.3046259 fatcat:6bbd6y46jrfv3mwi2dlf5r4mmq

A Report on Smoking Detection and Quitting Technologies

Alessandro Ortis, Pasquale Caponnetto, Riccardo Polosa, Salvatore Urso, Sebastiano Battiato
2020 International Journal of Environmental Research and Public Health  
of gestures) and have been conducted with limited numbers of patients as well as under constrained settings quite far from real-life use scenarios.  ...  Several recent scientific works show very promising results but, at the same time, present obstacles for the application on real-life daily scenarios.  ...  in real-time.  ... 
doi:10.3390/ijerph17072614 pmid:32290288 fatcat:uh3p7k4ygbeizmiggo7dyjxsc4

The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition

Berkeley N. Limketkai, Kasuen Mauldin, Natalie Manitius, Laleh Jalilian, Bradley R. Salonen
2021 Current Surgery Reports  
Recent findings Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data.  ...  Machine learning has allowed for more complex analyses of the increasing volume of data collected.  ...  In fact, one can imagine adding the detection of eating events and estimation of caloric intake from activity data to current smartwatch features.  ... 
doi:10.1007/s40137-021-00297-3 pmid:34123579 pmcid:PMC8186363 fatcat:5hp2msozybe5jkg6ppd6ow43i4

Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels

Simon Stankoski, Marko Jordan, Hristijan Gjoreski, Mitja Luštrek
2021 Sensors  
The recordings consist of data from accelerometer and gyroscope sensors.  ...  Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way.  ...  Furthermore, these sensors should be suitable for continuous wearing in a real-world setting for a long time.  ... 
doi:10.3390/s21051902 pmid:33803121 fatcat:tksv47tpfrcvpfvpjd7z3m4dpy

Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning

Ghulam Hussain, Bander Ali Saleh Al-rimy, Saddam Hussain, Abdullah M. Albarrak, Sultan Noman Qasem, Zeeshan Ali
2022 Applied Sciences  
In this paper, we present a wearable sensor in the form of a necklace embedded with a piezoelectric sensor, that detects skin movement from the lower trachea while eating.  ...  Our system based on a smartphone app helps users live healthily by providing them with real-time feedback about their ingested food types, volume, and calorie count.  ...  In our work, we processed data and extracted features from the time-domain signal as reported in [13, 19] .  ... 
doi:10.3390/app12126135 fatcat:2duzb2xyavaqdf3smom6h23kz4

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

Ghulam HUSSAIN, Kamran JAVED, Jundong CHO, Juneho YI
2018 IEICE transactions on information and systems  
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.  ...  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.  ...  However, microphone of smartwatch stays further away after bite and hence their smartwatch modality may face more challenges with background noise in real environment.  ... 
doi:10.1587/transinf.2018edp7076 fatcat:bgz6wtzydvhhxoqergmiyjsppq
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