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Ghosh T, Han Y, Raju V, Hossain D, McCrory MA, Higgins J, Boushey C, Delp EJ, Sazonov E. Integrated image and sensor-based food intake detection in free-living. Sci Rep 2024; 14:1665. [PMID: 38238423 PMCID: PMC10796396 DOI: 10.1038/s41598-024-51687-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2). Thirty participants wore the AIM-2 for two days each (pseudo-free-living and free-living). The eating episodes were detected by three methods: (1) recognition of solid foods and beverages in images captured by AIM-2; (2) recognition of chewing from the AIM-2 accelerometer sensor; and (3) hierarchical classification to combine confidence scores from image and accelerometer classifiers. The integration of image- and sensor-based methods achieved 94.59% sensitivity, 70.47% precision, and 80.77% F1-score in the free-living environment, which is significantly better than either of the original methods (8% higher sensitivity). The proposed method successfully reduces the number of false positives in the detection of eating episodes.
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Affiliation(s)
- Tonmoy Ghosh
- Electrical and Computer Engineering Department, University of Alabama, Tuscaloosa, AL, 35401, USA.
| | - Yue Han
- Electrical and Computer Engineering Department, Purdue University, West Lafayette, IN, 47907, USA
| | - Viprav Raju
- Electrical and Computer Engineering Department, University of Alabama, Tuscaloosa, AL, 35401, USA
| | - Delwar Hossain
- Electrical and Computer Engineering Department, University of Alabama, Tuscaloosa, AL, 35401, USA
| | - Megan A McCrory
- Department of Health Sciences, Boston University, Boston, MA, 02215, USA
| | - Janine Higgins
- Department of Pediatrics-Endocrinology, University of Colorado, Denver, CO, 80045, USA
| | - Carol Boushey
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Edward J Delp
- Electrical and Computer Engineering Department, Purdue University, West Lafayette, IN, 47907, USA
| | - Edward Sazonov
- Electrical and Computer Engineering Department, University of Alabama, Tuscaloosa, AL, 35401, USA
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2
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Mevissen SJ, Klaassen R, van Beijnum BJF, Haarman JAM. Eating Event Recognition Using Accelerometer, Gyroscope, Piezoelectric, and Lung Volume Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:571. [PMID: 38257664 PMCID: PMC11154557 DOI: 10.3390/s24020571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F1-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food.
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Affiliation(s)
- Sigert J. Mevissen
- Department of Human Media Interaction, University of Twente, 7522 NB Enschede, The Netherlands; (R.K.); (J.A.M.H.)
- Department of Biomedical Signals and Systems, University of Twente, 7500 AE Enschede, The Netherlands;
| | - Randy Klaassen
- Department of Human Media Interaction, University of Twente, 7522 NB Enschede, The Netherlands; (R.K.); (J.A.M.H.)
| | - Bert-Jan F. van Beijnum
- Department of Biomedical Signals and Systems, University of Twente, 7500 AE Enschede, The Netherlands;
| | - Juliet A. M. Haarman
- Department of Human Media Interaction, University of Twente, 7522 NB Enschede, The Netherlands; (R.K.); (J.A.M.H.)
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Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. 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. In contrast to the previous state-of-the-art piezoelectric sensor-based system that used spectral features, our system fully exploits temporal amplitude-varying signals for optimal features, and thus classifies foods more accurately. Through evaluation of the frame length and the position of swallowing in the frame, we found the best performance was with a frame length of 30 samples (1.5 s), with swallowing located towards the end of the frame. This demonstrates that the chewing sequence carries important information for classification. Additionally, we present a new approach in which the weight of solid food can be estimated from the swallow count, and the calorie count of food can be calculated from their estimated weight. 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.
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Cohen R, Fernie G, Roshan Fekr A. Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature. Nutrients 2021; 13:nu13062092. [PMID: 34205234 PMCID: PMC8233832 DOI: 10.3390/nu13062092] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/13/2021] [Accepted: 06/17/2021] [Indexed: 11/23/2022] Open
Abstract
Fluid intake monitoring is an essential component in preventing dehydration and overhydration, especially for the senior population. Numerous critical health problems are associated with poor or excessive drinking such as swelling of the brain and heart failure. Real-time systems for monitoring fluid intake will not only measure the exact amount consumed by the users, but could also motivate people to maintain a healthy lifestyle by providing feedback to encourage them to hydrate regularly throughout the day. This paper reviews the most recent solutions to automatic fluid intake monitoring both commercially and in the literature. The available technologies are divided into four categories: wearables, surfaces with embedded sensors, vision- and environmental-based solutions, and smart containers. A detailed performance evaluation was carried out considering detection accuracy, usability and availability. It was observed that the most promising results came from studies that used data fusion from multiple technologies, compared to using an individual technology. The areas that need further research and the challenges for each category are discussed in detail.
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Affiliation(s)
- Rachel Cohen
- The Kite Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G2A2, Canada; (G.F.); (A.R.F.)
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S3G9, Canada
- Correspondence:
| | - Geoff Fernie
- The Kite Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G2A2, Canada; (G.F.); (A.R.F.)
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S3G9, Canada
| | - Atena Roshan Fekr
- The Kite Research Institute, Toronto Rehabilitation Institute—University Health Network, Toronto, ON M5G2A2, Canada; (G.F.); (A.R.F.)
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S3G9, Canada
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Stankoski S, Jordan M, Gjoreski H, Luštrek M. Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels. SENSORS (BASEL, SWITZERLAND) 2021; 21:1902. [PMID: 33803121 PMCID: PMC7963188 DOI: 10.3390/s21051902] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/02/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022]
Abstract
Understanding people's eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. 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. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist.
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Affiliation(s)
- Simon Stankoski
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (M.J.); (M.L.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Marko Jordan
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (M.J.); (M.L.)
| | - Hristijan Gjoreski
- Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia;
| | - Mitja Luštrek
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (M.J.); (M.L.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
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Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review. INFORMATION 2020. [DOI: 10.3390/info11110521] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Ensuring healthy lives and promoting a healthy well-being for all at all ages are listed as some of the goals in Agenda 2030 for Sustainable Development. Considering that noncommunicable diseases (NCDs) are the leading cause of death worldwide, reducing the mortality of NCDs is an important target. To reach this goal, means for detecting and reacting to warning signals are necessary. Here, remote health monitoring in real time has great potential. This article provides a systematic review of the use of wearable sensors for the monitoring and prevention of NCDs. In addition, this article not only provides in-depth information about the retrieved articles, but also discusses examples of studies assessing warning signals that may result in serious health conditions, such as stroke and cardiac arrest, if left untreated. One finding is that even though many good examples of wearable sensor systems for monitoring and controlling NCDs are presented, many issues also remain to be solved. One major issue is the lack of testing on representative people from a sociodemographic perspective. Even though substantial work remains, the use of wearable sensor systems has a great potential to be used in the battle against NCDs by providing the means to diagnose, monitor and prevent NCDs.
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Watanabe T, Shimokakimoto T, Jayatilake D, Inoue M, Suzuki K. A portable sensor sheet for measuring the eating pace in meal assistance care. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4297-4300. [PMID: 31946818 DOI: 10.1109/embc.2019.8856883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper, we introduce the development of a new sensing device for measuring the pace, time, order, and intake of meal consumption for the elderly in a nursing home. The developed device is a portable sensor sheet which is suitable for use in nursing homes because it is designed not to disturb meal consumption and can be used conveniently. We first describe the measurement method of food intake using the proposed device such as the pace, time, order of meal. Finally, we report an experiment that we conducted about eating behavior in the nursing home while using the proposed device together with a sensing device for swallowing activities.
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Kristoffersson A, Lindén M. A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis. SENSORS 2020; 20:s20051502. [PMID: 32182907 PMCID: PMC7085653 DOI: 10.3390/s20051502] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 02/26/2020] [Accepted: 03/05/2020] [Indexed: 12/19/2022]
Abstract
The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.
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Silvera-Tawil D, Hussain MS, Li J. Emerging technologies for precision health: An insight into sensing technologies for health and wellbeing. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.smhl.2019.100100] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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10
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Heydarian H, Adam M, Burrows T, Collins C, Rollo ME. Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review. Nutrients 2019; 11:E1168. [PMID: 31137677 PMCID: PMC6566929 DOI: 10.3390/nu11051168] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 01/08/2023] Open
Abstract
Wearable motion tracking sensors are now widely used to monitor physical activity, and have recently gained more attention in dietary monitoring research. The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. Eleven electronic databases were searched in January 2019, and 653 distinct records were obtained. Including 10 studies found in backward and forward searches, a total of 69 studies met the inclusion criteria, with 28 published since 2017. Fifty studies were conducted exclusively in laboratory settings, 13 exclusively in free-living settings, and three in both settings. The most commonly used motion sensor was an accelerometer (64) worn on the wrist (60) or lower arm (5), while in most studies (45), accelerometers were used in combination with gyroscopes. Twenty-six studies used commercial-grade smartwatches or fitness bands, 11 used professional grade devices, and 32 used standalone sensor chipsets. The most used machine learning approaches were Support Vector Machine (SVM, n = 21), Random Forest (n = 19), Decision Tree (n = 16), Hidden Markov Model (HMM, n = 10) algorithms, and from 2017 Deep Learning (n = 5). While comparisons of the detection models are not valid due to the use of different datasets, the models that consider the sequential context of data across time, such as HMM and Deep Learning, show promising results for eating activity detection. We discuss opportunities for future research and emerging applications in the context of dietary assessment and monitoring.
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Affiliation(s)
- Hamid Heydarian
- School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia.
| | - Marc Adam
- School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia.
- Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, Australia.
| | - Tracy Burrows
- Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, Australia.
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia.
| | - Clare Collins
- Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, Australia.
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia.
| | - Megan E Rollo
- Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, Australia.
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia.
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