Fuzzy Classifier Based Ingestive Monitor

The observation of food intake and ingestive behavior remains an open problem that has significant implications in the study and treatment of obesity and eating disorders. A novel method of fusing a sensor and pattern recognition method was developed to detect periods of food intake based on non-invasive monitoring of chewing. A surface-type EMG electrode was used to capture the movement of the lower jaw from volunteers during periods of quiet sitting, and food consumption. These signals were processed to extract the most relevant features, identifying from 4 to 10 features most critical for classifying the type of food consumed. Fuzzy classifiers were trained to create food intake, detection models. The simplicity of the sensor may result in a less intrusive and simpler way to detect food intake. The proposed system is implemented using LabVIEW. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals and also to calculate the quantity of food intake

A novel approach for food intake detection using electroglottography

Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an electroglottograph (EGG) device for an objective and automatic detection of food intake. Thirty subjects participated in a four-visit experiment involving the consumption of meals with self-selected content. Variations in the electrical impedance across the larynx caused by the passage of food during swallowing were captured by the EGG device. To compare performance of the proposed method with a well-established acoustical method, a throat microphone was used for monitoring swallowing sounds. Both signals were segmented into non-overlapping epochs of 30 s and processed to extract wavelet features. Subject-independent classifiers were trained, using artificial neural networks, to identify periods of food intake from the wavelet features. Results from leave-one-out cross validation showed an average per-epoch classification accuracy of 90.1% for the EGG-based method and 83.1% for the acoustic-based method, demonstrating the feasibility of using an EGG for food intake detection.Fil: Farooq, Muhammad. University of Alabama; Estados UnidosFil: Fontana, Juan Manuel. University of Alabama; Estados Unidos. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Sazonov, Edward. University of Alabama; Estados Unido

A review of chewing detection for automated dietary monitoring

A healthy dietary lifestyle prevents diseases and leads to good physical conditions. Poor dietary habits, such as eating disorders, emotional eating and excessive unhealthy food consumption, may cause health complications. People’s eating habits are monitored through automated dietary monitoring (ADM), which is considered a part of our daily life. In this study, the Google Scholar database from the last 5 years was considered. Articles that reported chewing activity characteristics and various wearable sensors used to detect chewing activities automatically were reviewed. Key challenges, including chew count, various food types, food classification and a large number of samples, were identified for further chewing data analysis. The chewing signal’s highest reported classification accuracy value was 99.85%, which was obtained using a piezoelectric contactless sensor and multistage linear SVM with a decision tree classifier. The decision tree approach was more robust and its classification accuracy (75%–93.3%) was higher than those of the Viterbi algorithm-based finite-state grammar approach, which yielded 26%–97% classification accuracy. This review served as a comparative study and basis for developing efficient ADM systems

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Detection of Swallowing Events to Quantify Fluid Intake in Older Adults Based on Wearable Sensors

The percentage of adults aged 65 an over, defined as older adults, is prospected to increase in the coming decades over the total population. With such an increase, it is essential that healthcare technologies evolve to cater for the needs of an aging population. One such need is hydration: due to both physiological and psychological reasons, older adults tend to be more prone to develop dehydration, which in turn increases the chances of morbidity and mortality. Currently, there are no gold standards to monitor hydration, with most methods relying on filling manual forms. Thus, there is an urgent need to develop techniques which can accurately monitor fluid intake and prevent dehydration in older adult, especially those residing in healthcare settings. Several methods, such as smart cups and on-body sensors such as microphones, were proposed in the literature, however none of these have been widely investigated, and often the presented results were based on an extremely small cohort. Therefore, the scope of this PhD project is to investigate and develop methods that can detect swallowing events and that can quantify the volume of ingested fluids by leveraging on signals harvested using non-invasive, on-body sensors. Two types of on-body sensors were selected and used throughout this research: namely surface Electromyographic sensors (sEMG) and microphones. These sensors were then used to collect sound and electric signals from the subjects while swallowing boluses of different viscosities and while performing actions not related to swallowing but that could recruit the same muscles or produce similar sounds, such as talking or coughing. Features were then extracted from the collected observations and used to train Machine Learning (ML) and Deep Learning (DL) models to analyse their ability to differentiate between swallowing and non-swallowing actions, to distinguish between different bolus types, and to quantify the volume of fluid ingested. Results showed a precision of 81.55±3.40% in differentiating between swallows and non-swallows and a precision of 81.74±8.01% in distinguishing between bolus types, both given by the sEMG. Also, a root mean square error (RMSE) of 3.94±1.31 ml in estimating fluid intake was obtained using the microphone. The significance of the findings exposed in this thesis rely on the fact that surface EMGs and microphones demonstrate a significant potential in fluid intake monitoring, and on the concrete possibility of developing a non-invasive, reliable system that could prevent dehydration in older adults living in healthcare settings

Objective quantification and analysis of eating behaviour associated with obesity development - from lab to real-life

Introduction: The last four decades have seen a marked increase in childhood and adult obesity prevalence, attributed to an “obesogenic” environment. Several genetical, environmental and behavioural factors have been identified that increase the risk of obesity, but treatment outcomes are usually modest and the risk of relapse high. One limitation responsible for these moderate results could be methodological, with researchers questioning both the external validity of eating behaviour measures in the laboratory (controlled) and the internal validity of eating behaviour measures in free-living (real-life) settings. Technological advances could solve some of these issues, allowing for accurate methods, similar to those used in controlled settings, to be used in real- life. Deploying accurate methods in both controlled and real-life settings would in turn enable the estimation of external validity, determining the limits of generalization between settings. In turn enabling the deployment of these methods in settings which allow large scale screening, for early identification of individuals at risk of becoming obese. Aim: The overarching aim of the thesis was to: i) evaluate the stability of human eating behaviour and ii) investigate the usability and feasibility of methods developed for controlled settings, when deployed in semi-controlled and real-life settings. Paper I – Determine if individuals maintain their eating behaviour, in relation to the group, despite experimental manipulations to meal conditions (i.e., unit sizes and serving occasion). Paper II – Feasibility of employing novel technology for baseline eating behaviour collection in adolescents eating school lunches in a school cafeteria setting (semi-controlled). Paper III – Feasibility of employing novel technology in an experimental manipulation study, to determine the effect of proximity in a semi-controlled school setting. Paper IV – By use of novel technology, examine the maintenance of eating behaviours in adolescents, from semi-controlled to real-life settings, both at group- and individual-level. Methods: Paper I – Three randomised crossover studies, of which two compared eating behaviour across different unit sizes, while one compared eating behaviour between lunch and dinner in healthy young adults. Performed in a controlled setting, employing traditional laboratory methods. Paper II – An observational study of healthy adolescents, performed at lunch in a school cafeteria, employing traditional laboratory methods in a semi-controlled setting. Paper III – A randomised experimental study of healthy adolescents, performed in a semi- controlled, comparing the eating behaviour between two groups seated at different proximity to food items. Paper IV – An observational study on eating behaviour of healthy adolescents, divided into two parts; i) collection of eating behaviour data, performed at lunch in a school cafeteria, using a similar protocol to that of Paper II and ii) collection of eating behaviour data by the participants in real-life settings, using the same devices as in the controlled setting. Results: In all papers the distribution of eating behaviour values between individuals were large. In Paper I, the largest increase in unit size significantly increased meal duration and chews and while there was a trend for both increased meal duration and number of chews the larger the food unit sizes were, it did not lead to a significant reduction in food intake. Meanwhile, the correlation coefficient of all eating behaviours across all conditions was high (except for number of bites between the largest and smallest food unit size condition). In Paper II, male participants ate significantly more, mediated by significantly larger bites. The bite sizes of both men and women were reduced as the meal progressed. In Paper III, increased distance to food led to a significant reduction in intake, caused by individuals taking less chocolate. In Paper IV, there was no significant difference in eating behaviour characteristics between the semi- controlled and real-life meals. In addition, the correlation coefficient of food intake and eating rate was high between settings, while the correlation of meal duration was low. Also, on an individual level, 50%, 32% and 27% of the food intake, eating rate and meal duration measures, respectively, from the semi-controlled meal fell within the confidence interval of the real-life meals. In the semi-controlled and real-life settings (Papers II-IV), the agreement between subjective and objective eating behaviour measures were very low. Meanwhile, in both semi- controlled and real-life settings the method could be deployed within the time schedule imposed by the school, with high data retention. Also, participants rated the comfortability participating in the semi-controlled and real-life settings very high and the usability of the system as “Good” or higher. Conclusions: Human eating behaviour appears stable in comparison to the group when unit size and serving occasion is manipulated in a controlled setting and when eating in different settings (semi- controlled and real-life). Suggesting generalisations can be made between settings and conditions and that risk behaviours may be measured in settings other than real-life, at least on group level. However, although individual prediction rates of eating behaviour characteristics from semi-controlled setting to real-life settings appears higher than subjective ratings, they are still too low for use in the design of tailored interventions. In addition, compared to controlled studies, the method allowed recruitment of a younger age group, since it enabled measurements in a different location. The thesis also provides evidence that the employed methods are usable, feasible and acceptable, with high data retention in adolescent users, in semi-controlled and real-life settings. Methods similar to the ones used in this thesis can provide previously unattainable information (primarily temporal) in settings that are less controlled than the laboratory, such as semi-controlled and real-life