Automatically Measuring Individual Consumption Events During Natural Eating Using a Table Embedded Scale

This thesis is motivated to improve the tools available for tracking energy intake. The goal of this work is to develop a table-embedded scale capable of measuring the weight of individual consumption events during unrestricted eating. The method was tested on a data set gathered from 276 subjects eating 518 courses consisting of 22,383 marked individual consumption events in a cafeteria environment. Approximately 30% of the consumption events can be detected and weighed. The remaining 70% of the events occur without participants interacting with the scale or when noisy interactions with the scale prevent weight measurement. The relationship between bite size in grams and BMI was analyzed across all 7,501 measurable bites found using ground truth bite times and across 10,240 automatically detected events. The relationship between bite weight and BMI was found to be 0.28 g/BMI. Without using ground truth bite times, a relationship of 0.21 g/BMI was found for automatically detected events. The trend is diminished but still clearly present even with the presence of false alarms. In addition, when each bin is broken into quartiles, the results indicate that g/bite vs BMI is nearly constant for the smallest 25% of bites, but increases in each quartile. When the largest 25% of bites are analyzed, a relationship of 0.58 g/BMI is found. While these amounts may seem small, the cumulative effect over hundreds or thousands of bites suggests new opportunities for behavior change based on bite size

Embedding a Grid of Load Cells into a Dining Table for Automatic Monitoring and Detection of Eating Events

This dissertation describes a “smart dining table” that can detect and measure consumption events. This work is motivated by the growing problem of obesity, which is a global problem and an epidemic in the United States and Europe. Chapter 1 gives a background on the economic burden of obesity and its comorbidities. For the assessment of obesity, we briefly describe the classic dietary assessment tools and discuss their drawback and the necessity of using more objective, accurate, low-cost, and in-situ automatic dietary assessment tools. We explain in short various technologies used for automatic dietary assessment such as acoustic-, motion-, or image-based systems. This is followed by a literature review of prior works related to the detection of weights and locations of objects sitting on a table surface. Finally, we state the novelty of this work. In chapter 2, we describe the construction of a table that uses an embedded grid of load cells to sense the weights and positions of objects. The main challenge is aligning the tops of adjacent load cells to within a few micrometer tolerance, which we accomplish using a novel inversion process during construction. Experimental tests found that object weights distributed across 4 to 16 load cells could be measured with 99.97±0.1% accuracy. Testing the surface for flatness at 58 points showed that we achieved approximately 4.2±0.5 um deviation among adjacent 2x2 grid of tiles. Through empirical measurements we determined that the table has a 40.2 signal-to-noise ratio when detecting the smallest expected intake amount (0.5 g) from a normal meal (approximate total weight is 560 g), indicating that a tiny amount of intake can be detected well above the noise level of the sensors. In chapter 3, we describe a pilot experiment that tests the capability of the table to monitor eating. Eleven human subjects were video recorded for ground truth while eating a meal on the table using a plate, bowl, and cup. To detect consumption events, we describe an algorithm that analyzes the grid of weight measurements in the format of an image. The algorithm segments the image into multiple objects, tracks them over time, and uses a set of rules to detect and measure individual bites of food and drinks of liquid. On average, each meal consisted of 62 consumption events. Event detection accuracy was very high, with an F1-score per subject of 0.91 to 1.0, and an F1 score per container of 0.97 for the plate and bowl, and 0.99 for the cup. The experiment demonstrates that our device is capable of detecting and measuring individual consumption events during a meal. Chapter 4 compares the capability of our new tool to monitor eating against previous works that have also monitored table surfaces. We completed a literature search and identified the three state-of-the-art methods to be used for comparison. The main limitation of all previous methods is that they used only one load cell for monitoring, so only the total surface weight can be analyzed. To simulate their operations, the weights of our grid of load cells were summed up to use the 2D data as 1D. Data were prepared according to the requirements of each method. Four metrics were used to evaluate the comparison: precision, recall, accuracy, and F1-score. Our method scored the highest in recall, accuracy, and F1-score; compared to all other methods, our method scored 13-21% higher for recall, 8-28% higher for accuracy, and 10-18% higher for F1-score. For precision, our method scored 97% that is just 1% lower than the highest precision, which was 98%. In summary, this dissertation describes novel hardware, a pilot experiment, and a comparison against current state-of-the-art tools. We also believe our methods could be used to build a similar surface for other applications besides monitoring consumption

Using Hidden Markov Models to Segment and Classify Wrist Motions Related to Eating Activities

Advances in body sensing and mobile health technology have created new opportunities for empowering people to take a more active role in managing their health. Measurements of dietary intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require considerable manual effort, leading to underreporting of consumption, non-compliance, and discontinued use over the long term. We are investigating the use of wrist-worn accelerometers and gyroscopes to automatically recognize eating gestures. In order to improve recognition accuracy, we studied the sequential ependency of actions during eating. In chapter 2 we first undertook the task of finding a set of wrist motion gestures which were small and descriptive enough to model the actions performed by an eater during consumption of a meal. We found a set of four actions: rest, utensiling, bite, and drink; any alternative gestures is referred as the other gesture. The stability of the definitions for gestures was evaluated using an inter-rater reliability test. Later, in chapter 3, 25 meals were hand labeled and used to study the existence of sequential dependence of the gestures. To study this, three types of classifiers were built: 1) a K-nearest neighbor classifier which uses no sequential context, 2) a hidden Markov model (HMM) which captures the sequential context of sub-gesture motions, and 3) HMMs that model inter-gesture sequential dependencies. We built first-order to sixth-order HMMs to evaluate the usefulness of increasing amounts of sequential dependence to aid recognition. The first two were our baseline algorithms. We found that the adding knowledge of the sequential dependence of gestures achieved an accuracy of 96.5%, which is an improvement of 20.7% and 12.2% over the KNN and sub-gesture HMM. Lastly, in chapter 4, we automatically segmented a continuous wrist motion signal and assessed its classification performance for each of the three classifiers. Again, the knowledge of sequential dependence enhances the recognition of gestures in unsegmented data, achieving 90% accuracy and improving 30.1% and 18.9% over the KNN and the sub-gesture HMM

Evaluation of Pedometer Performance Across Multiple Gait Types Using Video for Ground Truth

This dissertation is motivated by improving healthcare through the development of wearable sensors. This work seeks improvement in the evaluation and development of pedometer algorithms, and is composed of two chapters describing the collection of the dataset and describing the im-plementation and evaluation of three previously developed pedometer algorithms on the dataset collected. Our goal is to analyze pedometer algorithms under more natural conditions that occur during daily living where gaits are frequently changing or remain regular for only brief periods of time. We video recorded 30 participants performing 3 activities: walking around a track, walking through a building, and moving around a room. The ground truth time of each step was manu-ally marked in the accelerometer signals through video observation. Collectively 60,853 steps were recorded and annotated. A subclass of steps called shifts were identified as those occurring at the beginning and end of regular strides, during gait changes, and during pivots changing the direction of motion. While shifts comprised only .03% of steps in the regular stride activity, they comprised 10-25% of steps in the semi-regular and unstructured activities. We believe these motions should be identified separately, as they provide different accelerometer signals, and likely result in different amounts of energy expenditure. This dataset will be the first to specifically allow for pedometer algorithms to be evaluated on unstructured gaits that more closely model natural activities. In order to provide pilot evaluation data, a commercial pedometer, the Fitbit Charge 2, and three prior step detection algorithms were analyzed. The Fitbit consistently underestimated the total number of steps taken across each gait type. Because the Fitbit algorithm is proprietary, it could not be reimplemented and examined beyond a raw step count comparison. Three previously published step detection algorithms, however, were implemented and examined in detail on the dataset. The three algorithms are based on three different methods of step detection; peak detection, zero crossing (threshold based), and autocorrelation. The evaluation of these algorithms was performed across 5 dimensions, including algorithm, parameter set, gait type, sensor position, and evaluation metric, which yielded 108 individual measures of accuracy. Accuracy across each of the 5 dimensions were examined individually in order to determine trends. In general, training parameters to this dataset caused a significant accuracy improvement. The most accurate algorithm was dependent on gait type, sensor position, and evaluation metric, indicating no clear “best approach” to step detection. In general, algorithms were most accurate for regular gait and least accurate for unstructured gait. In general, accuracy was higher for hip and ankle worn sensors than it was for wrist worn sensors. Finally, evaluation across running count accuracy (RCA) and step detection accuracy (SDA) revealed similar trends across gait type and sensor position, but each metric indicated a different algorithm with the highest overall accuracy. A classifier was developed to identify gait type in an effort to use this information to improve pedometer accuracy. The classifier’s features are based on the Fast Fourier Transform (FFT) applied to the accelerometer data gathered from each sensor throughout each activity. A peak detector was developed to identify the maximum value of the FFT, the width of the peak yielding the maximum value, and the number of peaks in each FFT. These features were then applied to a Naive Bayes classifier, which correctly identified the gait (regular, semi-regular, or unstructured) with 84% accuracy. A varying algorithm pedometer was then developed which switched between the peak detection, threshold crossing, and autocorrelation based algorithms depending on which algorithm performed best for the sensor location and detected gait type. This process yielded a step detection accuracy of 84%. This was a 3% improvement when compared to the greatest accuracy achieved by the best performing algorithm, the peak detection algorithm. It was also identified that in order to provide quicker real-time transitions between algorithms, the data should be examined in smaller windows. Window sizes of 3, 5, 8, 10, 15, 20, and 30 seconds were tested, and the highest overall accuracy was found for a window size of 5 seconds. These smaller windows of time included behaviors which do not correspond directly with the regular, semi-regular, and unstructured gait activities. Instead, three stride types were identified: steady stride, irregular stride, and idle. These stride types were identified with 82% accuracy. This experiment showed that at an activity level, gait detection can improve pedometer accuracy and indicated that applying the same principles to a smaller window size could allow for more responsive real-time algorithm selection

Precision nutrition : a review of personalized nutritional approaches for the prevention and management of metabolic syndrome

The translation of the growing increase of findings emerging from basic nutritional science into meaningful and clinically relevant dietary advices represents nowadays one of the main challenges of clinical nutrition. From nutrigenomics to deep phenotyping, many factors need to be taken into account in designing personalized and unbiased nutritional solutions for individuals or population sub-groups. Likewise, a concerted effort among basic, clinical scientists and health professionals will be needed to establish a comprehensive framework allowing the implementation of these new findings at the population level. In a world characterized by an overwhelming increase in the prevalence of obesity and associated metabolic disturbances, such as type 2 diabetes and cardiovascular diseases, tailored nutrition prescription represents a promising approach for both the prevention and management of metabolic syndrome. This review aims to discuss recent works in the field of precision nutrition analyzing most relevant aspects affecting an individual response to lifestyle/nutritional interventions. Latest advances in the analysis and monitoring of dietary habits, food behaviors, physical activity/exercise and deep phenotyping will be discussed, as well as the relevance of novel applications of nutrigenomics, metabolomics and microbiota profiling. Recent findings in the development of precision nutrition are highlighted. Finally, results from published studies providing examples of new avenues to successfully implement innovative precision nutrition approaches will be reviewed

Strategies for sodium reduction in foods

El sostenido aumento en la prevalencia de hipertensión, uno de los principalesfactores de riesgo para el desarrollo de enfermedades cardiovasculares, es motivo decreciente preocupación a nivel mundial. Para hacer frente a esta situación se haexhortado a los países a tomar acciones urgentes orientadas a mejorar el estado desalud de la población. La reducción de la ingesta de sodio a nivel poblacional hamostrado ser una iniciativa costo efectiva para lograr este objetivo, dado el estrechovíncluo entre el consumo de sodio y la presión arterial. En esta línea de acción, losesfuerzos se han focalizado principalmente en la reducción del contenido de sodio deproductos procesados, que constituyen una fuente importante de sodio en la dieta. Sinembargo, la reducción del contenido de sal (cloruro de sodio) de los alimentos seenfrenta a una enorme dificultad: la preferencia del ser humano por el gusto salado y elimpacto negativo de la reducción de sodio sobre la percepción del consumidor. En estecontexto, el objetivo de la tesis fue estudiar diferentes estrategias de reducción de sodioen alimentos desde un enfoque sensorial. Para ello se consideraron dos productosextensamente consumidos por diversos segmentos de nuestra población y con granaporte de sodio a la dieta: pan y arroz. La tesis se focalizó en la reformulación dealimentos hacia contenidos menores de sodio pero también se abordó el impacto decomunicar contenido excesivo de sodio sobre la percepción del consumidor. Losresultados reafirmaron que el contenido de sal de los alimentos afecta significativamentela percepción sensorial y hedónica del consumidor. La estrategia de reducción gradualmostró gran potencial para disminuir el contenido de sodio en alimentos sin que losconsumidores perciban el cambio. Los resultados sugieren que pueden implementarsereducciones de sal cercanas al 11% en arroz y pan blanco sin afectar la percepción delconsumidor. No obstante, la principal desventaja de la reducción gradual es el tiemporequerido para disminuir la ingesta diaria de sodio a los niveles establecidos por lasmetas regionales y globales. En este sentido, la combinación de esta estrategia con lasustitución parcial de 30% de cloruro de sodio por cloruro de potasio mostró ser unaalternativa prometedora para lograr reducciones más rápidas. La inclusión deadvertencias nutricionales en el frente del paquete favoreció la elección de productoscon bajo contenido de sodio y mostró enorme potencial para modificar la percepciónhedónica del consumidor. Se espera que los resultados de esta tesis proporcioneninsumos valiosos para el diseño e implementación de programas de reducción de sodioa nivel poblacional

Organizational and Technological Aspects of a Platform for Collective Food Awareness

Can Internet-of-food technologies foster collective food awareness within a food consumer community? The paper contributes to answer this question in a fourfold aspect. Firstly, we model a cooperative process for generating and sharing reliable food information that is derived from food instrumental measurements performed by consumers via smart food things. Secondly, we outline the functional architecture of a platform capable to support such a process and to let a consumer community share reliable food information. Thirdly, we identify main entities and their attributes necessary to model the contextualized interaction between a consumer and the platform. Lastly, we review articles reviewing technologies capable of acquiring and quantifying food characteristics for food performances assessment. The purpose is to give an insight into current research directions on technologies employable in a platform for collective food awareness

Advancement in Dietary Assessment and Self-Monitoring Using Technology

Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population surveillance or the effectiveness of interventions. In comparison, dietary self-monitoring primarily aims to create awareness of and reinforce individual eating behaviours, in addition to tracking foods consumed. Advancements in the capabilities of technologies, such as smartphones and wearable devices, have enhanced the collection, analysis and interpretation of dietary intake data in both contexts. This Special Issue invites submissions on the use of novel technology-based approaches for the assessment of food and/or nutrient intake and for self-monitoring eating behaviours. Submissions may document any part of the development and evaluation of the technology-based approaches. Examples may include: web adaption of existing dietary assessment or self-monitoring tools (e.g., food frequency questionnaires, screeners) image-based or image-assisted methods mobile/smartphone applications for capturing intake for assessment or self-monitoring wearable cameras to record dietary intake or eating behaviours body sensors to measure eating behaviours and/or dietary intake use of technology-based methods to complement aspects of traditional dietary assessment or self-monitoring, such as portion size estimation

The Effect of Different Weaning Strategies on Piglet Performance and Immune Function

The weaning period in commercial pig production has been a continuing problem for both the piglet and producer. An investigation was carried out to assess the effects of different, relatively simple, management strategies on the stress associated with the weaning period in terms of piglet performance, behaviour and immune status. Three of the main areas identified as playing a key part in the stress associated with weaning were mixing of unfamiliar piglets, relocation to a new environment and adaptation to a new diet. Management strategies were designed relating to assess these factors. The studies highlighted that mixing piglets pre-weaning at 14 days of age was a positive step to aid the transition of weaning, in terms of performance and immune status and requires little adaptation of current farrowing systems. Mixing at either 7 or 21 days of age does not show the same improvements in post-weaning performance. Remaining in a familiar environment was not beneficial, and may possibly be detrimental, to the newly weaned piglet and indeed, relocation to a novel environment appears to be important for the piglet psychologically and motivates the piglet to explore its new environment and search for alternative food sources. The benefits of creep feeding still needs further consideration in order to determine the optimal feeding strategy in relation to feed intakes pre- and post-weaning combined with the effects of creep feed on the underdeveloped digestive tract. Although it is not possible to completely remove the growth check that occurs post-weaning, it is clear that management strategies can be utilised to reduce the stress associated with weaning and improve post-weaning growth rates