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

Resilience Culture in the Healthcare Team During COVID-19

Abstract Background: Resilience commonly refers to the ability of an individual or organization to continue to maintain routine, normal, function despite sudden disruptions. Purpose: The purpose of this dissertation research was to provide a deeper understanding of healthcare team resilience. The goal of this research dissertation was to investigate how resilience manifested itself in the healthcare team during the COVID-19 pandemic. AIM 1: What is the concept of resilience in healthcare teams? AIM 2: Identify the barriers and facilitators of healthcare team resilience during the COVID-19 pandemic. AIM 3: Describe how the pandemic influenced healthcare team decision making. Methods: In the first manuscript we performed a comprehensive systematic analysis that delves into the concept of healthcare team resilience in the literature. Based on these results, in the second manuscript the authors utilized an adapted model developed by the research team that frames the healthcare team as a cohesive and aware entity, rather than merely a group of individuals or a subset of personnel within a healthcare system. Finally, the third manuscript uses this adapted model to present research findings from interviews on resilience culture, based on a thematic analysis. Findings: In chapter 2, we found 41 distinct definitions of the concept, with three defining attributes: 1) resilience is triggered by an a priori disruptive event that serves as a catalyst activating the healthcare team\u27s latent potential; 2) this potential leads to the actualization of skills and abilities that enable the team to respond to the disruption in an adaptive manner; 3) the team’s adaptive response enables them to continue executing responsibilities in the face of the disruption. This contributed to AIM 1 by describing the concept of resilience in healthcare teams during COVID-19. The concept analysis brought to light a significant disparity arising from the prevailing literature primarily emphasizing individual resilience as a lens to understand healthcare team resilience, thus potentially obscuring any hidden aspects of resilience within the healthcare team. This discrepancy underscored the necessity to develop a comprehensive model to explore healthcare team resilience during COVID-19 that acknowledges the healthcare team as a singular cognizant entity and not an individual or group of individuals. In chapter 3, we found by integrating knowledge and principles from the domains of resilience engineering, systems engineering, patient safety, and naturalistic decision- making we could create a framework by which AIM 2 and AIM 3 could be addressed. An adapted model was created. The exploration of the barriers and facilitators of resilience and the impact of COVID-19 on the decision-making processes in healthcare teams could be thoroughly explored using the adapted model. A qualitative descriptive study was conducted in 2021 and data were analyzed using reflexive thematic analysis. Chapter 4 presents the findings of this study related to AIM 2 and AIM 3. The study utilized the adapted model as a guide for the interview questions. The author developed the interview questions, which were reviewed and approved by faculty mentors. The author interviewed (N=22) interprofessional healthcare participants who worked during the COVID-19 pandemic. A thematic analysis of the interview data resulted in the identification of five themes related to resilience in the healthcare team during COVID-19: working in a pressure cooker; healthcare team cohesion; applying past lessons to current challenges; knowledge gaps, and altruistic behaviors. The evidence indicates that the pressures form working during COVID-19 and gaps in explicit knowledge, negatively influenced adaptive behaviors to maintain healthcare team resilience. Team cohesion, tacit knowledge and altruistic behaviors positively influenced adaptive behaviors and decision making. Conclusion: This compendium presents the exploration of resilience within healthcare teams amidst the challenges posed by the COVID-19 pandemic. The literature review revealed that the conventional approach to understanding the concept and measuring healthcare team resilience primarily focused on individual resilience. However, this research recognized the need for an adapted model that recognizes the healthcare team as a cohesive and cognizant entity to identify barriers and facilitators of resilience that may be otherwise obscured when solely emphasizing the resilience of individuals, or specific groups. Through a reflexive thematic analysis, several significant findings were identified regarding the impact of the COVID-19 pandemic on the healthcare team: 1) Emotionality played a crucial role in influencing adaptive behaviors, encompassing emotions such as fear, stress, anxiety, and frustration; 2) Drawing upon their tacit knowledge gained from prior experiences, the healthcare team demonstrated the capacity to anticipate and effectively respond to the crisis despite their lack of explicit knowledge, and 3) The solidarity and camaraderie within the healthcare team not only bolstered their overall functionality but also facilitated unified decision-making processes