Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions

A First Order Predicate Logic Formulation of the 3D Reconstruction Problem and its Solution Space

This paper defines the 3D reconstruction problem as the process of reconstructing a 3D scene from numerous 2D visual images of that scene. It is well known that this problem is ill-posed, and numerous constraints and assumptions are used in 3D reconstruction algorithms in order to reduce the solution space. Unfortunately, most constraints only work in a certain range of situations and often constraints are built into the most fundamental methods (e.g. Area Based Matching assumes that all the pixels in the window belong to the same object). This paper presents a novel formulation of the 3D reconstruction problem, using a voxel framework and first order logic equations, which does not contain any additional constraints or assumptions. Solving this formulation for a set of input images gives all the possible solutions for that set, rather than picking a solution that is deemed most likely. Using this formulation, this paper studies the problem of uniqueness in 3D reconstruction and how the solution space changes for different configurations of input images. It is found that it is not possible to guarantee a unique solution, no matter how many images are taken of the scene, their orientation or even how much color variation is in the scene itself. Results of using the formulation to reconstruct a few small voxel spaces are also presented. They show that the number of solutions is extremely large for even very small voxel spaces (5 x 5 voxel space gives 10 to 10(7) solutions). This shows the need for constraints to reduce the solution space to a reasonable size. Finally, it is noted that because of the discrete nature of the formulation, the solution space size can be easily calculated, making the formulation a useful tool to numerically evaluate the usefulness of any constraints that are added

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities

Tracking using a local closed-world assumption : tracking in the football domain

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1994.Includes bibliographical references (leaves 81-85).by Stephen Sean Intille.M.S

Visual recognition of multi-agent action

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (p. 167-184).Developing computer vision sensing systems that work robustly in everyday environments will require that the systems can recognize structured interaction between people and objects in the world. This document presents a new theory for the representation and recognition of coordinated multi-agent action from noisy perceptual data. The thesis of this work is as follows: highly structured, multi-agent action can be recognized from noisy perceptual data using visually grounded goal-based primitives and low-order temporal relationships that are integrated in a probabilistic framework. The theory is developed and evaluated by examining general characteristics of multi-agent action, analyzing tradeoffs involved when selecting a representation for multi-agent action recognition, and constructing a system to recognize multi-agent action for a real task from noisy data. The representation, which is motivated by work in model-based object recognition and probabilistic plan recognition, makes four principal assumptions: (1) the goals of individual agents are natural atomic representational units for specifying the temporal relationships between agents engaged in group activities, (2) a high-level description of temporal structure of the action using a small set of low-order temporal and logical constraints is adequate for representing the relationships between the agent goals for highly structured, multi-agent action recognition, (3) Bayesian networks provide a suitable mechanism for integrating multiple sources of uncertain visual perceptual feature evidence, and (4) an automatically generated Bayesian network can be used to combine uncertain temporal information and compute the likelihood that a set of object trajectory data is a particular multi-agent action. The recognition algorithm is tested using a database of American football play descriptions. A system is described that can recognize single-agent and multi-agent actions in this domain given noisy trajectories of object movements. The strengths and limitations of the recognition system are discussed and compared with other multi-agent recognition algorithms.by Stephen Sean Intille.Ph.D

Acquiring in situ training data for context-aware ubiquitous computing applications

Ubiquitous, context-aware computer systems may ultimately enable computer applications that naturally and usefully respond to a user's everyday activity. Although new algorithms that can automatically detect context from wearable and environmental sensor systems show promise, many of the most flexible and robust systems use probabilistic detection algorithms that require extensive libraries of training data with labeled examples. In this paper, we describe the need for such training data and some challenges we have identified when trying to collect it while testing three contextdetection systems for ubiquitous computing and mobile applications. Author Keywords Context-aware, ubiquitous, computing, supervised learning, experience sampling, user interface design ACM Classification Keywords H5.m Information interfaces and presentation (e.g. HCI): Miscellaneous

Designing for Diabetes Decision Support Systems with Fluid Contextual Reasoning

Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technolo- gies such as blood glucose meters have long been an essen- tial part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cog- nitive tasks. However, evidence from the first of two stud- ies we present suggests that in demanding and complex situations, some individuals approach disease management in distinctive ways that do not seem to fit well within existing models. This finding motivated, and helped frame our second study, a survey (n=192) to investigate these behaviors in more detail. On the basis of the resulting analysis, we posit Fluid Contextual Reasoning to explain how some people with diabetes respond to particular situations, and discuss how an extended framework might help inform the design of user interfaces for diabetes management

Understanding longitudinal ecological momentary assessment completion: results from 12 months of burst sampling in the TIME study

Background: Ecological momentary assessment (EMA) is a valuable method for capturing real-time data on behaviors and experiences in naturalistic settings. However, maintaining participant engagement in longitudinal (ie, multiburst) EMA studies remains challenging, particularly when collecting intensive data over extended periods. Understanding factors affecting completion rates is essential for designing more effective EMA protocols and interpreting results accurately. Objective: This study investigated factors influencing EMA completion rates in a 12-month intensive longitudinal study among young adults in the United States, examining both time-varying factors and stable individual characteristics. Methods: Young adults (N=246, ages 18-29 years) participated in the Temporal Influences on Movement and Exercise (TIME) study, responding to smartphone-based EMA prompts during biweekly measurement bursts (4-day periods of intensive sampling), with continuous passive data collection via smartwatches. Each burst included signal-contingent prompts delivered approximately once per hour during waking hours, resulting in an average of 12.1 (SD 1.3) prompts per day. Multilevel logistic regression models examined the effects of time-varying temporal factors (time of day, day of week, season, and time in study), contextual factors (phone screen status, phone usage, and location), behavioral factors (sleep duration, physical activity levels, and travel status), and psychological factors (momentary affect and stress) on prompt completion. Models also included time-invariant demographic characteristics (sex, race, ethnicity, education, and employment) and tested interactions between time in study and other predictors. Results: Mean completion rate was 77% (SD 13%). Hispanic participants showed lower odds of completion compared to non-Hispanic participants (odds ratio [OR] 0.79, 95% CI 0.63-0.99; P=.04) and employed participants were less likely to complete prompts than unemployed participants (OR 0.75, 95% CI 0.61-0.92; P<.01). Having the phone screen on at prompt delivery substantially increased completion odds (OR 3.39, 95% CI 2.81-4.09; P<.001), while being away from home reduced completion likelihood, with particularly low odds when at sports facilities (OR 0.58, 95% CI 0.47-0.74; P<.001) or restaurants and shops (OR 0.61, 95% CI 0.51-0.72; P<.001). Short sleep duration the previous night (OR 0.92, 95% CI 0.87-0.99; P=.02) and traveling status (OR 0.78, 95% CI 0.75-0.82; P<.001) were associated with lower completion odds. Higher momentary stress levels predicted lower completion of subsequent prompts (OR 0.85, 95% CI 0.78-0.93; P<.001). Completion odds declined over the 12-month study (OR 0.95, 95% CI 0.94-0.96; P<.001), with significant interactions between time in study and various predictors, indicating changing patterns of engagement over time. Conclusions: Findings highlight the dynamic nature of EMA engagement in longitudinal multiburst studies and underscore the importance of considering time-varying and time-invariant factors in study design and analysis. This study provides valuable insights for researchers designing intensive longitudinal studies in behavioral science and digital health. Potential strategies for optimizing EMA protocols could include tailoring prompt schedules to individual contexts and developing adaptive sampling techniques