AmicroN: A Framework for Generating Annotations for Human Activity Recognition with Granular Micro-Activities

Efficient human activity recognition (HAR) using sensor data needs a significant volume of annotated data. The growing volume of unlabelled sensor data has challenged conventional practices for gathering HAR annotations with human-in-the-loop approaches, often leading to the collection of shallower annotations. These shallower annotations ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, first analyze this lack of granular annotations from available pre-annotated datasets to understand the practical inconsistencies and also perform a detailed survey to look into the human perception surrounding annotations. Drawing motivations from these, we next develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures and the available coarse-grain macro-activity labels. In the backend, AmicroN applies change-point detection followed by zero-shot learning with activity embeddings to identify the unseen micro-activities in an unsupervised manner. Rigorous evaluation on publicly available datasets shows that AmicroN can accurately generate micro-activity annotations with a median F1-score of >0.75. Additionally, we also show that AmicroN can be used in a plug-and-play manner with Large Language Models (LLMs) to obtain the micro-activity labels, thus making it more practical for realistic applications.Comment: 27 pages, 5 tables, 9 figure

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

Recent NVIDIA Graphics Processing Units (GPUs) can execute multiple kernels concurrently. On these GPUs, the thread block scheduler (TBS) uses the FIFO policy to schedule their thread blocks. We show that FIFO leaves performance to chance, resulting in significant loss of performance and fairness. To improve performance and fairness, we propose use of the preemptive Shortest Remaining Time First (SRTF) policy instead. Although SRTF requires an estimate of runtime of GPU kernels, we show that such an estimate of the runtime can be easily obtained using online profiling and exploiting a simple observation on GPU kernels' grid structure. Specifically, we propose a novel Structural Runtime Predictor. Using a simple Staircase model of GPU kernel execution, we show that the runtime of a kernel can be predicted by profiling only the first few thread blocks. We evaluate an online predictor based on this model on benchmarks from ERCBench, and find that it can estimate the actual runtime reasonably well after the execution of only a single thread block. Next, we design a thread block scheduler that is both concurrent kernel-aware and uses this predictor. We implement the SRTF policy and evaluate it on two-program workloads from ERCBench. SRTF improves STP by 1.18x and ANTT by 2.25x over FIFO. When compared to MPMax, a state-of-the-art resource allocation policy for concurrent kernels, SRTF improves STP by 1.16x and ANTT by 1.3x. To improve fairness, we also propose SRTF/Adaptive which controls resource usage of concurrently executing kernels to maximize fairness. SRTF/Adaptive improves STP by 1.12x, ANTT by 2.23x and Fairness by 2.95x compared to FIFO. Overall, our implementation of SRTF achieves system throughput to within 12.64% of Shortest Job First (SJF, an oracle optimal scheduling policy), bridging 49% of the gap between FIFO and SJF.Comment: 14 pages, full pre-review version of PACT 2014 poste

Empirical Investigations Into The Causal Impact Of Healthcare Provider Behavior On Patient Care

This dissertation in operations management focuses on the study of healthcare operations management using large-scale empirical datasets and econometric methods. In chapter one, we utilize infrared location tracking data to study the impact of physical facility layout on how service workers organize their tasks. We focus on the hospital emergency department as a service setting where nurses (servers) have discretion over how they interact with their patients (customers) in a facility that introduces significant heterogeneity in necessary walking distance. Our findings show that even in services, the spatial organization of a facility can lead to servers with discretion over task timing using that discretion in ways that help the server but that lead to reduced customer quality. In chapter two, we examine the hospital intensive care unit (ICU) to investigate the impact of exogenous medication delays, introduced by shift changes, on granular patient health outcomes. The ICU is an ideal setting for this research because patients are often in critical condition and require medications to remain in healthy states (as measured by vital signs). Using patient vital sign data electronically archived every few minutes, merged with the electronic medical record and the medication order/delivery database, we are able to estimate the marginal impact of a minute of medication delay on patient vital status following the late medication. Beyond providing actionable, data-driven insight to managers and healthcare practitioners surrounding how we can better enable workers to maximize effectiveness and efficiency, the research in this dissertation utilizes novel large-scale datasets, unique econometric techniques, and innovative measurement of health outcomes

Virtually connected, practically mobile

This is the post-print version of the Chapter. The official published version can be accessed from the links below - Copyright @ 2006 SpringerThis chapter addresses a central issue in studies of mobile work and mobile technology – what is the work of mobile workers, and how do they use the resources that they have to undertake this work (i.e. the work they have to do in order to do their work)? In contrast to many of the other papers in this collection, the objective of this chapter is to examine individual mobile work, and not teamwork and co-operation other than where it impacts on the work of individuals. We present data from a study of mobile workers, examining a range of mobile workers to produce a rich picture of their work. Our analysis reveals insights into how mobile workers mix their mobility with their work, home and social lives, their use of mobile technology, the problems – technological and otherwise – inherent in being mobile, and the strategies that they use to manage their work, time, other resources and availability. Our findings demonstrate important issues in understanding mobile work, including the maintenance of communities of practice, the role and management of interpersonal awareness and co-ordination, how environmental resources affect activity, the impact of mobility on family/social relationships and the crossover between the mobile workers’ private and working lives, how preplanning is employed prior to travel, and how mobile workers perform activity multitasking, for example through making use of ‘dead time’. Finally, we turn to the implications of this data for the design and deployment of mobile virtual work (MVW) technologies for individuals and a broader organisational context

Crowded Social Media: Investigating the Crowdedness Effect on Social Media Usage

While public transit is a major place where people devote their time to smartphones, particularly social media usage, the literature has paid scant attention to how contextual factors in public transit affect the individual usage behavior of social media. Building upon prior literature on stress and social media as a stress reliever, this study examines the impact of physical crowdedness on users’ content consumption and generation behaviors on social media platforms. In collaboration with a major wireless telecom provider in China, we collect detailed information on smartphone usage behavior among 200,000 randomly selected individuals on a major public subway line in Qingdao, China. The results demonstrate a positive causal relationship between crowdedness and content consumption and generation on social media platforms, even after controlling for potential endogeneity of crowdedness

Modality-Specific Effects of Perceptual Load in Multimedia Processing

Digital media are sensory-rich, multimodal, and often highly interactive. An extensive collection of theories and models within the field of media psychology assume the multimodal nature of media stimuli, yet there is current ambiguity as to the independent contributions of visual and auditory content to message complexity and to resource availability in the human processing system. In this article, we argue that explicating the concepts of perceptual and cognitive load can create progress toward a deeper understanding of modality-specific effects in media processing. In addition, we report findings from an experiment showing that perceptual load leads to modality-specific reductions in resource availability, whereas cognitive load leads to a modality-general reduction in resource availability. We conclude with a brief discussion regarding the critical importance of separating modality-specific forms of load in an increasingly multisensory media environment