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For any successful business endeavor, recruitment of required number of appropriately qualified employees in proper positions is a key requirement. For effective utilization of human resources, reorganization of such workforce assignment is also a task of utmost importance. This includes situations when the under-performing employees have to be substituted with fresh applicants. Generally, the number of candidates applying for a position is large and hence, the task of identifying an optimal subset becomes critical. Moreover, a human resource manager would also like to make use of the opportunity of retirement of employees to improve manpower utilization. However, the constraints enforced by the security policies prohibit any arbitrary assignment of tasks to employees. Further, the new employees should have the capabilities required to handle the assigned tasks. In this article, we formalize this problem as the Optimal Recruitment Problem (ORP), wherein the goal is to select the minimum number of fresh employees from a set of candidates to fill the vacant positions created by the outgoing employees, while ensuring satisfiability of the specified security conditions. The model used for specification of authorization policies and constraints is Attribute Based Access Control (ABAC), since it is considered to be the de facto next generation framework for handling organizational security policies. We show that the ORP problem is NP-hard and propose a greedy heuristic for solving it. Extensive experimental evaluation shows both the effectiveness as well as efficiency of the proposed solution.
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BACKGROUND: Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists. OBJECTIVE: The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization. METHODS: Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations. RESULTS: Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (χ24=14.1, P=.002) and monitoring (χ24=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for "monitoring" between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups. CONCLUSIONS: We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine-chosen as the basis for the construction of present taxonomy-was confirmed.
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Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk. In this paper, we describe the design, collection, and analysis of text messages from individuals with a history of suicidal thoughts and behaviors to build a model to identify periods of suicidality (i.e., suicidal ideation and non-fatal suicide attempts). By reconstructing the timeline of recent suicidal behaviors through a retrospective clinical interview, this study utilizes a prospective research design to understand if text communications can predict periods of suicidality versus depression. Identifying subtle clues in communication indicating when someone is at heightened risk of a suicide attempt may allow for more effective prevention of suicide.
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Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities.
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Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors - breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.
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Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days ( for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.
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Robots have potential to provide assistance to healthcare providers in daily caregiving tasks. The healthcare providers' acceptance of assistive robots will mediate the success or failure of implementation of robotic systems in care settings. It is essential to understand why and how providers would accept implementation of a robot in their daily work routines. We identified caregiving tasks with which healthcare providers would or would not accept assistance from a personal robot (Willow Garage's PR2). We also explored preferences for human or robot assistance. The healthcare providers we interviewed were quite open to the idea of receiving robot assistance for certain tasks.
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The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous sensing and inference of social interactions and physical activities.