Privacy in Mobile Technology for Personal Healthcare

Information technology can improve the quality, efficiency, and cost of healthcare. In this survey, we examine the privacy requirements of \emphmobile\/ computing technologies that have the potential to transform healthcare. Such \emphmHealth\/ technology enables physicians to remotely monitor patients\u27 health, and enables individuals to manage their own health more easily. Despite these advantages, privacy is essential for any personal monitoring technology. Through an extensive survey of the literature, we develop a conceptual privacy framework for mHealth, itemize the privacy properties needed in mHealth systems, and discuss the technologies that could support privacy-sensitive mHealth systems. We end with a list of open research questions

Ambulatory Energy Expenditure Estimation: A Machine Learning Approach

This paper presents a machine learning approach for accurate estimation of energy expenditure using a fusion of accelerometer and heart rate sensing. To address short comings in existing off-the-shelf solutions, we designed Jog Falls, an end to end system for weight management in collaboration with physicians in India. This system is meant to enable people to accurately monitor their energy expenditure and intake and make educated tradeoffs to reach their weight goals. In this paper we describe the sensing components of Jog Falls and focus on the energy expenditure estimation algorithm. We present results from controlled experiments in the lab, as well results from a 15 participant user study over a period of 63 days. We show how our algorithm mitigates many of the issues in existing solutions and yields more accurate results

A Privacy Framework for Mobile Health and Home-Care Systems

In this paper, we consider the challenge of preserving patient privacy in the context of mobile healthcare and home-care systems, that is, the use of mobile computing and communications technologies in the delivery of healthcare or the provision of at-home medical care and assisted living. This paper makes three primary contributions. First, we compare existing privacy frameworks, identifying key differences and shortcomings. Second, we identify a privacy framework for mobile healthcare and home-care systems. Third, we extract a set of privacy properties intended for use by those who design systems and applications for mobile healthcare and home-care systems, linking them back to the privacy principles. Finally, we list several important research questions that the community should address. We hope that the privacy framework in this paper can help to guide the researchers and developers in this community, and that the privacy properties provide a concrete foundation for privacysensitive systems and applications for mobile healthcare and home-care systems

Computational investigation of thallium interactions with functionalized multi-walled carbon nanotubes for electrochemical sensing applications

Thallium (Tl) is a heavy toxic element which can cause several health issues. WHO and EPA have set a maximum permissible limit for thallium in drinking water above which it is hazardous, so its determination in our environment becomes crucial. Multi-walled carbon nanotubes (MWCNTs) are preferred for use in thallium sensing due to their large surface area and high conductivity, which allow them to be readily functionalized to selective groups. Previous experimental results showed that Tl selectively interacted with the MWCNTs functionalized with 3-amino-1,2,4-triazole-5-thiol (T-MWCNTs) with a limit of detection of 1.29 μg L−1 and linear range 10–100 μg L−1 by using voltammetry under optimized conditions. In actual water samples, the electrochemical sensor fabricated with the above-mentioned functionalized MWCNTs nanocomposite demonstrated high reproducibility and recovery. Molecular recognition and the outcomes of chemical and biological processes are shaped by non-covalent interactions among molecules. It is essential to investigate how these interactions impact binding preferences to enhance our understanding of these events. Here, we examine the structures of complexes of Tl and T-MWCNTs using quantum chemical calculations. Our results show that the most favourable complex of Tl-T-MWCNTs involve strong interaction of Tl with the nitrogen lone pair and additional stabilising interaction provided by the oxygen lone pair of amide linkage of T-MWCNTs. Moreover, we observed that the thiol group within T-MWCNTs readily undergoes deprotonation due to its acidic nature. Non-covalent interactions among molecules influence chemical and biological processes and molecular recognition. To improve our knowledge of these events, it is important to explore the ways in which these interactions affect binding preferences The negative value of adsorption energy (−1.53 eV) of this structure suggested that the interaction process between Tl and T-MWCNTs is spontaneous

Joint Resource Scheduling for AMR Navigation Over Wireless Edge Networks

The future of autonomous systems will rely on the usage of wireless time-sensitive networks to connect mobile cyberphysical systems, such as Autonomous Mobile Robots (AMRs), to Edge compute platforms to offload computationally intensive workloads necessary to complete tasks. In the case of AMRs, due to their mobility, the offloading of expensive processes such as localization and tracking methods to the Edge computing infrastructure must also be done over dynamic wireless networks. In larger scale systems, the network and compute resource requirements can quickly become prohibitively large due to network traffic and heavy workloads and tight deadline requirements for proper execution of time-critical tasks. In this paper, we formulate the problem of jointly allocating network and compute resources for time sensitive systems as the state of the wireless channel changes over time. By characterizing a compute model for AMR workloads, we further demonstrate how the network and compute scheduling decisions can be serialized, thus making the optimal scheduling problem significantly more tractable, via the incorporation of a compute-utility aware network cost function. Simulation results of AMR systems in a Wi-Fi network demonstrate substantial gains over baseline scheduling methods in total resource efficiency

Jog Falls: A Pervasive Healthcare Platform for Diabetes Management

Abstract. This paper presents Jog Falls, an end to end system to manage diabetes that blends activity and energy expenditure monitoring, diet-logging, and analysis of health data for patients and physicians. It describes the architectural details, sensing modalities, user interface and the physician’s backend portal. We show that the body wearable sensors accurately estimate the energy expenditure across a varied set of active and sedentary states through the fusion of heart rate and accelerometer data. The GUI ensures continuous engagement with the patient by showing the activity goals, current and past activity states and dietary records along with its nutritional values. The system also provides a comprehensive and unbiased view of the patient’s activity and food intake trends to the physician, hence increasing his/her effectiveness in coaching the patient. We conducted a user study using Jog Falls at Manipal University, a leading medical school in India. The study involved 15 participants, who used the system for 63 days. The results indicate a strong positive correlation between weight reduction and hours of use of the system