Abstract-Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-andtreat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.

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h i g h l i g h t sThe energy stored in a supercapacitor cannot be determined by terminal voltage alone. Kalman state tracking with a three branch model improves stored energy awareness. A novel estimation technique enables in-situ estimation of required model parameters. The proposed method accurately determines the energy buffered in a supercapacitor.Keywords: Supercapacitor Three branch model State of charge Parameter estimation Kalman Energy awareness a b s t r a c t Among energy buffering alternatives, supercapacitors can provide unmatched efficiency and durability. Additionally, the direct relation between a supercapacitor's terminal voltage and stored energy can improve energy awareness. However, a simple capacitive approximation cannot adequately represent the stored energy in a supercapacitor. It is shown that the three branch equivalent circuit model provides more accurate energy awareness. This equivalent circuit uses three capacitances and associated resistances to represent the supercapacitor's internal SOC (state-of-charge). However, the SOC cannot be determined from one observation of the terminal voltage, and must be tracked over time using inexact measurements. We present: 1) a Kalman filtering solution for tracking the SOC; 2) an on-line system identification procedure to efficiently estimate the equivalent circuit's parameters; and 3) experimental validation of both parameter estimation and SOC tracking for 5 F, 10 F, 50 F, and 350 F supercapacitors. Validation is done within the operating range of a solar powered application and the associated power variability due to energy harvesting. The proposed techniques are benchmarked against the simple capacitive model and prior parameter estimation techniques, and provide a 67% reduction in root-meansquare error for predicting usable buffered energy.

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In today's technology, even leading medical institutions diagnose their cardiac patients through ECG recordings obtained at healthcare organizations (HCO), which are costly to obtain and may miss significant clinically-relevant information. Existing long-term patient monitoring systems (e.g., Holter monitors) provide limited information about the evolution of deadly cardiac conditions and lack interactivity in case there is a sudden degradation in the patient's health condition. A standardized and scalable system does not currently exist to monitor an expanding set of patient vitals that a doctor can prescribe to monitor. The design of such a system will translate to significant healthcare savings as well as drastic improvements in diagnostic accuracy. In this chapter, we will propose a concept system for real-time remote cardiac health monitoring, based on available and emerging technologies today. We will analyze the details of such a system from acquisition to visualization of medical data.