5,463 research outputs found
e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices
To facilitate monitoring and management, modern Implantable Medical Devices
(IMDs) are often equipped with wireless capabilities, which raise the risk of
malicious access to IMDs. Although schemes are proposed to secure the IMD
access, some issues are still open. First, pre-sharing a long-term key between
a patient's IMD and a doctor's programmer is vulnerable since once the doctor's
programmer is compromised, all of her patients suffer; establishing a temporary
key by leveraging proximity gets rid of pre-shared keys, but as the approach
lacks real authentication, it can be exploited by nearby adversaries or through
man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one
of the most important design goals, few schemes explore to lower the
communication and computation overhead all at once. Finally, how to safely
record the commands issued by doctors for the purpose of forensics, which can
be the last measure to protect the patients' rights, is commonly omitted in the
existing literature. Motivated by these important yet open problems, we propose
an innovative scheme e-SAFE, which significantly improves security and safety,
reduces the communication overhead and enables IMD-access forensics. We present
a novel lightweight compressive sensing based encryption algorithm to encrypt
and compress the IMD data simultaneously, reducing the data transmission
overhead by over 50% while ensuring high data confidentiality and usability.
Furthermore, we provide a suite of protocols regarding device pairing,
dual-factor authentication, and accountability-enabled access. The security
analysis and performance evaluation show the validity and efficiency of the
proposed scheme
Evaluation of a Behind-the-Ear ECG Device for Smartphone based Integrated Multiple Smart Sensor System in Health Applications
In this paper, we present a wireless Multiple Smart Sensor System (MSSS) in conjunction with a smartphone to enable an unobtrusive monitoring of electrocardiogram (ear-lead ECG) integrated with multiple sensor system which includes core body temperature and blood oxygen saturation (SpO2) for ambulatory patients. The proposed behind-the-ear device makes the system desirable to measure ECG data: technically less complex, physically attached to non-hair regions, hence more suitable for long term use, and user friendly as no need to undress the top garment. The proposed smart sensor device is similar to the hearing aid device and is wirelessly connected to a smartphone for physiological data transmission and displaying. This device not only gives access to the core temperature and ECG from the ear, but also the device can be controlled (removed and reapplied) by the patient at any time, thus increasing the usability of personal healthcare applications. A number of combination ECG electrodes, which are based on the area of the electrode and dry/non-dry nature of the surface of the electrodes are tested at various locations near behind the ear. The best ECG electrode is then chosen based on the Signal-to-Noise Ratio (SNR) of the measured ECG signals. These electrodes showed acceptable SNR ratio of ~20 db, which is comparable with existing tradition ECG electrodes. The developed ECG electrode systems is then integrated with commercially available PPG sensor (Amperor pulse oximeter) and core body temperature sensor (MLX90614) using a specialized micro controller (Arduino UNO) and the results monitored using a newly developed smartphone (android) application
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management
The increase of mental illness cases around the world can be described as an urgent
and serious global health threat. Around 500 million people suffer from mental disorders, among
which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological
paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess,
and care for patients early. This paper comprehensively survey works done at the intersection
between IoT and mental health disorders. We evaluate multiple computational platforms, methods
and devices, as well as study results and potential open issues for the effective use of IoT systems
in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT
solutions for mental health care, which can be relevant given the potential impairments in some
mental health patients such as data acquisition issues, lack of self-organization of devices and service
level agreement, and security, privacy and consent issues, among others. We aim at opening the
conversation for future research in this rather emerging area by outlining possible new paths based
on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program
PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine"
RTI2018-101674-B-I0
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
Wireless body sensor networks for health-monitoring applications
This is an author-created, un-copyedited version of an article accepted for publication in
Physiological Measurement. The publisher is
not responsible for any errors or omissions in this version of the manuscript or any version
derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01
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