On the security of consumer wearable devices in the Internet of Things

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands

Feasibility study of detecting surface electromyograms in severely obese patients

The aims of this study were to examine if surface EMG signals can be detected from the quadriceps femoris muscle of severely obese patients and to investigate if differences exist in quadriceps force and myoelectric manifestations of fatigue between obese patients and lean controls. Fourteen severely obese patients (body mass index, BMI, mean±SD: 44.9±6.3kg/m(2)) and fourteen healthy controls (BMI: 23.7±2.5kg/m(2)) were studied. The vastus medialis and lateralis of the dominant thigh were concurrently investigated during voluntary isometric contractions (10-s long at submaximal and maximal intensities and intermittent submaximal contractions until exhaustion) and sustained (120-s long) electrically elicited contractions. We found that the detection of surface EMG signals from the quadriceps is feasible also in severely obese subjects presenting increased thickness of the subcutaneous fat tissue. In addition, we confirmed and extended previous findings showing that the volume conductor properties determine the amplitude and spectral features of the detected surface EMG signals: the lower the subcutaneous tissue thickness, the higher the amplitude and mean frequency estimates. Further, we found no differences in the mechanical and myoelectric manifestations of fatigue during intermittent voluntary and sustained electrically elicited contractions between obese patients and lean controls

A Survey of Machine Learning Algorithms and Their Application in Information Security

In this survey, we touch on the breadth of applications of machine learning to problems in information security. A wide variety of machine learning techniques are introduced, and a sample of the applications of each to security-related problems is briefly discussed

Interpreting joint moments and powers in gait

Gait analysis is becoming an increasingly important tool to provide a quantitative description of a patient's gait deviations. It is not only used to diagnose walking disorders but also for treatment selection and evaluation. While spatiotemporal, kinematic, and EMG parameters are commonly used to describe movement and muscle activity, kinetic measures are less often evaluated, even though they give insight into the moments and powers that drive human walking. As such, kinetic parameters are able to connect abnormal movement to underlying muscle malfunction and bony malalignment. This chapter focuses on the role of joint moments and powers of the lower extremities in clinical gait analysis. After a brief introduction of normal kinetic patterns, the clinical interpretation of abnormal joint moments and powers is described. Next, typical deviations in lower limb kinetics are illustrated for several patient populations, including stroke, cerebral palsy, Duchenne muscular dystrophy, anterior cruciate ligament (ACL) injury, and osteoarthritis (OA), and for patients walking with prostheses or orthotics. This section also illustrates the clinical usefulness of specific kinetic parameters in these patient populations, including their sensitivity to treatment and ability to predict treatment outcome. The chapter illustrates that the role of kinetics within clinical gait analysis deserves more attention, and potential applications should be further pursued