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Implantable medical devices such as pacemakers, implantable cardioverter defibrillators, deep brain stimulators, retinal and cochlear implants are gaining significant attraction and growth due to their capability to monitor the health condition in real time, diagnose a particular disease, or provide treatment for a particular disease. In order to charge these devices, wireless power transfer technology is considered as a powerful means. This eliminates the need for extra surgery to replace the battery. In this chapter, some of the major implanted medical devices are reviewed. Then, various wireless power transfer configurations are reviewed briefly for charging such devices. The chapter continues with reviewing wireless power transfer configurations based on the multi-layer printed or non-printed planar spiral coils. At the end, some of the recent works related to using multi-layer planar spiral coils for safe and efficient powering of IMDs will be discussed.

Replacing the batteries of wearable medical devices is an inconvenience to the user. Wireless power transfer (WPT) could be a solution for this problem. However, the structure should be compact to satisfy the wearable device requirements and the power efficiency should be as high as possible for prolonging the user experience. Besides, as algorithms embedded in these devices become more complex, the device power consumption also increases. Fulfilling these specifications in a compact structure with better efficiency is the aim of this research paper. This paper proposes a power processing module for WPT applications. This module is embedded within the WPT path to convert receiving end ac power to dc power, and to simultaneously provide multiple dc–dc power conversion for wearable medical devices. The ac–dc circuit is specifically designed using a compact topology to convert wireless ac power to stable dc power. The dc power is further regulated by different mode controls either to perform battery charging or to provide independent voltage supplies to dc loads. The integrated circuit occupies an area of 1.9 × 2.3 mm when implemented on a 0.25 μm high voltage CMOS process.

Google's Perspective API was introduced to help detect and classify toxic comments in online platforms. Adversarial machine learning attacks can decrease the effectiveness of Perspective in identifying toxic comments. We have shown in our previous study that by applying a semantic-based attack to a surrogate model trained with just 10,000 queries could produce adversarial examples which evade Perspective 25% of the time. In this paper, we propose two new approaches to generate adversarial text to evade Google's Perspective, one based on acoustic similarity and the other based on visual similarity. We tested the success rate of obfuscation in Google Perspective using the adversarial texts generated through the proposed approaches and showed that Google Perspective misclassified the generated texts 33% and 72.5% of the time for the visual-based and acoustic-based approaches, respectively. The study aims to broaden the understanding of adversarial text generation and to improve the robustness for online toxic comment detection for a safe online community.

Immersion into a virtual environment (VE) often results in adverse symptoms including nausea, dizziness, and disorientation. These symptoms are an indicator of cybersickness, which is a condition similar to motion sickness experienced in VEs. In this paper, we hypothesized that administered cognitive distraction can accelerate the rate of habituation to a VE. This acceleration, therefore, can lower severity of cybersickness in fewer amount of immersions. To evaluate the impact of cognitive distraction on reducing the effects of cybersickness, we designed a VE and carried out a human subject study with control and experimental groups created through stratified random sampling. Subjects were immersed in our VE on four separate sessions, and our experimental group received cognitive distraction throughout the immersions. Cybersickness was measured using the Simulator Sickness Questionnaire (SSQ) and Presence Questionnaire (PQ). Upon comparing the average SSQ subgroups nausea, oculomotor, and disorientation scores reported by participants for each immersion session, we observed that our experimental group exhibited decrease in cybersickness to a greater extent than that of our control group. We completed t-tests for each of these comparisons, to find that these results are statistically insignificant. We plan to continue with this work by incorporating up to 30 total participants to clarify these findings.

Wearable and implantable medical devices ranging from wellness monitors to deep brain stimulators are becoming increasingly vital and ubiquitous. Such devices continuously take measurements, which consumes battery. The power consumption is proportional to the amount of information collected and with the frequency of data collection. High power consumption leads to rapid discharging of battery limiting the usage of these devices. These signals are often transmitted wirelessly for analysis, as well as to keep track of the user's record, which also significantly increases power consumption. In this project, we evaluated adaptively modifying the rate of data collection on these devices, in other words, the sampling rate, for electrophysiological monitoring as the relevance of the signal changes in time. We carried out these tests using a proof-of-concept prototype developed for this project. In particular, we reviewed the effects of such adaptive sampling on intracellular potentials, and motor unit action potentials (MUAPs). By doing so, we were able to reduce the amount of data by 48.95% and power by 41.50% for the MUAPs with an 8% sample loss within MUAPs, and by 69.20% and 57.14% for intracellular potentials with a 6.75% sample loss.

Gait abnormalities are one of the distinguishing symptoms of patients with Parkinson’s disease (PD) that contribute to fall risk. Our study compares the gait parameters of people with PD when they walk through a predefined course under different haptic speed cue conditions (1) without assistance, (2) pushing a conventional rolling walker, and (3) holding onto a self-navigating motorized walker under different speed cues. Six people with PD were recruited at the New York Institute of Technology College of Osteopathic Medicine to participate in this study. Spatial posture and gait data of the test subjects were collected via a VICON motion capture system. We developed a framework to process and extract gait features and applied statistical analysis on these features to examine the significance of the findings. The results showed that the motorized walker providing a robust haptic cue significantly improved gait symmetry of PD subjects. Specifically, the asymmetry index of the gait cycle time was reduced from 6.7% when walking without assistance to 0.56% and below when using a walker. Furthermore, the double support time of a gait cycle was reduced by 4.88% compared to walking without assistance.

As the sizes of implantable medical devices such as implantable cardioverter defibrillators, deep brain stimulators, and retinal implants shrink; powering these devices become more challenging. Wireless power transfer (WPT) enables the use of rechargeable and thus smaller batteries, however it also requires additional space for the receiving coil. Depending on the location of the implant in the body, device area or volume can be more constrained. In this paper, we investigate multi-layer planar spiral coils via simulations. We study the impact of number of turns on the quality factor for multi-layer coils including coils with different number of turns in each layer (hybrid coils) and we offer recommendations on the approximate number of turns for having high Q values.

Transportation Cyber-Physical Systems (TCPS) strive to achieve the seamless interoperability of a rich set of sensors embedded in vehicles, roadside units, and other infrastructure with computing platforms ranging from smartphones to cloud servers, through a variety of communication mechanisms. A successful TCPS will provide smart and scalable solutions to some of the major problems urban societies facing today including high fatalities in road crashes, time and emission costs of traffic congestion, and efficient allocation of parking spaces. However, the practicality of such a TCPS is challenged by (1) stakeholders with different and often conflicting security and privacy requirements, (2) the demands of real-time data intensive computing and communication, and (3) a high level of heterogeneity in the types of technologies deployed. Transportation Cloud Computing, which is the integration of Cloud Computing with TCPS, is a promising solution to the challenges listed above for a scalable implementation. This chapter presents the security, trust, and privacy issues posed by integrating cloud computing with TCPS as in the first challenge above. We will survey the state of the art with respect to countermeasures which are capable ofproviding improved security and privacy for cloud computing in TCPS. More specifically, we will first discuss the unique challenges and the current state of the art in TCPS as well as the integration of cloud computing techniques into a TCPS application scenario. Next, we will present a comprehensive literature review on attack surface and strategies for cloud computing in TCPS. To address these attacks, we will describe various techniques to enhance security, trust, and privacy to help better safeguard cloud computing paradigms for TCPS.

Objective High channel count electrode arrays allow for the monitoring of large-scale neural activity at high spatial resolution. Implantable arrays featuring many recording sites require compact, high bandwidth front-end electronics. In the present study, we investigated the use of a small, light weight, and low cost digital current-sensing integrated circuit for acquiring cortical surface signals from a 61-channel micro-electrocorticographic (μECoG) array. Approach We recorded both acute and chronic μECoG signal from rat auditory cortex using our novel digital current-sensing headstage. For direct comparison, separate recordings were made in the same anesthetized preparations using an analog voltage headstage. A model of electrode impedance explained the transformation between current- and voltage-sensed signals, and was used to reconstruct cortical potential. We evaluated the digital headstage using several metrics of the baseline and response signals. Main results The digital current headstage recorded neural signal with similar spatiotemporal statistics and auditory frequency tuning compared to the voltage signal. The signal-to-noise ratio of auditory evoked responses (AERs) was significantly stronger in the current signal. Stimulus decoding based on true and reconstructed voltage signals were not significantly different. Recordings from an implanted system showed AERs that were detectable and decodable for 52 d. The reconstruction filter mitigated the thermal current noise of the electrode impedance and enhanced overall SNR. Significance We developed and validated a novel approach to headstage acquisition that used current-input circuits to independently digitize 61 channels of μECoG measurements of the cortical field. These low-cost circuits, intended to measure photo-currents in digital imaging, not only provided a signal representing the local cortical field with virtually the same sensitivity and specificity as a traditional voltage headstage but also resulted in a small, light headstage that can easily be scaled to record from hundreds of channels.