1,849 research outputs found
Electrical vestibular stimulation in humans. A narrative review
Background: In patients with bilateral vestibulopathy, the
regular treatment options, such as medication, surgery, and/
or vestibular rehabilitation, do not always suffice. Therefore,
the focus in this field of vestibular research shifted to electri-
cal vestibular stimulation (EVS) and the development of a
system capable of artificially restoring the vestibular func-
tion. Key Message: Currently, three approaches are being
investigated: vestibular co-stimulation with a cochlear im-
plant (CI), EVS with a vestibular implant (VI), and galvanic
vestibular stimulation (GVS). All three applications show
promising results but due to conceptual differences and the
experimental state, a consensus on which application is the
most ideal for which type of patient is still missing. Summa-
ry: Vestibular co-stimulation with a CI is based on “spread of
excitation,” which is a phenomenon that occurs when the
currents from the CI spread to the surrounding structures
and stimulate them. It has been shown that CI activation can
indeed result in stimulation of the vestibular structures.
Therefore, the question was raised whether vestibular co-
stimulation can be functionally used in patients with bilat-
eral vestibulopathy. A more direct vestibular stimulation
method can be accomplished by implantation and activa-
tion of a VI. The concept of the VI is based on the technology
and principles of the CI. Different VI prototypes are currently
being evaluated regarding feasibility and functionality. So
far, all of them were capable of activating different types of
vestibular reflexes. A third stimulation method is GVS, which
requires the use of surface electrodes instead of an implant-
ed electrode array. However, as the currents are sent through
the skull from one mastoid to the other, GVS is rather unspe-
cific. It should be mentioned though, that the reported
spread of excitation in both CI and VI use also seems to in-
duce a more unspecific stimulation. Although all three ap-
plications of EVS were shown to be effective, it has yet to be
defined which option is more desirable based on applicabil-
ity and efficiency. It is possible and even likely that there is a
place for all three approaches, given the diversity of the pa-
tient population who serves to gain from such technologies
Comparison of low-power wireless communication technologies for wearable health-monitoring applications
Health monitoring technologies such as Body Area Network (BAN) systems has gathered a lot of attention during the past few years. Largely encouraged by the rapid increase in the cost of healthcare services and driven by the latest technological advances in Micro-Electro-Mechanical Systems (MEMS) and wireless communications. BAN technology comprises of a network of body worn or implanted sensors that continuously capture and measure the vital parameters such as heart rate, blood pressure, glucose levels and movement. The collected data must be transferred to a local base station in order to be further processed. Thus, wireless connectivity plays a vital role in such systems. However, wireless connectivity comes at a cost of increased power usage, mainly due to the high energy consumption during data transmission. Unfortunately, battery-operated devices are unable to operate for ultra-long duration of time and are expected to be recharged or replaced once they run out of energy. This is not a simple task especially in the case of implanted devices such as pacemakers. Therefore, prolonging the network lifetime in BAN systems is one of the greatest challenges. In order to achieve this goal, BAN systems take advantage of low-power in-body and on-body/off-body wireless communication technologies. This paper compares some of the existing and emerging low-power communication protocols that can potentially be employed to support the rapid development and deployment of BAN systems
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SkinnySensor: Enabling Battery-Less Wearable Sensors Via Intrabody Power Transfer
Tremendousadvancement inultra-low powerelectronics and radiocommunica tionshas significantly contributed towards the fabrication of miniaturized biomedical sensors capable of capturing physiological data and transmitting them wirelessly. However, most of the wearable sensors require a battery for their operation. The battery serves as one of the critical bottlenecks to the development of novel wearable applications, as the limitations offered by batteries are affecting the development of new form-factors and longevity of wearable devices. In this work, we introduce a novel concept, namely Intra-Body Power Transfer (IBPT), to alleviate the limitations and problems associated with batteries, and enable wireless, batteryless wearable devices. The innovation of IBPT is to utilize the human body as the medium to transfer power to passive wearable devices, as opposed to employingon-boardbatteries for each individual device. The proposed platform eliminates the on-board rigid battery for ultra-low power and ultra-miniaturized sensors such that their form-factor can be flexible, ergonomically designed to be placed on small body parts. The platform also eliminates the need for battery maintenance (e.g., recharging or replacement) for multiple wearable devices other than the central power source. The performance of the developed system is tested and evaluated in comparison to traditional Radio Frequency based solutions that can be harmful to human interaction. The system developed is capable of harvesting on average 217µW at 0.43V and provides an average sleep/high impedance mode voltage of 4.5V
New Contact Sensorization Smart System for IoT e-Health Applications Based on IBC IEEE 802.15.6 Communications
[EN] This paper proposes and demonstrates the capabilities of a new sensorization system that monitors skin contact between two persons. Based on the intrabody communication standard (802.15.6), the new system allows for interbody communication, through the transmission of messages between di erent persons through the skin when they are touching. The system not only detects if there has been contact between two persons but, as a novelty, is also able to identify the elements that have been in contact. This sensor will be applied to analyze and monitor good follow-up of hand hygiene practice in health care, following the ¿World Health Organization Guidelines on Hand Hygiene in Health Care¿. This guide proposes specific recommendations to improve hygiene practices and reduce the transmission of pathogenic microorganisms between patients and health-care workers (HCW). The transmission of nosocomial infections due to improper hand hygiene could be reduced with the aid of a monitoring system that would prevent HCWs from violating the protocol. The cutting-edge sensor proposed in this paper is a crucial innovation for the development of this automated hand hygiene monitoring system (AHHMS).This research was funded by the Spanish Ministerio de Economia y Competitividad, grant number DPI2016-80303-C2-1-P.Hernández, D.; Ors Carot, R.; Capella Hernández, JV.; Bonastre Pina, AM.; Campelo Rivadulla, JC. (2020). New Contact Sensorization Smart System for IoT e-Health Applications Based on IBC IEEE 802.15.6 Communications. Sensors. 20(24):1-17. https://doi.org/10.3390/s20247097S117202
User Recognition Based on Human Body Impulse Response: A Feasibility Study
Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR
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