A Self-tracked High-dielectric Wireless Power Transfer System for Neural Implants

This paper introduces a novel, efficient and long-range ( 0.5λ) wireless power transfer system for implantable neural devices. The operating principle of this system is based on the high-dielectric coupling, which occurs between an external lossless high-dielectric metamaterial (permittivity, ε r =100, loss tangent, tanδ = 0.0001) and lossy dielectric such as rat (ε r =54.1, conductivity, σ = 1.5 S/m). As magnetic field coupling occurs between two dielectric resonators, therefore, the rat (lossy dielectric) itself acts as a self-tracking energy source. The Ansoft HFSS simulation software was used to verify the concept. Initially, the rat was modelled as a phantom box and the resonant frequency was found to be 1.5 GHz. Then, for matching this intrinsic mode of the rat model, the external high-dielectric metamaterial designed accordingly to realize a highly efficient (η = 1×10 -3 ) and self-tracked wireless power system for neural implants

Flexible Wirelessly Powered Implantable Device

Brain implantable devices have various limitations in terms of size, power, biocompatibility and mechanical properties that need to be addressed. This paper presents a neural implant that is powered wirelessly using a flexible biocompatible antenna. This delivers power to an LED at the end of the shaft to provide a highly efficient demonstration. The proposed design in this study combines mechanical properties and practicality given the numerous constraints of this implant typology. We have applied a modular structure approach to the design of this device considering three modules of antenna, conditioner circuit and shank. The implant was fabricated using a flexible substrate of Polyimide and encapsulated by PDMS for chronic implantation. In addition, finite element method COMSOL Multiphysics simulation of mechanical forces acting on the implant and shank was carried out to validate a viable shank conformation-encapsulation combination that will safely work under operational stress with a satisfactory margin of safety

Visual Hand Tracking on Depth Image Using 2-D Matched Filter

Hand detection has been the central attention of human-machine interaction in recent researches. In order to track hand accurately, traditional methods mostly involve using machine learning and other available libraries, which requires a lot of computational resource on data collection and processing. This paper presents a method of hand detection and tracking using depth image which can be conveniently and manageably applied in practice without the huge data analysis. This method is based on the two-dimensional matched filter in image processing to precisely locate the hand position through several underlying codes, cooperated with a Delta robot. Compared with other approaches, this method is comprehensible and time-saving, especially for single specific gesture detection and tracking. Additionally, it is friendly-programmed and can be used on variable platforms such as MATLAB and Python. The experiments show that this method can do fast hand tracking and improve accuracy by selecting the proper hand template and can be directly used in the applications of human-machine interaction. In order to evaluate the performance of gesture tracking, a recorded video on depth image model is used to test theoretical design, and a delta parallel robot is used to follow the moving hand by the proposed algorithm, which demonstrates the feasibility in practice

Wearable Wristworn Gesture Recognition Using Echo State Network

This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%

Photovoltaic power harvesting technologies in biomedical implantable devices considering the optimal location

here are still many challenges in effectively harvesting and generating power for implantable medical devices. Most of today's research focuses on finding ways to harvest energy from the human body to avoid the use of batteries, which require surgical replacement. For example, current energy harvesters rely on piezoelectricity, thermoelectricity and solar electricity to drive the implantable device. However, the majority of these energy harvesting techniques suffer from a variety of limitations such as low power output, large size or poor efficiency. Due to their high efficiency, we focus our attention on solar photovoltaic cells. We demonstrate the tissue absorption losses severely influence their performance. We predict the performance of these cells using simulation through the verified experimental data. Our results show that our model can obtain 17.20% efficiency and 0.675 V open-circuit voltage in one sun condition. In addition, our device can also harvest up to 15 mW/ cm2 in dermis and 11.84 mW/ cm2 in hypodermis by using 100 mW/ cm2 light source at 800 nm and 850 nm, respectively. We propose implanting our device in hypodermis to obtain a stable power output

Electronic contact lens: a platform for wireless health monitoring applications

Electronic contact lenses can be used for non‐invasively monitoring vital human signs and medical parameters. However, maintaining a secure communications connection and a self‐sustainable power source are still looming challenges. This paper demonstrates a proof of concept electronic contact lens that includes a spiral antenna with its wireless circuit unit for data telemetry, a rectifier circuit for power conditioning and a micro light emitting diode (µLED) as a load. The spiral antenna with its rectifying circuit was designed considering operation in the Industrial, Scientific and Medical (ISM) band of 2.4 GHz. The spiral coil with an inner diameter of 10 mm, an outer diameter of 12 mm and a wire width of 0.2 mm was fabricated on a donut‐shaped flexible polyimide substarte. For biocompatibility purposes, Polyimide was used as the contact lens substrate and polydimethylsiloxane (PDMS) was used for encapsulation. A 3D‐printed eye model was developed for accurately shaping the curvature of the PDMS‐encapsulated contact lens. The reflection coefficient (S11) of the fabricated antenna was tested in different conditions and on an eye model to mimic the liquid condition of the human eye. In a wide range of conditions, a minimum of ‐20 dB reflection coefficient (S11) was obtained. The maximum antenna gain was ‐28 dBi and the contact lens satisfied the electromagnetic exposure safety limit of 1.6 W/kg for 1 g of tissue mass. We also determined the wavelength dependence of the electronic contact lens on different lens thicknesses. Our results showed that the lens is transmissive in the visible part of the spectrum

Biointegrated and wirelessly powered implantable brain devices: a review

Implantable neural interfacing devices have added significantly to neural engineering by introducing the low-frequency oscillations of small populations of neurons known as local field potential as well as high-frequency action potentials of individual neurons. Regardless of the astounding progression as of late, conventional neural modulating system is still incapable to achieve the desired chronic in vivo implantation. The real constraint emerges from mechanical and physical diffierences between implants and brain tissue that initiates an inflammatory reaction and glial scar formation that reduces the recording and stimulation quality. Furthermore, traditional strategies consisting of rigid and tethered neural devices cause substantial tissue damage and impede the natural behaviour of an animal, thus hindering chronic in vivo measurements. Therefore, enabling fully implantable neural devices, requires biocompatibility, wireless power/data capability, biointegration using thin and flexible electronics, and chronic recording properties. This paper reviews biocompatibility and design approaches for developing biointegrated and wirelessly powered implantable neural devices in animals aimed at long-term neural interfacing and outlines current challenges toward developing the next generation of implantable neural devices

Design, test and optimization of inductive coupled coils for implantable biomedical devices

Biomedical implant devices are fast becoming a growing part of the healthcare industry. Providing power to these devices in such a confined area is a critical challenge. Consequently, resonancebased wireless power delivery provides a harmless yet effective way for powering these implantable biomedical devices. This technique relies on transferring power via the inductive coupling technique. In this regard, optimizing the quality factor and matched resonant frequency is required to achieve high efficiency. However, the efficiency depends on the space available for the coil and the separation distance between the two coils. In our case, the minimum separation distance between the two coils needs to be at least 2 cm. Therefore, we demonstrate the design, simulation and experimental procedure of an optimized wireless power delivery system for bio-implantable applications with various considerations for size limitations. Our design delivers 68 mW output power to a 50-Ω load with an efficiency of 67% in vitro test and 74.8% in the FEM simulation