An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

Stable force-myographic control of a prosthetic hand using incremental learning.

Force myography has been proposed as an appealing alternative to electromyography for control of upper limb prosthesis. A limitation of this technique is the non-stationary nature of the recorded force data. Force patterns vary under influence of various factors such as change in orientation and position of the prosthesis. We hereby propose an incremental learning method to overcome this limitation. We use an online sequential extreme learning machine where occasional updates allow continual adaptation to signal changes. The applicability and effectiveness of this approach is demonstrated for predicting the hand status from forearm muscle forces at various arm positions. The results show that incremental updates are indeed effective to maintain a stable level of performance, achieving an average classification accuracy of 98.75% for two subjects.

An ingestible wireless capsule for treatment of obesity.

Intragastric balloon has become a popular method for treatment of obesity due to its less-invasive and non-pharmaceutical procedure. In this method, a gas (or liquid)-filled balloon is inserted into the stomach using endoscopy or surgery. The balloon stays in and partially fills the stomach for a desired period of time to induce the feeling of satiety in the patient. At the end of the treatment period, the balloon is removed from the body using endoscopy or surgery. Although proven effective in treatment of obesity, this method suffers from several drawbacks. Requiring an endoscopic procedure or surgery to insert and exert the balloon from the stomach is the most important disadvantage of this method. These procedures are usually costly and may cause the patient to feel uncomfortable. Here, we propose a non-invasive method to overcome these drawbacks. In this method, an intragastric balloon is introduced into the body using an ingestible capsule. The volume of the capsule can be adjusted wirelessly after being swallowed by the patient. Using this method, a non-invasive and patient-specific treatment is possible.

Energy sources and their development for application in medical devices.

Electronic medical devices have become an indispensable part of modern healthcare. Currently, a wide variety of electronic medical devices are being used to monitor physiological parameters of the body, perform therapy and supplement or even entirely replace complex biological functions. Cardiac pacemakers, cardioverter-defibrillators and cochlear implants are a few examples of such medical devices. Proper functionality of these devices relies heavily on the continuous supply of a sufficient amount of electricity to them. In this sense, a reliable, safe and convenient method for the provision of energy is very crucial. Various approaches have been developed to fulfil the divergent and challenging energy requirements of medical devices. In this article, we present a brief overview of the energy requirements of medical devices and review the existing and emerging energy sources for application in these devices, particularly wearable and implantable devices.