The development of assistive technologies by means of human machine interface is turning out to be one of the most attractive areas of today's research. Innovation in power consumption, signal processing and wireless communication has been the focus for the development of such systems. However, these devices have not reached the mass market yet. The development of a versatile system which can as well play a major role in clinical and rehabilitative control applications could revolutionize this domain. The system is based on TI's embedded processor, wireless communication solutions and highly-customized analog front ends. As a proof of concept, our technology integrates the MSP430 architecture, INA333-HT instrumentation amplifier, INA129/128 as analog front end and further LM324 for analog signal processing in an effective manner. This system measures EOG, a biopotential signal, which is being pre-processed, amplified, digitized and after signal processing results are wirelessly transmitted thereby establishing human-machine communication. This project cleverly combines hardware and software disciplines resulting into one successful system which could hold ground for the development of intelligent assistive technology. The proposed technology holds clinical and rehabilitative applications as well.
The primary source of vision loss in patients is mainly due to Diabetic retinopathy (DR), caused due to diabetes mellitus. It has become a significant reason for visual impairment among people within 25-74 years of age. If timely medical attention is provided to DR patients, over 90% of people can be saved from vision loss. It's crucial for the early diagnosis of the disease and provide the necessary treatment. The symptoms are more prevalent in type 2 diabetics than associated with type 1 diabetics. Unlike computer-aided diagnosis systems, the traditional procedures of DR detection using fundus photography are both time and cost-consuming. Among the numerous methods for screening and detecting DR, Convolutional Neural Networks are considered extensively in Deep Learning (DL) methods. This review article illustrates the different datasets, pre-processing steps, and DL techniques used in the fundus images for efficient DR detection at an early stage. The main motive of this review article is to provide the research community with an insight into the various pre-processing steps, Public datasets, DL models in DR detection, and some future research directions in this field.
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