Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

The large interest in utilising fibre Bragg grating (FBG) strain sensors for minimally invasive surgery (MIS) applications to replace conventional electrical tactile sensors has grown in the past few years. FBG strain sensors offer the advantages of optical fibre sensors, such as high sensitivity, immunity to electromagnetic noise, electrical passivity and chemical inertness, but are not limited by phase discontinuity or intensity fluctuations. FBG sensors feature a wavelength-encoding sensing signal that enables distributed sensing that utilises fewer connections. In addition, their flexibility and lightness allow easy insertion into needles and catheters, thus enabling localised measurements inside tissues and blood. Two types of FBG tactile sensors have been emphasised in the literature: single-point and array FBG tactile sensors. This paper describes the current design, development and research of the optical fibre tactile techniques that are based on FBGs to enhance the performance of MIS procedures in general. Providing MIS or microsurgery surgeons with accurate and precise measurements and control of the contact forces during tissues manipulation will benefit both surgeons and patients.

The staggering innovations and emergence of numerous deep learning (DL) applications have forced researchers to reconsider hardware architecture to accommodate fast and efficient application-specific computations. Applications, such as object detection, image recognition, speech translation, as well as music synthesis and image generation, can be performed with high accuracy at the expense of substantial computational resources using DL. Furthermore, the desire to adopt Industry 4.0 and smart technologies within the Internet of Things infrastructure has initiated several studies to enable on-chip DL capabilities for resource-constrained devices. Specialized DL processors reduce dependence on cloud servers, improve privacy, lessen latency, and mitigate bandwidth congestion. As we reach the limits of shrinking transistors, researchers are exploring various application-specific hardware architectures to meet the performance and efficiency requirements for DL tasks. Over the past few years, several software optimizations and hardware innovations have been proposed to efficiently perform these computations. In this article, we review several DL accelerators, as well as technologies with emerging devices, to highlight their architectural features in application-specific integrated circuit (IC) and field-programmable gate array (FPGA) platforms. Finally, the design considerations for DL hardware in portable applications have been discussed, along with some deductions about the future trends and potential research directions to innovate DL accelerator architectures further. By compiling this review, we expect to help aspiring researchers widen their knowledge in custom hardware architectures for DL.

An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.

Surface plasmon resonance (SPR) is a novel optical sensing technique with a unique ability to monitor molecular binding in real-time for biological and chemical sensor applications. Interferometry is an excellent tool for accurate measurement of SPR changes, the measurement and comparison is made for the sensitivity, dynamic range and resolution of the different analytes using interferometry techniques. SPR interferometry can also employ phase detection in addition to the amplitude of the reflected light wave, and the phase changes more rapidly compared with other approaches, i.e., intensity, angle and wavelength. Therefore, the SPR phase interferometer offers the advantages of spatial phase resolution and high sensitivity. This work discusses the advancements in interferometric SPR methods to measure the phase shifts due to refractive index changes. The main application areas of SPR sensors are demonstrated, i.e., the Fabry-Perot interferometer, Michelson interferometer and Mach-Zehnder interferometer, with different configurations. The three interferometers are discussed in detail, and solutions are suggested to enhance the performance parameters that will aid in future biological and chemical sensors.