Wavelet-based EMG Sensing Interface for Pattern Recognition

Department of Electrical EngineeringAs interest in healthcare and smart devices has increased in recent years, the studies that are sensing and analyzing various bio signals, such as EMG, ECG, and EEG, have been growing. These studies and advances in smart devices have allowed human to increase access to their own physical information. With the physical information, human can diagnose himself or herself. These advances in technology will improve the quality of human life and provide solutions in various fields. The convergence of information and communication technologies has led to the fourth industrial revolution and the development of artificial intelligence, big data and the Internet of Things(IoT) by increasing computing power has led to various data analysis using machine learning. Various fields are moving toward the next level using machine learning, and this trend is also happening in the healthcare field. The era of self-diagnosis begins when medical knowledge, which had previously been entrusted to doctors is passed directly to consumers through big data and machine learning. Thanks to these developments, the healthcare interface, such as front-end integrated chip, is also working to leverage machine learning to deliver various solutions to consumers. Existing papers related to bio signals are focused on reducing power consumption, allowing long-term monitoring or reducing various noise. This paper provides an idea to extend the scope of data processes through machine learning while maintaining existing trends. Wavelet transform is implemented as a circuit to reduce computing power and eliminate specific frequency range including noise and motion artifact. The data from the chip is transmitted to external device (MATLAB) by wireless communication (Bluetooth) to be analyzed by machine learning. This paper present wavelet-based EMG sensing interface which includes front-end amplifier, wavelet filters, Analog to digital converter and Microcontroller. The main idea of the paper is front-end amplifiers which reduce a noise and motion artifact, wavelet filters that decompose the input signal for wavelet transform and machine learning for gesture recognition.ope

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

Optimization of the position of single-lead wireless sensor with low electrodes separation distance for ECG-derived respiration

A classical method for estimation of respiratory information from electrocardiogram (ECG), called ECG - derived respiration (EDR), is using flexible electrodes located at standard electrocardiography positions. This work introduces an alternative approach suitable for miniaturized sensors with low inter-electrode separation and electrodes fixed to the sensor encapsulation. Application of amplitude EDR algorithm on single-lead wireless sensor system with optimized electrode positions shows results comparable with standard robust systems. The modified method can be applied in daily physiological monitoring, in sleep studies or implemented in smart clothes when standard respiration techniques are not suitable

SMART FABRICS-WEARABLE TECHNOLOGY

Smart fabrics, generally regarded as smart Textiles are fabrics that have embedded electronics and interconnections woven into them, resulting in physical flexibility that is not achievable with other known electronic manufacturing techniques. Interconnections and components are intrinsic to the fabric therefore are not visible and less susceptible of getting tangled by surrounding objects. Smart fabrics can also more easily adapt to quick changes in the sensing and computational requirements of any specific application, this feature being useful for power management and context awareness. For electronic systems to be part of our day-to-day outfits such electronic devices need to conform to requirements as regards wear-ability, this is the vision of wearable technology. Wearable systems are characterized by their capability to automatically identify the activity and the behavioral status of their wearer as well as of the situation around them, and to use this information to adjust the systems' configuration and functionality. This write-up focused on recent developments in the field of Smart Fabrics and pays particular attention to the materials and their manufacturing techniques

Electro-textile UHF-RFID compression sensor for health-caring applications

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Electro-textile Ultra High Frequency (UHF, 865–868 MHz) Radio Frequency Identification (RFID) devices have great potential to be explored as sensors due to the features of fabric materials. In this work, an electro-textile UHF-RFID compression sensor base on T-match structure with a corresponding interface are developed and evaluated for two application scenarios. For accurate textile UHF-RFID antenna design and maximize the read range, the impedance of the electro-textile based on snap buttons is modelled and characterized an a measured read range of 5.22m is experimentally obtained. If the distance of the RFID reader and RFID sensor remain constant at 1 m. The experimental results show that RSSI range change from -42 dBm to -58 dBm as a quadratic function in terms of the knee angle bending and from -45 dBm to -40 dBm during expiration and inspiration phase when the sensor is located on the chest, which validated the usefulness of the proposed sensor.This work was supported in part by Spanish Government-MINECO under Project TEC2016-79465-R and China Scholarship Council under Grant No.201908440233.Peer ReviewedPostprint (author's final draft

Smart Home Technologies for Cognitive Assessment in Healthcare

With the term 'smart home' developers usually refer to a house, or more generally a residential environment, where a set of integrated sensors, devices and technologies provides the occupants with innovative functionalities and utilities which improve both the living comfort and the resource management of the building