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wearable-electronics
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A new 3D, microfluidic-oriented, multi-functional, and highly stretchable soft wearable sensor
, Article Scientific Reports ; Volume 12, Issue 1 , 2022 ; 20452322 (ISSN) ; Esmaeili Dokht, P ; Olyanasab, A ; Orouji, N ; Alipour, Z ; Sayad, M. H ; Rajabi, K ; Mazzolai, B ; Fardmanesh, M ; Sharif University of Technology
Nature Research
2022
Abstract
Increasing demand for wearable devices has resulted in the development of soft sensors; however, an excellent soft sensor for measuring stretch, twist, and pressure simultaneously has not been proposed yet. This paper presents a novel, fully 3D, microfluidic-oriented, gel-based, and highly stretchable resistive soft sensor. The proposed sensor is multi-functional and could be used to measure stretch, twist, and pressure, which is the potential of using a fully 3D structure in the sensor. Unlike previous methods, in which almost all of them used EGaIn as the conductive material, in this case, we used a low-cost, safe (biocompatible), and ubiquitous conductive gel instead. To show the...
Study and Fabrication of Flexible and Stretchable Electronic Circuits
, M.Sc. Thesis Sharif University of Technology ; Sarvari, Reza (Supervisor) ; Kolahdouz Esfahani, Mohammad Reza (Supervisor)
Abstract
In recent years, flexible electronics have started to attract a tremendeos amount of attention, partly on its remarkable growth in new technologies like IoT, wearable electronics, personal and health care devices. This emerging field offers many advantages compared to its traditional rigid counterparts, such as bendability, stretchablity, recyclability and biocompatibility. Inkjet printing is one of the most promising method for realizing flexible electronics due to its low-investment cost, fully additive nature, low material westage, relatively low temperature process and non-contact patterning.A printed circuit board (PCB), is undoubtedly a vital part of any electronics systems. So,...
Sleep Apnea Detection Using Wearable Devices
, M.Sc. Thesis Sharif University of Technology ; Fotowat Ahmady, Ali (Supervisor) ; Akbar, Fatemeh (Supervisor)
Abstract
Approximately 1.36 billion people worldwide suffer from sleep apnea, necessitating accurate diagnosis and treatment. Traditional sleep clinics rely on Polysomnography (PSG)-based monitoring devices. However, these devices are not only voluminous but also prohibitively expensive, requiring numerous sensors and wires that disrupt sleep and cause discomfort for patients. To address these challenges, we have developed a wireless wearable system. This system comprises three sensor blocks, each capable of capturing vital signs from the body, resulting in a total of eight signals. The first two sensor blocks capture signals from the body, which are subsequently modulated and transmitted to a user's...
A new scheme for the development of IMU-based activity recognition systems for telerehabilitation
, Article Medical Engineering and Physics ; Volume 108 , 2022 ; 13504533 (ISSN) ; Eslaminia, A. R ; Bakhshayesh, P. R ; Ejtehadi, M ; Alibiglou, L ; Behzadipour, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and...
ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices
, Article IEEE Transactions on Biomedical Circuits and Systems ; Volume 13, Issue 6 , 2021 , Pages 1483-1493 ; 19324545 (ISSN) ; Hashemi, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
This paper presents a novel ECG classification algorithm for inclusion as part of real-time cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption in real-time classification...