Many people around the world suffer from losing the ability to talk and hear with different levels of disabilities, caused by either a car or a work accident or some diseases. After losing communication, these people cannot do normal functions of normal life. Along with the aforementioned disabilities, those people may also have psychological effects. This paper introduces a technique to realize multiple sign language translation using a sensors-based glove and an Android smartphone for speech impaired people to communicate normally with people. The design of the hand talking system (HTS) was implemented with a minimum possible number of sensors and a capable sewing controller (Lilypad). The proposed HTS includes flex sensors, Arduino, smartphone, and accelerometer. The HTS uses an Android application programmed to store multi-language in the SQLite database and enables the user to interact with the system. The system provides talking with a letter formed words, or using the most frequently used words in daily communication by hand gesture. The HTS has achieved high accuracy obtained for American Sign Language and Arabic Sing Language which are about 98.26% and 99.33% respectively with an average accuracy of 98.795 for both Sign Languages.
The electroretinogram (ERG) is an electrophysiological recording method that measures the retinal electrical potential. The electrical reaction is quantified by electrical interaction of the indicator electrode with the cornea or at various levels inside the retina. However, such ERG systems suffer from certain limitations and challenges, such as high cost, low a/b-wave amplitude, and the outcomes do not provide any information about patients. In this work, we designed and implemented a real-time prototype for an ERG system for measuring eye waves via diode-transistor logic (DTL)- electrode and AD624AD-model. In addition, a graphical user interface (GUI) via virtual instrument engineering workbench (LabVIEW) was used. The developed system achieved high amplitude for ERG a/b-waves of about 100 and 700 mV. In terms of a/b-waves in the system, the findings show that this study has good results for optimizing the measurement of ERG signals. The method showed satisfactory accuracy of about 92.5% for 10 participants aged 20-60 years and comprising both genders
A wheelchair control system based on Gyroscope of wearable tool can serve the disabled, especially in helping them move freely. The recent evolution of new technology means that unassisted, free movement has become possible. For this purpose, human–machine interface hands-free command of an electric-powered wheelchair can be achieved. In this paper, an electroencephalogram instrument, namely the EMOTIV Insight, was implemented in a human–computer interface to acquire the user’s head motion signals. The system can be operated based on the user’s head motions to carry out motion orders and control the motor of the wheelchair. The proposed system consists of an EMOTIV Insight brain-based gyroscope to sense head tilt, a DC motor driver to control wheelchair speed and directions, an eclectic-powered wheelchair, microcontroller, and laptop. We implemented the system in practice and tested it on smooth and rough surfaces in indoor/outdoor settings. The experimental results were greatly encouraging: disabled users were able to drive the wheelchair without any limitations. We obtained a significant average response time of 2 seconds. In addition, the system had accuracy, sensitivity, and specificity of 99%, 99.16%, and 98.83%, respectively.
<span>The fingerprint is <span>certainly one of the distinguishing features of the human body that is easily available and identifies one individual from another. The fingerprint sensor increases this distinctiveness, which is a device that can automatically classify or identify a person. The fingerprint based medical system is a more efficient means of storing clinical data for patients. It makes take advantage of fingerprint recognition technology to quickly and easily for determine the patient's past medical history. The system consists of an Arduino UNO board, a fingerprint sensor, an secure digital (SD) card module, and a micro-SD card. The suggested technology allows the use of a micro-SD card to store patient information as well as send it by internet. When this system was compared to the manual technique, the results indicate that the main advantage is that the proposed device saves a significant amount of time that manual searching and enrolling requires. Patients' information is simply collected and managed with this system, which has enhanced dependability, durability, and efficiency. It provides improved speed and performance, as well as better data security because the data is stored within the device.</span></span>
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