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Journal : Indonesian Journal of Electrical Engineering and Computer Science

A rest tremor detection system based on internet of thing technology Safira Faizah; Dian Nugraha; Mohammed N. Abdulrazaq; Brainvendra Widi Dionova; Muhammad Irsyad Abdullah; Leni Novianti
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp476-484

Abstract

This article outlines the creation of a health detection system designed to identify rest tremors in Parkinson's disease (PD). The system leverages internet of things (IoT) technology to measure frequencies derived from human activities, excluding other symptoms such as heartbeat and voice recording. The core components include the Arduino Nano microcontroller and the Node ESP MCU 8266 V3 for data processing. The system employs an accelerometer sensor positioned at the body's center axis to gauge the frequency of motor symptoms associated with resting tremors, particularly when the hands are at rest in the lap. The findings indicate that 9 samples displayed symptoms of rest tremor. The recorded p-value, standing at 0.884, signifies a robust correlation between the two variables at a significant threshold of 0.01 or 1%.
Enhancing interaction and learning experience for deaf students through sign language translator Dian Nugraha; Safira Faizah; Mohamad Zaenudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1730-1738

Abstract

The study addresses persistent communication barriers faced by students with disabilities, particularly the deaf, by exploring challenges, presenting breakthroughs, and introducing an innovative solution-a sign language translator (SLT) using motion capture technology. This groundbreaking technology, deployed through the ADDIE model and validated with user acceptance testing (UAT), successfully integrates into the learning management system (LMS) at SLB Bina Insani Depok, demonstrating its efficacy in bridging communication gaps. The results suggest a notable increase in efficiency for tasks such as t2, t3, and t5, highlighting the system’s improved ability to direct users to the LMS homepage, the SLT page, and translate words into sign language, respectively. The study suggests further development in advanced animation to enhance the learning experience for deaf students and recommends progressing toward the total communication (KOMTAL) system for comprehensive communication preparation, ultimately aiming to create an inclusive and dynamic learning platform for the holistic development of deaf students.