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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology)
Sugiarto Wibowo
Petra Christian University
Computer Science & IT

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Hand Symbol Classification for Human-Computer Interaction Using the Fifth Version of YOLO Object Detection Sugiarto Wibowo; Indar Sugiarto
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8520

Abstract

Human-Computer Interaction (HCI) nowadays mostly uses physical contact, such as people using the mouse to choose something in an application. However, there are certain problems that people face in using conventional HCI. The research tries to overcome some problems when people use conventional HCI using the computer vision method. The research focuses on creating and evaluating the object detection model for classifying hand symbols. The research applies the fifth version of YOLO with the architecture of YOLOv5m to classify hand symbols in real time. The methods are divided into three steps. Those steps are dataset creation consisting of 100 images in each class, training phase, and performance evaluation of the model. The hand gesture classes made in the research are ‘ok’, ‘cancel’, ‘previous’, ‘next’, and ‘confirm’, the dataset is made by the researchers custom. After the training phase, the validation results show 93% for accuracy, 99% for precision, 100% for recall, and 99% for F1 score. Meanwhile, in real-time detection, the performance of the model for classifying hand symbols is 80% for accuracy, 95% for precision, 84% for recall, and 89% for F1 score. Although there are differences, it still acceptable for the research and can be improved in future research.
ALZO: an outdoor Alzheimer's patient tracking system using internet of things Sugiarto Wibowo; Indar Sugiarto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 6: December 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i6.25156

Abstract

Alzheimer's patients have an abnormal brain that affects some functionalities such as memory and motoric function. Some patients experience disorientation, such as losing their way back home, and impaired lower body motoric function, leading to stumbling. To overcome these problems, we propose a wearable device called Alzo (Alzheimer locator) for tracking Alzheimer patients during outdoor activities. Alzo can detect the patient's location and is also equipped with a fall detection algorithm. The sensor produces an accelerometer and quaternion value, which are used for calculating alpha (represents activity acceleration) and theta (represents body orientation). The location and the patient's fall condition could be monitored using a mobile-based application. The experiments were conducted by operating the Alzo system to detect the patient's location and fall condition. The results showed that Alzo worked for about 3 hours and sent location data 1-5 times if lost or fall detected. Furthermore, thresholds for the fall detection algorithm were 235 m/s2 (lower-alpha), 8,108 m/s2 (higher-alpha), and 70⸰ (theta). These thresholds were determined based on the experiment which includes standing up, walking, jumping, sitting down, cycling, jogging, bowing, and squatting. From the experiment, the fall detection algorithm achieved 93.33% of accuracy.
Co-Authors Indar Sugiarto