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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MEDIA KONSERVASI Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Applied Engineering and Technology
Kohei Arai
Graduate School of Science and Engineering, Saga University, Saga City, Japan
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Optimized yolov8 to identify normal and disabled people Wulanningrum, Resty; Handayani, Anik Nur; Herwanto, Heru Wahyu; Arai, Kohei
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1977

Abstract

This research aims to develop an object detection model capable of distinguishing the gait of normal and disabled people with high accuracy. Object detection is currently being developed to detect people and is being implemented in normal and gender-based gait recognition. Gait recognition, if further observed, includes recognizing both normal people and people with disabilities. Normal people walk like most people, but people with disabilities have different gaits from normal people. Some use walking aids, while others walk without them. Yolov8 is a platform for detecting people. This research proposes an object detection for normal people and people with disabilities, both those who use assistive devices and those who do not. The dataset used is Disabled gait, comprising 6500 images, and will be split into 3 data splits: 70% for training, 20% for validation, and 10% for testing. Model evaluation uses precision, recall, mAP50, and mAP50-90. The test results for 3 classifications, namely assistive, non-assistive, and normal, show the highest value in the assistive class with an mAP50 value of 0.98 and an mAP50-95 value of 0.996. This research provides a reliable gait-based object detection model that can accurately distinguish normal individuals from people with disabilities, including those using assistive devices. The findings support the development of more inclusive surveillance, healthcare, and mobility-assessment systems through high-performance detection validated by strong mAP scores.
Co-Authors Aji Prasetya Wibawa Ali Ridho Barakbah Andrie Asmara, Rosa Anik Nur Handayani Ariyanta, Nadindra Dwi Burhanuddin, Mohd Aboobaider Damayanti, Farradila Ayu Daud, Nathan Dimas Wahyu Wibowo Faqih, Kamil Heru Wahyu Herwanto Hitipeuw, Emanuel Indriati Indriati Jevri Tri Ardiansah Kurnianingtyas, Diva Marji Marji Mustika Mentari Nugraha, Agil Zaidan Priharta, Ari Primadi, Wahyu Rahmat Samudra Anugrah, Muhammad Resty Wulanningrum Rosa Andrie Asmara Sakti Oktafiansyah, Haidar Salsabila, Reni Fatrisna