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All Journal JOIV : International Journal on Informatics Visualization JURNAL DINAMIKA VOKASIONAL TEKNIK MESIN Data Science: Journal of Computing and Applied Informatics JISIP: Jurnal Ilmu Sosial dan Pendidikan JUPE : Jurnal Pendidikan Mandala Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian Masyarakat Bangsa Journal Of Engineering Sciences (Improsci)
Dalimarta, Fahmy Ferdian
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Journal : Data Science: Journal of Computing and Applied Informatics

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Towards Automated Motor Impulsivity Monitoring in Real-world Scenarios: A Multiple Object Tracking Approach Dalimarta, Fahmy; Andono, Pulung Nurtantio; Soeleman, Moch. Arief; Hasibuan, Zainal Arifin
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 1 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v9.i1-16686

Abstract

Assessment of motor impulsivity often faces several challenges. Conventional assessments that rely on controlled settings often fail to capture impulsive behaviors in real-world contexts. This study proposes an automated approach using Multiple Object Tracking (MOT) technology to assess motor impulsivity. The aim was to develop a system for detecting and quantifying motor impulsivity in naturalistic, multi-person environments. By employing cutting-edge MOT algorithms, the solution tracks multiple individuals concurrently, enabling movement and interaction analyses. This methodology integrates MOT with behavioral models to identify motor impulsivity patterns such as abrupt trajectory changes or impulsive gesturing. Trained on real-world annotated datasets, the system ensures adaptability across settings. Our approach successfully distinguished impulsive movements from typical behavioral patterns, with an accuracy of 95.43%. This approach could revolutionize assessments by providing objective and quantitative measurements and facilitating enhanced diagnostics and personalized interventions. Extensive evaluations are required to assess real-time capabilities, robustness in occluded environments, and accurate impulsive pattern identification. These findings could enable broader clinical, research, and behavioral monitoring applications, advancing our understanding of the implications of motor impulsivity.
Co-Authors Andono, Pulung N. Didi Kusaeri Doni Setiawan Faoziah, Nina Faoziyah, Nina Jaelani Jaelani Muzakki Mahdi Nugroho Susanto Pratama, Aziz Yulianto Pulung Nurtantio Andono Randi Dwi Wibisono Soeleman, Moch. A. Soeleman, Moch. Arief Sulistyo, Adi Zainal A. Hasibuan Zainal Arifin Hasibuan