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All Journal Indonesian Journal of Innovation Studies
Ramadan, Nofri
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Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice

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Risk Detection System for Children Putting Objects into Mouth Based on Computer Vision using YOLOv11n: Sistem Deteksi Risiko Anak Memasukkan Benda ke dalam Mulut Berbasis Computer Vision menggunakan YOLOv11n Ramadan, Nofri; Muhtadi, Muis; Rafi, Muhammad Zulfiqar; Waluyo, Rinakit Estu
Indonesian Journal of Innovation Studies Vol. 26 No. 4 (2025): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i4.1851

Abstract

Background: Young children commonly place objects near or into their mouths, creating safety concerns that require constant supervision. Specific Background: Advances in computer vision enable real-time recognition of hands, faces, and objects, allowing automated identification of behaviors that may lead to hazardous mouth-related interactions. Knowledge Gap: Few systems combine object detection and human pose estimation specifically to assess risks related to toddler hand–mouth–object interactions in real-time environments. Aims: This study develops a risk detection system using YOLOv11n to recognize hands, faces, and objects while classifying conditions into safe or risky based on Euclidean distance between hand and mouth keypoints. Results: The system produces 92% accuracy in scenarios without objects and 74% in scenarios with objects, demonstrating its capability to differentiate between safe and risky conditions. Novelty: This research introduces an integrated spatial analysis approach that evaluates real-time proximity among hands, mouth, and objects, rather than detecting these elements independently. Implication: The system provides practical potential for real-time child safety monitoring, offering earlier awareness of mouth-related object risks in various activity settings. Highlights • The system identifies hand–mouth proximity to classify safe and risky situations.• Integrated pose estimation and object detection enable spatial risk assessment in real time.• The model supports early awareness of mouth-related object risks during toddler activities. Keywords Object Detection, Pose Estimation, Computer Vision, YOLO, Child Safety Monitoring
Application of YOLOv8 in Computer Vision-Based Facial Expression Detection of Toddlers: Penerapan YOLOv8 dalam Deteksi Ekspresi Wajah Balita Berbasis Computer Vision Waluyo, Rinakit Estu; Muhtadi, Muis; Ramadan, Nofri; Rafi, Muhammad Zulfiqar
Indonesian Journal of Innovation Studies Vol. 26 No. 4 (2025): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i4.1853

Abstract

Background: Facial expressions are a primary form of nonverbal communication for toddlers whose verbal abilities are still developing. Specific background: Advances in computer vision and deep learning have enabled real-time facial expression detection; however, most existing systems are designed using adult facial datasets and pretrained models. Knowledge gap: Research focusing on toddler facial expression detection using models trained exclusively on toddler data without pretrained weights remains limited. Aims: This study applies YOLOv8 to detect happy, sad, and neutral facial expressions of toddlers in real time using a model trained from scratch. Results: The proposed system achieved an average detection accuracy of 86%, with precision of 0.944, recall of 0.933, and mean Average Precision at 0.5 of 0.966, demonstrating stable real-time performance under varying lighting conditions. Novelty: The study demonstrates that YOLOv8 can learn toddler-specific facial expression patterns without relying on pretrained weights derived from adult facial data. Implications: The findings indicate the feasibility of deploying real-time toddler facial expression detection systems to support emotional monitoring in childcare and early education environments. Highlights Real-time detection of toddler facial expressions using YOLOv8. Model training conducted entirely from scratch using toddler facial datasets. Consistent detection performance observed in natural and varied environments. Keywords Facial Expression Detection, Toddler, YOLOv8, Computer Vision, Real-Time System
Evaluation of YOLOv8n, YOLOv10n, and YOLOv11n for Early Detection of Seizures in Infants: Evaluasi YOLOv8n, YOLOv10n, dan YOLOv11n untuk Deteksi Dini Kejang pada Bayi Rafi, Muhammad Zulfiqar; Muhtadi, Muis; Ramadan, Nofri; Waluyo, Rinakit Estu
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1855

Abstract

Background: Infant epilepsy is a serious neurological condition that is difficult to recognize visually because seizure manifestations often resemble normal infant reflexes. Specific background: Advances in computer vision and deep learning have enabled image-based seizure detection as a non-invasive approach for early identification in infants. Knowledge gap: Comparative evaluations of different YOLO model generations under identical experimental settings for infant seizure detection remain limited. Aims: This study evaluates and compares the performance of YOLOv8n, YOLOv10n, and YOLOv11n in detecting seizure activity from infant image data. Results: Using a dataset of 645 images across seizure and neutral classes, YOLOv11n achieved the highest precision, recall, and mean average precision at stricter localization thresholds, while YOLOv10n demonstrated the fastest inference time. Novelty: This study provides a systematic cross-generation evaluation of YOLO nano variants for infant seizure detection using uniform training parameters. Implications: The findings support informed selection of YOLO architectures for non-invasive, image-based early seizure detection systems in clinical and real-time monitoring contexts. Highlights • YOLOv11n shows the most stable detection performance for infant seizure images• YOLOv10n achieves the fastest inference time under identical training settings• Cross-version evaluation reveals clear trade-offs between detection stability and processing speed Keywords YOLO, Epilepsy, Computer Vision, Object Detection, Infant Seizure Detection
Co-Authors Muhtadi, Muis Rafi, Muhammad Zulfiqar Waluyo, Rinakit Estu