Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Electronics, Electromedical Engineering, and Medical Informatics

BHMI: A Multi-Sensor Biomechanical Human Model Interface for Quantifying Ergonomic Stress in Armored Vehicle Mutiara, Giva Andriana; Adiluhung, Hardy; Periyadi, Periyadi; Alfarisi, Muhammad Rizqy; Meisaroh, Lisda
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.877

Abstract

Ergonomic stress inside armored military vehicles presents a critical yet often overlooked risk to soldier safety, operational effectiveness, and long-term health. Traditional ergonomic assessments rely heavily on subjective expert evaluations, failing to capture dynamic environmental stressors such as vibration, noise, thermal fluctuations, and gas exposure during actual field operations. This study aims to address this gap by introducing the Biomechanical Human Model Interface (BHMI), a multi-sensor platform designed to objectively quantify ergonomic stress under operational conditions. The main contribution of this work is the development and validation of BHMI, which integrates anthropometric human modeling with embedded environmental sensors, enabling real-time, multi-dimensional ergonomic data acquisition during vehicle maneuvers. BHMI was deployed in high-speed off-road vehicle operations, simulating the 50th percentile Indonesian soldier’s seated posture. The system continuously monitored vibration (0–16 g range), noise (30–130 dB range), temperature (–40°C to 80°C), humidity (0–100% RH), and gas concentration (CO and NH₃) using calibrated, field-hardened sensors. Experimental results revealed ergonomic stress levels exceeding human tolerance thresholds, including vibration peaks reaching 9.8 m/s², cabin noise levels up to 100 dB, and cabin temperatures exceeding 39°C. The use of BHMI improved the repeatability and precision of ergonomic risk assessments by 27% compared to traditional methods. Seating gap deviations of up to ±270 mm were identified when soldiers wore full operational gear, highlighting critical areas of postural fatigue risk. In conclusion, BHMI represents a novel, sensor-integrated approach to ergonomic evaluation in military environments, enabling more accurate design validation, reducing subjective bias, and providing actionable insights to enhance soldier endurance, comfort, and mission readiness.
Implementation of Real-Time Face Recognition for Secure Weapon Storage Access Control Anisa, Anisa; Mutiara, Giva Andriana; Alfarisi, Muhammad Rizqy
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1608

Abstract

The security of weapon storage warehouses is a critical concern that requires an access control system with exceptionally high reliability, particularly in minimizing false acceptance, where unauthorized individuals are incorrectly granted access. In high-risk facilities, even a single false acceptance incident can lead to serious security consequences. Conventional systems based on physical keys or access cards present limitations, including risks of loss, duplication, and access forgery. Therefore, a biometric-based solution is necessary to enhance identification accuracy and strengthen overall security. This study aims to design and implement a reliable, high-security facial-recognition-based access control system for weapon storage facilities. The proposed system integrates a Multi-task Cascaded Convolutional Neural Network (MTCNN) for face detection, FaceNet for feature extraction, and a Support Vector Machine (SVM) for identity classification. The system is implemented as a standalone application on an edge computing device (mini PC) integrated with an electronic door lock. All detection and decision-making processes are performed locally without reliance on cloud services. System evaluation was conducted under various testing scenarios, including variations in lighting intensity, camera distance, facial attributes, and unregistered face testing. Experimental results show that the system achieved an accuracy of 96.25%. A precision of 100% indicates that no unauthorized access was granted. The recall reached 92.50%, reflecting a small proportion of rejected authorized users. The F1-score of 96.11% demonstrates balanced performance. The False Acceptance Rate was 0%, confirming complete prevention of illegal access. The False Rejection Rate was 7.50%, which remains acceptable in high-risk security environments. The system consistently rejected all unregistered faces and operated in real time with an average door unlocking response time of approximately 1.3 seconds. In conclusion, the proposed system provides reliable recognition performance with a strong emphasis on preventing false acceptance. These findings indicate its suitability for enhancing security in high-risk weapon storage facilities.