Applications of Biometric Sensing Technologies in Automated Systems

Abstract

Biometric sensing technologies are rapidly expanding beyond their traditional applications in security and authentication, becoming increasingly relevant to engineering, manufacturing, and industrial automation. By exploiting human physiological and behavioral characteristics, biometric sensors enable non-contact, high-precision, and secure human–machine interaction (HMI), while also supporting reliable operator authentication and real-time physiological monitoring. These capabilities can significantly improve workplace safety, system reliability, and operational efficiency in automated environments. In this study, we investigate the feasibility of using electromyographic (EMG) signals as an intuitive control modality for low-cost robotic systems through a simplified system architecture. The proposed modular and didactic framework is implemented using readily available, low-cost components, making it particularly suitable for educational and experimental applications. The platform provides a practical environment for investigating, implementing, and evaluating fundamental principles of myoelectric control, including EMG signal acquisition, processing, feature extraction, and robotic actuation control. Finally, we test a MoCap system for motion analysis and present the experimental results.