Disciplines
Biomedical Engineering and Bioengineering | Other Engineering
Abstract (300 words maximum)
This paper proposes a framework for integrating IoT and automation in a biomedical laboratory to improve safety, optimize processes, and enhance students' learning experience. The framework incorporates a centralized control unit and distributed subsystems to control equipment and machinery and includes autonomous robotics and intelligent monitoring systems. The paper presents the results of undergraduate students' work on automating various biomedical processes, including developing an Intelligent Autonomous Monitoring (IAM) device. IAM utilizes machine learning algorithms to identify outliers in processes and safety hazards. Moreover, IAM autonomously detects and localizes biomedical tools and equipment. Results show the feasibility of delivering real-time results for localizing specific tools necessary within a biomedical laboratory. This framework offers a systemic approach toward process automation, aiding researchers in developing new equipment and automating existing processes. Furthermore, it assists students in gaining a fundamental understanding of the theory behind biomedical principles while providing a repeatable experimental environment through more accurate data and event collection.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Razvan Voicu
Advanced Biomedical Laboratory (ABL) Synergy with Communication, Robotics, and IoT
This paper proposes a framework for integrating IoT and automation in a biomedical laboratory to improve safety, optimize processes, and enhance students' learning experience. The framework incorporates a centralized control unit and distributed subsystems to control equipment and machinery and includes autonomous robotics and intelligent monitoring systems. The paper presents the results of undergraduate students' work on automating various biomedical processes, including developing an Intelligent Autonomous Monitoring (IAM) device. IAM utilizes machine learning algorithms to identify outliers in processes and safety hazards. Moreover, IAM autonomously detects and localizes biomedical tools and equipment. Results show the feasibility of delivering real-time results for localizing specific tools necessary within a biomedical laboratory. This framework offers a systemic approach toward process automation, aiding researchers in developing new equipment and automating existing processes. Furthermore, it assists students in gaining a fundamental understanding of the theory behind biomedical principles while providing a repeatable experimental environment through more accurate data and event collection.