Biomechatronics and Cognitive Engineering Research Lab – IUST School of Mechanical Engineering

Biomechatronics and Cognitive Engineering Research Lab (BACE Lab)

This is a graduate level course (the first one) in the Mechatronics systems, and is designed for the graduate students of Mechatronics; other graduate students of Applied Mechanics may take this course as well.

Welcome to the Biomechatronics and Cognitive Engineering Research Lab (BACE Lab). Here, our main focus is to study human and his inherent capabilities. However, our approach is somehow a philosophical one; that is, we believe that the human is a creature with both physical and metaphysical aspects. Therefore, we use science and technology as tools to study the correlation between physics and metaphysics in the human being. We try to conduct the research in BACE Lab in such a way that this aim would be accessible more than ever.

Robotics and Biomechatronics, Dynamics and Control, and Processing of Biological Signals are the main axes of research in BACE Lab. In this regard, you might visit the research page.

Dr. Borhan Beigzadeh, Director of BACE Lab,
School of Mechanical Engineering, Iran University of Science and Technology.

Borhan Beigzadeh، Director
School of Mechanical Engineering, IUST

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Ongoing Research Projects

Previous Research Projects

Rehabilitation and Assistive Robotics

Robotic systems could be incorporated for rehabilitating and assisting humans with special disabilities. If any unrecoverable injury or loss of function happens to its body parts, it becomes a challenge to provide assistive and/or rehabilitating devices which are able to recover every aspect of the lost functionality. In BACE lab, we are in early stage of this project; an upper-limb, EMG-excited, simple exoskeleton was our first output in this regard.

EMG-Based Motion Intention Detection

Myoelectric control has a key role in human-machine interface applications such as orthosis control and teleoperation. Myoelectric signals are bio signals that are detectable from surface of the skin, and contain useful information about user’s moving intention. This project presents a methodology to estimate elbow joint angle from muscle’s data using neural network (NN). Proposed methodology can be expanded to estimate any joint angle by recording muscle activities concerning with the joint. Several data sets will be recorded and processed to train and test the NN. The trained network will use to predict elbow angles for individual data sets. We predict Results will show that trained network can estimate joint angle with an acceptable performance.