Designing remote Parkinson’s disease (PD) management systems requires critical decision-making in two phases: developing technologies to address gaps in existing systems, and designing treatment protocols (e.g., medication dosage). This research proposes a decision-making framework for PD remote monitoring, combining hardware/software analysis, patient needs, and cost factors. It introduces an AI-based decision support system for dynamic treatment planning, leveraging prior clinical data and technical specifications.
Healthcare IT advancements, like wearable devices and remote monitoring, have improved patient care. However, securing electronic health records (EHRs) remains a challenge due to weak security policies. Blockchain technology enhances data privacy, reliability, and transparency. For movement disorder patients—who require continuous care—this research proposes a blockchain-based remote monitoring system to ensure real-time treatment management, data integrity, and reduced security risks while minimizing clinic visits and costs.
Spatial awareness is a key human ability that enables environmental evaluation and navigation by integrating sensory data and spatial memory. It enhances cognitive navigation in complex settings (e.g., cities), improves spatial recall, and aids route optimization. Crucially, it allows humans to adapt to dynamic environments and leverage tools like maps for efficient wayfinding. Motion capture systems quantify this by analyzing joint kinematics and movement patterns during navigation tasks. This study will: (1) collect clinical questionnaires and motion data, (2) analyze behavioral patterns, and (3) develop an algorithm to assess cognitive navigation performance. The resulting framework will bridge motion analysis with real-world spatial challenges, offering insights for rehabilitation and assistive technologies.
Attention is a fundamental cognitive process that enables humans to focus limited mental resources on relevant stimuli. It plays a vital role in perception, memory, and decision-making. Different attention types exist, including selective, sustained, divided, and executive attention - crucial for goal-directed tasks. Measuring attention has become essential across fields like education and healthcare, as attention deficits impair daily functioning. While traditional paper-based tests exist, technological advances now enable computerized assessments using eye-tracking and motion analysis. This study develops an AI-based algorithm to evaluate attention by: (1) designing cognitive tests, (2) collecting eye movement and hand motion data via camera, and (3) analyzing patterns to create an accessible, reliable assessment tool.
Human decision-making is a complex cognitive process influenced by multiple factors. Eye-tracking technology has emerged as a powerful tool to study decision-making by revealing visual attention patterns and unconscious biases. This research integrates eye movements with machine learning and deep learning techniques to: (1) understand how gaze behavior reflects decision processes, (2) identify hidden influences on choices, and (3) improve decision outcomes. Data will be collected via cameras during cognitive tasks and then analyzed to correlate eye-tracking parameters with decision components. The goal is to develop an AI framework that decodes decision-making from eye kinematics, offering applications in human-computer interaction.
Inhibitory control (attentional and response inhibition) is a core executive function enabling the suppression of inappropriate responses. This research develops AI algorithms using motion-pattern analysis (eye and body movements captured mainly via camera) to assess inhibition. Machine learning and signal processing will extract biomarkers from kinematic data to evaluate inhibitory attention and measure response inhibition. The approach uniquely captures naturalistic behavioral signatures that traditional lab-based methods often miss. The developed tool addresses environmental noise (lighting, camera angle) and aims to replace costly equipment (EEG/eye-trackers) with accessible video-based analysis for clinical/behavioral applications.
Biomechatronic Systems Research Group - March 2025
Atiye received her B.Sc. in electrical engineering - electronics from Ferdowsi University of Mashhad, Mashhad, Iran. Her project was to design a motor speed controller for a photoresist spinner. She received her M.Sc. degree in bioelectric engineering from K. N. Toosi University of Technology. During her master's studies, she focused on the prediction of ventricular fibrillation using morphological features of ECG signal. Her work includes biomedical engineering in hospital-based health care, electrical installations testing, and safety observer of buildings as a member of Iran Construction Engineering Organization (IRCEO), and referee of science and technology park in electronics and bioelectric fields. Currently, Atiye is a Ph.D. student in bioelectric engineering at K. N. Toosi University of Technology. She is a member of Biomechatronic Systems research group and working on the implementation of a decision support system to help patients with Parkinson's disease.
Behnaz received her B.Sc. in software engineering and her M.Sc. in biomedical engineering from Hakim Sabzevari University, Sabzevar, Iran. Currently, she is a Ph.D. student in bioelectric engineering at K. N. Toosi University of Technology, where she is a member of the Biomechatronic Systems research group. Her research focuses on blockchain-based solutions for securing electronic health records and improving remote patient monitoring, particularly for movement disorders, to enhance data integrity and healthcare efficiency.
Akram, a Biomedical Engineer, specializes in biomedical signal processing, machine learning, and health care systems. She earned her bachelor's degree in Electrical Engineering from the University of Zanjan and a master's in Biomedical Engineering from K.N. Toosi University of Technology. As a researcher at the Biomechatronics Lab, she has played a crucial role in developing a hybrid algorithm that utilizes vital signs to classify the severity of COPD.
Neda received her B.Sc. in electrical engineering from Kermanshah University, Kermanshah, Iran. She is currently a M.Sc. student in mechatronics engineering, K. N. Toosi University of Technology. Her research is to develop wearables to help the visually impaired.
Arash received his B.Sc. in mechanical engineering. He is currently a M.Sc. student in mechatronics engineering, K. N. Toosi University of Technology. His research work is focused on developing smart wearable devices in cardiovascular care.
Sama completed her B.Sc. degree in mechanical engineering at the University of Tabriz, Tabriz, Iran. Her project was to model and analyze a self-balancing system based on Arduino and PID controllers. She is currently a M.Sc. student in mechatronics engineering at K. N. Toosi University of Technology. Her research is focused on investigating the relationship between executive dysfunction and human movement. She is interested in wearable technologies, biomechatronic systems, and gait and movement disorder assessment.
Ali completed his B.Sc. degree in electrical engineering at the University of Tehran, Tehran, Iran. He is currently a M.Sc. student in biomedical engineering at K. N. Toosi University of Technology.
Iman completed his B.Sc. degree in electrical engineering at Isfahan University of Technology, Isfahan, Iran. He is currently a M.Sc. student in biomedical engineering at K. N. Toosi University of Technology.
Yas is currently a B.Sc. student in electrical engineering - control at K. N. Toosi University of Technology, Tehran, Iran. Her work is focused on the design and development of smart and secure wearable infusion pumps.
Mobina received her B.Sc. in electrical engineering - communications from the University of Zanjan, Zanjan, Iran. Her project focused on designing and implementing a weather station and sending data to the operator with RF signals. She is currently a M.Sc. student in mechatronics engineering at K. N. Toosi University of Technology, Tehran, Iran. Her research is mainly devoted to optimal asset management and tracking in hospital environments. She is interested in the Internet of Things, mechatronics and biomechatronic systems, and non-invasive brain stimulation.
Ali received his B.Sc. in electrical engineering. He is currently a M.Sc. student in mechatronics engineering at K. N. Toosi University of Technology, Tehran, Iran. He is particularly interested in human-robot interactions, especially in retail environments.
Sobhan received his B.Sc. in mechanical engineering from K. N. Toosi University of Technology, Tehran, Iran, and he is currently an M.Sc. student in mechatronics engineering at K. N. Toosi University of Technology. Sobhan is all about artificial intelligence!