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Bhawna Shiwani, Delsys Research fellow at Worcester Polytechnic Institute, will be presenting her M.S. Thesis on: Autonomous tracking of bradykinesia in Parkinson’s disease during unscripted activities.

Autonomous tracking of bradykinesia in Parkinson’s disease during unscripted activities

Bradykinesia is one of the most prevalent yet poorly monitored motor symptoms of Parkinson’s Disease (PD). Generally defined by slowness of movements, the specific symptoms and severity of bradykinesia fluctuate throughout the day and are therefore difficult to assess and treat during relatively short duration visits to a clinician. Body-worn sensors and AI algorithms could provide valuable feedback for the clinical assessment of bradykinesia. However, to date no algorithm has yet been developed that can solve this problem. Therefore, I set out to design a software platform consisting of machine learning algorithms and clinically-informed metrics to provide real-time tracking of the motor symptoms associated with bradykinesia. Activity-specific neural-network detection algorithms were designed, trained and tested to classify sensor data from activities associated with minimal movement, such as non-walking, separately from those associated with more continuous movement, such as walking. Clinically-informed metrics were identified through rigorous signal analysis of EMG and IMU sensor data of muscle activity and limb movement to quantify the motor symptoms used for clinical assessment of bradykinesia. Real-time processing of more than 2000 minutes of movement data from PD patient provided the following results: 1) walking from non-walking activities were separated using a neural-network activity classifier with an accuracy of 99.5%; 2) a second neural network algorithm trained for walking data provided minute-by-minute detection of bradykinesia with an accuracy of 93.8%; 3) a third neural network algorithm trained for non-walking data provided minute-by-minute detection of bradykinesia with 97.4% accuracy. In addition, the clinically-informed metrics successfully quantified changes in motor symptoms of bradykinesia – such as poverty of movement, reduced limb velocity and reduced range of movement – that occurred before and after administration of PD medication. Together the detection algorithm and sensor-derived metrics provide a novel, proof-of-concept framework that establishes the clinical viability of a real-time tracking system for therapeutic interventions and patient-specific treatment.

About Bhawna Shiwani

Bhawna ShiwaniBhawna Shiwani is a Research fellow at Delsys Inc. She received her B.E. in Electronics and Communication Engineering from National Institute of Technology, Jalandhar, India in 2012; and is currently pursuing her M.S. in Robotics Engineering at Worcester Polytechnic Institute, Worcester, MA She was previously employed by Cadence Design Systems, Noida, India where she worked as a Member of Technical Staff in Analog Design Environment team. Her current research interests include artificial intelligence, control theory and cyber physical systems.

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