A Wearable Sensor-Based Framework for Quantifying Imbalance in Parkinson's Disease: A Proof-of-Concept Study Using Biomechanical Analysis and OpenSim Modeling
Neil Daru
30/09/2026
Parkinson’s disease (PD) is the second most common and dangerous progressive neurodegenerative disease, affecting motor symptoms and causing more than 329,000 global deaths annually. Current methods to diagnose and understand impairments in motor and cognitive function can cost hundreds of thousands of dollars annually, making it difficult for individuals in low-income and rural communities to access adequate technology and equipment. Musculoskeletal simulations and a wearable sensor present an affordable and accessible solution to track movement qualities quantitatively in real-time to analyze movement during daily tasks. In this study, a neurologically healthy adult and a patient with Parkinson’s disease were examined on their postural stability across 3 trials, each repeated thrice, where the normal adult was used as a negative control and the Parkinson’s patient as the experimental participant (PD participant). The results showed that the PD participant demonstrated peak pitch displacement approximately 279% greater than the control during the standing trial (22 vs. ~5.8), and a sustained lateral roll deviation peaking at 37° to the left during walking compared to a maximum of approximately 2° to the left in the control. The time taken to complete each trial shows that the PD participant took longer on the standing and walking trials (+3.48 s and +7.54 s, respectively) but terminated the twisting trial earlier than the control due to instability. This study provided a proof-of-concept of the effectiveness of small wearable systems in quantifying movement characteristics in Parkinson's patients, presenting applications as an inexpensive and large-scale tracking system.