Smart Insole Based Shuffling Detection System for Improved Gait Analysis in Parkinson's Disease

Gait analysis plays an important role in the diagnosis and treatment of Parkinson's disease. Gait disorders, such as foot shuffling, are common symptoms of Parkinson's disease and can affect the estimation of gait parameters, such as stance and swing durations. To avoid the influence of such gait deviations on measured gait parameters, it is important to detect their occurrence and duration. This study proposes a smart insole-based method using pressure sensors and an inertial measurement unit (IMU) for the continuous detection of foot shuffling steps and the estimation of the duration of shuffling activity within a step cycle. A total of 536 walking steps were obtained from 10 participants to classify foot shuffling steps from normal walking steps. The proposed method achieved an average classification accuracy of 97.8% when validated using a k-fold cross-validation approach. Accelerometer and pressure data were employed to estimate the duration of each shuffling step, achieving a root mean squared error of 0.018 seconds when compared to a ground truth obtained from an optical motion capture system. To the best of our knowledge, there is currently no existing method for estimating the duration of foot shuffling within a step cycle. These results demonstrate the potential of the proposed method to continuously detect the occurrence and duration of foot shuffling in a free-living environment and contribute to improved gait analysis for the diagnosis and treatment assessment of individuals with Parkinson's disease.