Real-Time Estimation of Pathological Tremor Parameters from Gyroscope Data

This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading

Filtering Voluntary Motion for Pathological Tremor Compensation

International audienceThis paper describes an algorithm to estimate tremor and voluntary motion from measured motion data. Estimation is performed by means of an extended Kalman filter (EKF), which also estimates tremor parameters. Comparison of the proposed method with techniques described in the literature are conducted with two experimental data sets from tremor patients performing the same task, drawing a spiral. The presented algorithm may be directly applied in real-time pathological tremor compensation systems