Tremor Severity Rating by Markov Chains

The paper deals with mathematical modeling tools for tremor quantification, a problem arising in e.g. clinical applications and sports. Tremor is an involuntary repetitive movement of extremities, head, or trunk that occurs in disease but also in health, due to e.g. strain or fatigue. Quantification of tremor is traditionally performed by ocular observation, while numerous technologies based on wearable accelerometer data exist and have been tested in medical practice. The currently available approaches rely on spectral analysis that reduces a fundamentally nonlinear and non-stationary phenomenon to a linear combination of harmonic components. The classical nonlinear identification methods are as well of limited use because the underlying system is essentially autonomous and produces sustained oscillations without exogenous excitation. An alternative view on tremor is therefore adopted that treats the problem from a severity rating perspective aligned with clinical practices. The tremor amplitude is modelled by a Markov chain, where the states represent the predefined intervals of severity. A comparison with a previously developed event-based method of tremor quantification is provided on data collected using a smart phone in a patient diagnosed with Parkinson disease and undergoing Deep Brain Stimulation therapy. The experimental procedure is unobtrusive and can be implemented in a way that is transparent to the patient