There has been a lot of interest in recent years in using inertial sensors (accelerometers and gyroscopes) to monitor movement disorder motion and monitor the efficacy of treatment options. Two of the most prominent movement disorders, which are under evaluation in this research paper, are essential tremor (ET) and Parkinson’s disease (PD). These movement disorders are first evaluated to show that ET and PD motion often depict more (tremor) motion content in the 3–12 Hz frequency band of interest than control data and that such tremor motion can be characterized using inertial sensors. As well, coherence analysis is used to compare between pairs of many of the six degrees-of-freedom of motions under evaluation, to determine the similarity in tremor motion for the various degrees-of-freedom at different frequency bands of interest. It was quite surprising that this coherence analysis depicts that there is a statistically significant relationship using coherence analysis when differentiating between control and effectively medicated PD motion. The statistical analysis uncovers the novel finding that PD medication induced dyskinesia is depicted within coherence data from inertial signals. Dyskinesia is involuntary motion or the absence of intended motion, and it is a common side effect among medicated PD patients. The results show that inertial sensors can be used to differentiate between effectively medicated PD motion and control motion; such a differentiation can often be difficult to perform with the human eye because effectively medicated PD patients tend to not produce much tremor. As well, the finding that PD motion, when well medicated, does still differ significantly from control motion allows for researchers to quantify potential deficiencies in the use of medication. By using inertial sensors to spot such deficiencies, as outlined in this research paper, it is hoped that medications with even a larger degree of efficacy can be created in the future.

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A weighted-frequency Fourier linear combiner (WFLC) filter is used for removal of tremor motion for data captured from movement disorders subjects with essential tremor (ET) and Parkinson's disease (PD). This technique is applied here in six degrees-of-freedom and data are filtered so that a comparison can be made before and after filtering to assess the extent to which the WFLC filter removed tremor. A wavelet spectral analysis is employed to determine the effectiveness of the filter in removing tremor in the 3-12 Hz band of interest. A Kalman filter is employed to improve data processing so that six degree-of-freedom tremor motion can be accurately rendered for subsequent filtering; such accurate rendering is needed so that full tremor motion can be adequately described. A Fourier coherence based technique is utilized so that relationships for interrelated tremors for the different six degrees-of-freedom can be identified. Much of the analysis shown is novel.

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