Multimodal monitoring for neurological disorders

Many medications and therapies are available to treat neurological movement disorder symptoms such as tremor, bradykinesia, postural instability, and gait disturbances. However, proper dosages and treatment combinations can be difficult to determine and often must be adjusted over time. Such adjustments are typically based on infrequent clinical evaluations. Mobile, long term monitoring of motor signs will allow better informed treatment adjustments and, as a result, has the potential to improve the quality of life for patients. With this in mind, we built a system for portable monitoring of neurological disorders in humans. The system analyzes movement signals from distributed wirelessly connected gyroscopes and accelerometers using a commercial smartphone, off-the-shelf attachments, and custom-programmed software. With approval from the Georgia Institute of Technology Institutional Review Board, we conducted a study with human subjects to evaluate whether unsupervised remote mobile monitoring might be possible with such a system, and, if so, how accurately the system could assess the signs of Parkinson's disease. We found that it was useful to consider data from multiple sensors in order to discriminate between normal and parkinsonian movement and that it was possible to identify activity types from the accelerometer and gyroscope data with high accuracy (1.0). After the activity type was identified, average discrimination accuracy between parkinsonian and normal conditions was 0.88. Additionally, individual symptoms of the disease could be accurately detected in > 0.8 of cases.