Wearable and Wireless Systems with Internet Connectivity for Quantification of Parkinson’s Disease and Essential Tremor Characteristics

Wearable and wireless systems for the objective quantification of neurodegenerative movement disorder status, such as Parkinson’s disease, have been successful achieved through the application of a smartphone. Preliminarily, the smartphone represented a wearable and wireless accelerometer system, which could be readily mounted to the dorsum of the hand through a glove. The initial proof-of-concept demonstration had broad implications. The experimental and post-processing resources were situated on effectively opposite sides of the continental United States of America. Through the smartphone’s wireless connectivity to the Internet, the post-processing resources to reduce the data and the experimentation sited could be located effectively anywhere in the world. Furthermore, the experimental location could be selected based on the patient’s preference. Another exemplary wearable and wireless system is the portable media device. As an extension of this wearable and wireless system capability, the smartphone was successfully applied to ascertain from a quantified perspective the efficacy of deep brain stimulation for Essential tremor. Extrapolations of inertial signal data for a wearable and wireless system, such as a smartphone, advocate the application of machine learning classification to distinguish between deep brain stimulation efficacy regarding “On” and “Off” status. Future evolutions of wearable and wireless systems for the objective quantification of neurodegenerative movement disorder status, such as Parkinson’s disease and Essential tremor, underscore the value of local wireless connectivity from an inertial sensor node to a more powerful wireless system, such as a smartphone or tablet, to achieve Internet connectivity. These trends provide preliminary realization of the opportunities that Network Centric Therapy can enable with inertial sensor signal data stored in a Cloud computing database for post-processing to achieve patient-specific intervention and optimized deep brain stimulation parameter configurations.