Use of wearable and wireless inertial sensor systems for treating movement disorders

In their new book, Applied Software Development with Python & Machine Learning by Wearable & Wireless Systems for Movement Disorder Treatment via Deep Brain Stimulation, Dr. Robert LeMoyne and Timothy Mastroianni present the confluence of wearable and wireless inertial sensor systems, such as a smartphone, and machine learning, for deep brain stimulation for treating movement disorders, such as essential tremor.

Machine learning distinguishes between distinct deep brain stimulation settings, such as its 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources.

Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for post-processing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on the quantification of a reach and grasp task for essential tremors using a smartphone as a wearable and wireless accelerometer system.

These skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity.

The authors present the utility of the amalgamation of applied software development using Python with machine learning and wearable and wireless systems for establishing the efficacy of deep brain stimulation.