Author:
LeMoyne Robert,Mastroianni Timothy
Abstract
Wearable and wireless systems have progressively evolved to achieve the capabilities of Network Centric Therapy. Network Centric Therapy comprises the application of wearable and wireless inertial sensors for the quantification of human movement, such as reflex response, gait, and movement disorders, with machine learning classification representing advanced diagnostics. With wireless access to a functional Cloud computing environment Network Centric Therapy enables subjects to be evaluated at any location of choice with Internet connectivity and expert medical post-processing resources situated anywhere in the world. The evolutionary origins leading to the presence of Network Centric Therapy are detailed. With the historical perspective and state of the art presented, future concepts are addressed.
Reference117 articles.
1. LeMoyne R, Mastroianni T, Whiting D, Tomycz N. Wearable and Wireless Systems for Healthcare II: Movement Disorder Evaluation and Deep Brain Stimulation Systems. Springer, Singapore; 2019.
2. LeMoyne R, Mastroianni T, Whiting D, Tomycz N. Wearable and Wireless Systems for Movement Disorder Evaluation and Deep Brain Stimulation Systems. Wearable and Wireless Systems for Healthcare II: Movement Disorder Evaluation and Deep Brain Stimulation Systems 2019 (pp. 1-15). Springer, Singapore.
3. LeMoyne R, Mastroianni T. Wearable and Wireless Systems for Healthcare I: Gait and Reflex Response Quantification. Springer, Singapore; 2018.
4. LeMoyne R, Mastroianni T. Wearable and Wireless Systems for Gait Analysis and Reflex Quantification. Wearable and Wireless Systems for Healthcare I: Gait and Reflex Response Quantification 2018 (pp. 1-20). Springer, Singapore.
5. LeMoyne R, Mastroianni T. Machine Learning Classification for Network Centric Therapy Utilizing the Multilayer Perceptron Neural Network. Multilayer Perceptrons: Theory and Applications 2020 (pp. 39-76). Nova Science Publishers, Hauppauge, NY.