**Author details**

*Wireless Sensor Networks - Design, Deployment and Applications*

onstrated considerable classification accuracy [113].

between all six of these parameter configurations [116, 117].

**movement disorder assessment with machine learning**

classification accuracy and time to develop the machine learning model. Based on these criteria the K-nearest neighbors machine learning algorithm displayed the optimal satisfaction of classification accuracy in conjunction with time to develop the machine learning model and the support vector machine achieved the optimal classification accuracy [112]. The multilayer perceptron neural network also dem-

Deep learning was then applied to distinguish between deep brain stimulation parameter configuration settings for the treatment of Parkinson's disease, such as 'Off' status as a baseline, amplitude set to 1.0 mA, amplitude set to 1.75 mA, amplitude set to 2.5 mA, amplitude set to 3.25 mA, and amplitude set to 4.0 mA. The BioStamp nPoint conformal wearable and wireless inertial sensor system provided the accelerometer signal data. The post-processing was facilitated by Google Colab and TensorFlow to implement a convolutional neural network. The convolutional neural network achieved considerable classification accuracy to distinguish

**8. Future perspectives for Network Centric Therapy for reflex, gait, and** 

Network Centric Therapy is anticipated to have a transformative influence on the healthcare and biomedical industry. Conformal wearable and wireless inertial sensor systems are envisioned to enable historical and distinctly quantified data for subjects undergoing rehabilitation and subjects with neurodegenerative movement disorders, such as Parkinson's disease and Essential tremor. Data science methodologies can be incorporated to optimize the respective therapy strategy. With the amalgamation of machine learning and eventually deep learning conformal wearable and wireless inertial sensor systems are predicted to considerably advance augmented clinical situational awareness for diagnostic and prognostic capabilities. In particular, with the Cloud computing accessibility intrinsic to Network Centric Therapy, the most talented clinical resources from anywhere in the world can provide optimal patient specific rehabilitation and therapy to subjects from the convenience of a homebound setting. Additionally, the inherent aspects of Network Centric Therapy, such as conformal wearable and wireless inertial sensor systems, machine learning, and Cloud computing access, imply a plausible pathway to the closed-loop optimization of deep brain stimulation parameter configurations.

The evolutionary perspective for the advent of Network Centric Therapy for the domains of assessing reflex, gait, and movement disorders have been thoroughly discussed. Inherent aspects pertaining to Network Centric Therapy involve wearable and wireless inertial sensor systems, machine learning, and Cloud computing access for the acquired inertial sensor signal data. The implications are that expert clinicians can access a patient's health status based on the wearable and wireless inertial sensor system signal data from anywhere in the world. These achievements constitute a significant evolution relative to traditional ordinal scale methodologies and electro-mechanical signal data obtained by clinical laboratory resources. Conformal wearable and wireless inertial sensor systems have further evolved the capabilities of Network Centric Therapy. In the future Network Centric Therapy is envisioned to augment clinical diagnostic and prognostic acuity, optimize rehabilitation, and enable closed-loop optimization of deep brain stimulation parameter configurations.

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**9. Conclusion**

Robert LeMoyne1 \* and Timothy Mastroianni2

1 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

2 Founder of Cognition Engineering, Pittsburgh, PA, USA

\*Address all correspondence to: robert.lemoyne@nau.edu; rlemoyne07@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
