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Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.

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.

Deep brain stimulation offers the opportunity for patient specific and optimally tuned treatment to ameliorate Parkinson’s disease. Imperative for the efficacy of deep brain stimulation is the quantification of therapeutic response. The implementation of inertial sensor systems has considerably augmented the acuity of quantifying the characteristics of the movement disorder through the respective signal data. For example, these inertial sensors constitute effectively wearable and wireless systems that can be mounted about the dorsum of the hand to objectively quantify tremor. Using machine learning algorithms considerable classification accuracy has been achieved. Conformal wearable and wireless inertial sensor systems have been recently developed that can be conveniently mounted about the dorsum of the hand by an adhesive medium with a profile on the order of a bandage. Additionally, these conformal wearable and wireless inertial sensor systems can have their data wirelessly transmitted to a secure Cloud computing environment. This capability offers the opportunity to ascertain the inertial sensor signal for Parkinson’s disease tremor response in the context of multiple deep brain stimulation parameter configurations, such as an assortment of amplitude settings. The objective of the research was to quantify and distinguish the response of Parkinson’s disease tremor through an assortment of amplitude settings for deep brain stimulation, such as ‘Off’ setting 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, using a deep learning convolutional neural network through TensorFlow with the quantification of tremor by a conformal wearable and wireless inertial sensor system. Considerable classification accuracy was attained to distinguish between these specified deep brain stimulation settings for the amelioration of Parkinson’s disease.

The development of wearable and wireless inertial sensor systems enables a highly robust and convenient means for quantifying gait. In particular, the quantification hemiplegic gait can objectively identify disparity, such as with respect to reduced arm swing. Recent developments have produced the BioStamp nPoint, which is a conformal wearable and wireless inertial sensor system. The BioStamp nPoint is conveniently mounted by adhesive to an aspect of the human anatomy, such as the dorsum of the hand, for quantifying reduced arm swing for hemiplegic gait. The acquired inertial sensor data quantifying the characteristics of reduced arm swing for hemiplegic gait is wirelessly transmitted to a secure Cloud computing environment for subsequent post-processing. The pertinent gyroscope signal data is consolidated into a feature set suitable for machine learning classification. Using a multilayer perceptron neural network considerable classification accuracy is attained to distinguish between a hemiplegic affected arm with reduced arm swing and the unaffected arm. These accomplishments imply a pathway for ameliorating reduced arm swing through the application of conformal wearable and wireless inertial sensor systems, such as the BioStamp nPoint.

The advent of the conformal wearable and wireless inertial sensor enables expanded utility for progressive evolution of gait quantification. The acquired signal data, such as through a gyroscope, can be wirelessly transmitted to a secure Cloud computing environment for subsequent post-processing. In particular, the amalgamation of machine learning classification can be applied for the distinction of hemiplegic gait in the context of the hemiplegic affected leg and the unaffected leg. The integration of these technologies enables the realization of Network Centric Therapy, for which the patient can ascertain the benefit of gait quantification through extremely lightweight conformal wearable and wireless inertial sensor systems that have profiles relative to a bandage. Furthermore, the clinical rehabilitation team can remotely instill therapy optimized with the augmented acuity of machine learning. The research endeavor successfully demonstrates considerable classification accuracy through machine learning for the differentiation of a hemiplegic affected leg and unaffected leg during gait.

A standard aspect of the neurological examination involves the assessment of diadochokinesia. Diadochokinesia pertains the other swift alternation of an agonist and antagonist muscle pair, such as pronation and supination with respect to the forearm. For example, hemiparesis can manifest perceptible distinction for diadochokinesia with respect to the hemiplegic affected forearm and unaffected forearm. Standard techniques involve ordinal scales, which are inherently subjective, and the development of quantified methods has been recommended. The advent of conformal wearable and wireless inertial sensor systems, such as the BioStamp nPoint with a lightweight and bandage-like profile, enables quantification of diadochokinesia with wireless access to a Cloud computing environment for post-processing. The gyroscope signal data quantifying diadochokinesia can be consolidated to a feature set for machine learning classification to differentiate between a hemiplegic affected forearm and unaffected forearm. Considerable machine learning classification accuracy is attained for distinguishing between a hemiplegic affected forearm and unaffected forearm based on the quantified gyroscope signal data acquired from a conformal wearable and wireless inertial sensor system.

Eccentric strength training represents a robust rehabilitation and therapy capability, which is inherently oxygen efficient. The oxygen efficient properties of eccentric training are uniquely suited for elderly people with impaired cardiopulmonary capacity. Furthermore, this technique of eccentric training may be extended to people with reduced cardiopulmonary capacity due to post-symptoms from COVID- 19. Synthesis of conformal wearable and wireless inertial sensor systems with eccentric strength training enables the evolution of Virtual Proprioception for eccentric training. The conformal properties of the wearable and wireless inertial sensor, such as the BioStamp nPoint, feature the ability to flexibly mount by adhesive to effectively any segment of the human anatomy, such as about the forearm near the wrist for quantifying a biceps curl. Using Virtual Proprioception for eccentric training with a conformal wearable and wireless inertial sensor, such as the BioStamp nPoint, the eccentric phase can be emphasized by visualized feedback of the associated gyroscope signal with a predefined threshold. Machine learning classification, such as a support vector machine, achieves considerable classification accuracy to distinguish between standard strength training and eccentric strength training for a biceps curl through a preliminary application of Virtual Proprioception for eccentric training with a conformal wearable and wireless inertial sensor.

Deep brain stimulation enables highly specified patient-unique therapeutic intervention ameliorating the symptoms of Parkinson’s disease. Inherent to the efficacy of deep brain stimulation is the acquisition of an optimal parameter configuration. Using conventional methods, the optimization process for tuning the deep brain stimulation system parameters can intrinsically induce strain on clinical resources. An advanced means of quantifying Parkinson’s hand tremor and distinguishing between parameter settings would be highly beneficial. The conformal wearable and wireless inertial sensor system, such as the BioStamp nPoint, has a volumetric profile on the order of a bandage that readily enables convenient quantification of Parkinson’s disease hand tremor. Furthermore, the BioStamp nPoint has been certified by the FDA as a 510(k) medical device for acquisition of medical grade data. Parametric variation of the amplitude parameter for deep brain stimulation can be quantified through the BioStamp nPoint conformal wearable and wireless inertial sensor system mounted to the dorsum of the hand. The acquired inertial sensor signal data can be wirelessly transmitted to a secure Cloud computing environment for post-processing. The quantified inertial sensor data for the parametric study of the effects of varying amplitude can be distinguished through machine learning classification. Software automation through Python can consolidate the inertial sensor data into a suitable feature set format. Using the multilayer perceptron neural network considerable machine learning classification accuracy is attained to distinguish multiple parametric settings of amplitude for deep brain stimulation, such as 4.0 mA, 2.5 mA, 1.0 mA, and ‘Off’ status representing a baseline. These findings constitute an advance toward the pathway of attaining real-time closed loop automated parameter configuration tuning for treatment of Parkinson’s disease using deep brain stimulation.

Essential tremor is a prevalent neurodegenerative movement disorder. Deep brain stimulation represents a highly effective means of treatment, especially for scenarios for which traditional medical intervention is no longer feasible. One of the major post-operative challenges is the determination of an optimal set of tuning parameters. Optimizing the deep brain stimulation parameters can impart a time-intensive task to the clinician. The smartphone in the context of a wearable and wireless inertial sensor system offers the capability to objectively quantify the characteristics of the tremor. Machine learning in conjunction with a wearable and wireless inertial sensor system, such as a smartphone, can distinguish between disparate states, such as deep brain stimulation in ‘On’ and ‘Off’ status. Multiple machine learning classification techniques are available, such as the multilayer perceptron neural network, support vector machine, K-nearest neighbors, logistic regression, J-48 decision tree, and random forest. The objective of this research endeavor is to evaluate these six machine learning classification algorithms for classification of deep brain stimulation regarding ‘On’ and ‘Off’ status for Essential tremor during a reach and grasp task. The reach and grasp task is quantified through the smartphone as wearable and wireless inertial sensor system mounted to the dorsum of the hand and secured by latex glove. Multiple feature set scenarios are considered, such as recordings from both the accelerometer and gyroscope, accelerometer, and gyroscope. These scenarios facilitate the determination of the most robust machine learning algorithms. The multilayer perceptron neural network, support vector machine, K-nearest neighbors, and logistic regression achieve the highest classification accuracy for three feature set scenarios derived by recordings from both accelerometer and gyroscope, accelerometer, and gyroscope.

The evolutionary trends of wearable and wireless systems for the treatment of progressive neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor, have been elucidated. Wearable and wireless systems further synergize with advanced technologies, such as deep brain stimulation. These capabilities establish the basis for Network Centric Therapy for progressive neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor.

Inertial sensor systems, such as accelerometers, were proposed for the monitoring of human movement before their technology capability was sufficient for application to the human body. With sufficient progressive evolution, these sensors have been demonstrated for quantifying neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor. Initial success was demonstrated for matters, such as medication efficacy and symptom status. Their further recent evolution has elucidated utility regarding the preliminary context of wearable and locally wireless systems. A novel configuration was proposed for the use of wearable and wireless accelerometer systems to provide quantified feedback to establish a strategy for acquiring optimal parameter settings for a deep brain stimulation system. Further demonstration of wearable and locally wireless inertial sensor systems for objectively quantifying neurodegenerative movement disorder tremor symptoms has been provided with local wireless connectivity to a proximally situated personal computer for post-processing. These developments establish the foundation for the extension to wearable and wireless inertial sensor systems with considerable accessibility to the Internet, such as provided by the smartphone. This foundation sets the precedence for the emergence of Network Centric Therapy regarding the domain of quantifying neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor.

Ordinal scale strategies are standardly applied to diagnose the severity of neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor. A clinician is tasked with the challenge of assigning an ordinal parameter based on a series of criteria to quantify a subjectively observed interpretation. Multiple ordinal scale systems exist for evaluating movement disorder symptoms. However, the issue is the uncertainty of translating the findings of one scale to another. The Unified Parkinson’s Disease Rating Scale (UPDRS) and upgraded Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) are commonly utilized for evaluating Parkinson’s disease severity. The Fahn-Tolosa-Marin Tremor Rating Scale is prevalently applied for Essential tremor. There are issues of concern regarding the application of ordinal scale approaches for determining the state of progressive neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor. The reliability of ordinal scale systems has not been conclusively established, and interpretive disparity is apparent respective of experience. A novel resolution is the introduction of wearable and wireless inertial sensor systems to objectively quantify movement disorder tremor. The inertial signal (accelerometer and/or gyroscope) can readily record the intrinsic characteristics of tremor for both Parkinson’s disease and Essential tremor. Successful testing and evaluation have even demonstrated the efficacy of deep brain stimulation systems for Parkinson’s disease and Essential tremor using a smartphone as a wearable and wireless inertial sensor system. These findings enable the pathways for developing Network Centric Therapy, which is in essence the emergence of the Internet of Things for healthcare regarding the domains of robustly diagnosing severity of neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor.

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.

Movement disorders manifesting in tremor influence the quality of life for millions of people. In particular, two prevalent types of movement disorder are Parkinson’s disease and Essential tremor. The neurological foundation for Parkinson’s disease is attributed to dysfunction of the substantia nigra and associated aspects of the basal ganglia. By contrast, Essential tremor is not conclusively defined. However, notable amplified cerebellar activity is a characteristic for Essential tremor. Traditional strategies for diagnosing the severity of Parkinson’s disease and Essential tremor apply expert clinical although subjective interpretation of ordinal scales. This ordinal scale approach is the subject of contention regarding reliability. Traditional therapy involves the prescription of medication. As a last resort, permanent disruption of the deep brain neural pathways is an alternative. Recent developments have demonstrated the utility of wearable and wireless systems for the objective and quantified measurement of tremor symptoms. Furthermore, wearable and wireless systems have been amalgamated with deep brain stimulation for the determination of therapy efficacy. Near-term future objectives implicate the opportunity for real-time patient-specific optimization of deep brain stimulation tuning parameters. These developments lead to the presence of Network Centric Therapy for the treatment of movement disorders, such as Parkinson’s disease and Essential tremor.

The surgical procedure for instilling a deep brain stimulation system is an incredibly serious endeavor. A multiphase approach is applied for the implantation of the deep brain stimulation system, such as neurosurgery to position the electrodes and other surgical techniques to implant other aspects of the system. The quality of the surgical procedure can ensure against complication risks, such as infection and hemorrhaging. Electromagnetic interaction can pose hazards to the patient. However, the benefit of noninvasive imaging through magnetic resonance imaging (MRI) transcends the risk in light of the proper safety procedures. Other considerations involve the neurological and neuropsychological effects during the operation of the deep brain stimulation system. By addressing these concerns, a more comprehensive risk to benefit perspective can be established. Finally, a surgical procedure instilled at an internationally renowned hospital is presented. The actual parameter configuration tuning process advocated by the internationally renowned hospital is further discussed.