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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.

Recently, machine learning has augmented the capability of the amalgamation of wearable and wireless systems for deep brain stimulation systems. Machine learning platforms have been applied to attain considerable classification accuracy for distinguishing between deep brain stimulation set to “On” and “Off” modes for Essential tremor and Parkinson’s disease. Other movement disorders, such as hemiplegic affected and unaffected limb pairs, have been successfully differentiated through machine learning classification. Central to these machine learning endeavors has been the application of wearable and wireless systems using inertial sensors, such as the accelerometer and gyroscope, to consolidate signal data into feature sets for machine learning classification. An assortment of prevalent machine learning platforms is discussed, such as J48 decision tree, K-nearest neighbor, logistic regression, support vector machine, multilayer perceptron neural network, and random forest. Machine learning is envisioned to serve an instrumental role for the objective of achieving closed-loop optimization of deep brain stimulation parameter configurations. In essence, machine learning is envisioned to function as a predominant role for the post-processing perspective of Network Centric Therapy.

The considerable advantage of integrating wearable and wireless systems with machine learning for the assessment of deep brain stimulation parameter configuration status is addressed. A wearable and wireless system, such as a smartphone with its accelerometer and gyroscope, provides the quantified basis for the efficacy determination of a treatment strategy. In particular, deep brain stimulation is well suited for being amalgamated with wearable and wireless systems. For a subject with Parkinson’s disease, deep brain stimulation set to “On” and “Off” status is distinguished through an assortment of machine learning algorithms, such as J48 decision tree, K-nearest neighbors, logistic regression, support vector machine, multilayer perceptron neural network, and random forest. The feature set is consolidated from the accelerometer signal and gyroscope signal from a smartphone using software automation. The appropriateness for these machine learning algorithms was assessed in terms of both classification accuracy and computational efficiency. These capabilities further refine the opportunities of machine learning classification being allocated local to the wearable and wireless system with an available Cloud computing resource. These findings establish a preliminary perspective regarding the utility of Network Centric Therapy, for which effectively real-time optimization of parameter configurations for deep brain stimulation can be developed in a patient-specific context. Furthermore, the real-time optimization process can be adaptive to the inherent temporal fluctuations of a progressive neurodegenerative movement disorder, such as Parkinson’s disease and Essential tremor.

The previous ten chapters demonstrate the vast utility of wearable and wireless systems for the quantification of reflex and gait. These evolutionary trends are envisioned to facilitate the development of Network Centric Therapy. A perspective from the authors on the role of Network Centric Therapy and associated opportunities is briefly presented.

The capacity to quantify the movement features of a person undergoing the rehabilitation process enables therapists and clinicians to proactively optimize the therapy strategy. Wearable and wireless systems, such as the smartphone and portable media device, are equipped with accelerometers and gyroscopes that can readily quantify aspects of human movement pertinent to rehabilitation, such as gait and reflex response. The smartphone and portable media device can measure gait and reflex response through their inertial sensors, and the acquired data can be conveyed by wireless transmission to the Internet as an email attachment. This capability enables the experimental site and post-processing resources to be remotely situated. Three phases of the evolution of quantification techniques for the rehabilitation process are observed, which are characterized as a first, second, and third wave. The first wave pertains to the traditional ordinal scale approach used by expert clinicians. The second wave emphasizes the role of quantification systems that are generally constrained to a clinical setting. The third wave envisions the development of Network Centric Therapy through the application of wearable and wireless systems, such as smartphones and portable media devices, for quantifying movement characteristics, such as gait and reflex response. Network Centric Therapy encompasses a quantum leap in rehabilitation capability through Cloud Computing amalgamated with machine learning with patient and therapy team situated remotely anywhere in the world. A summary of each chapter is further presented.

With the advent of wireless technology and inertial measurement units, the prevalence of wireless accelerometers is addressed for quantification of gait, reflex response, and reflex latency. Over the course of four generations of research, development, testing, and evaluation the ability to quantify patellar tendon reflex response and latency has been achieved in an accurate, reliable, and reproducible manner. As a transitional phase to the research, development, testing, and evaluation cycle an artificial reflex device was also applied. The central themes to the wireless quantified reflex device are tandem operated wireless accelerometer nodes that are effectively wearable for deriving response and latency and a potential energy impact pendulum for evoking the patellar tendon reflex. The successful application of these wireless accelerometers that are wearable has been further extended toward the quantification of hemiplegic gait, and real-time modification of hemiplegic gait through the quantified feedback of Virtual Proprioception. Other developments regarding the use of wireless accelerometers that are wearable are further addressed.

The smartphone and portable media device are equipped with inertial sensors, such as an accelerometer and gyroscope. With the proper software application they can function as wireless accelerometer and gyroscope platforms. This capability enables the smartphone and portable media device to function as wearable and wireless systems for gait and reflex response. The experimental trial data can be conveyed through wireless connectivity to the Internet as an email attachment for post-processing. The signal data can be further consolidated into a feature set for machine learning classification. Many experimental scenarios pertaining to quantifying the domains of gait and reflex response are presented. The smartphone and portable media device present an insightful perspective of the significant potential of Network Centric Therapy.

The context of smartphones and portable media devices as wearable and wireless systems can logically be extrapolated to homebound therapy, especially with regards to of a rehabilitation for hemiparesis from traumatic brain injury and stroke. Four applications are addressed. The portable media device operating as a functionally wireless accelerometer platform can be mounted to a cane for machine learning classification to distinguish appropriate and inappropriate use. An ankle rehabilitation system can apply a smartphone as a wireless gyroscope to differentiate between a hemiplegic ankle and unaffected ankle. Further applications using a portable media device as a wireless gyroscope platform involve the use of a wobble board with machine learning also classifying between a hemiplegic ankle and unaffected ankle. Another scenario applies the smartphone as a wireless gyroscope for Virtual Proprioception as feedback for eccentric training while applying machine learning to classify between Virtual Proprioception feedback and without Virtual Proprioception feedback for eccentric training. These preliminary systems are capable of providing essentially autonomous homebound therapy amendable for Network Centric Therapy.

Wearable and wireless systems equipped with the ability to mutually record the accelerometer and gyroscope signal can be applied to sensor fusion. Sensor fusion can provide the location of the inertial sensor with trajectory information, such as displacement, velocity, and acceleration as a function of time. In order to achieve the results of sensor fusion multiple subjects must be applied, such as the use of quaternion mathematics and orientation filtering. A traditional orientation filter is the Kalman filter; however, the gradient descent orientation filter offers a more computationally robust alternative that is suitable for wearable and wireless systems. The result information provided by sensor fusion is particularly useful for the assessment of gait trajectory. Sensor fusion is anticipated to enhance Network Centric Therapy with improved visualization of patient status.

Conventional gait quantification is provided in a highly structured clinical setting. These devices represent a metaphorical second wave encompassing clinically standard quantification techniques. Traditional gait quantification systems, such as force plates, EMG, foot-switches, and motion capture systems are described in the chapter for gait analysis. Their relevance for objectively quantifying the status of a patient’s rehabilitation progress is advocated. Regarding reflex quantification the application of motion capture systems, EMG, and strain/force sensors are covered in the chapter. There are drawbacks of these devices, such as expense, complexity, and limitations to a clinical setting. By contrast, wearable and wireless systems are projected to transcend the capabilities of these traditional quantification systems with expanded autonomy for subject evaluation in the context of Network Centric Therapy.

Bluetooth wireless enables localized connectivity to a smartphone, portable media device, and tablet. Rather than using these devices as wearable and wireless systems alone, the nature of Bluetooth wireless enables locally situated inertial sensors to be mounted to a subject for quantified evaluation of gait. The smartphone, portable media device, and tablet can then wirelessly transmit the data to a Cloud Computing resource for post-processing. Preliminary demonstration is presented regarding the machine learning classification of gait for Friedreich’s ataxia. A perspective of the application of Bluetooth wireless for reflex quantification is presented. Themes, such as sensor fusion and the Internet of Things, are further discussed. The prevalence of Bluetooth wireless further establishes the realization of Network Centric Therapy.

An inherent aspect of the development of wearable and wireless systems has been the progressive evolution of the inertial measurement unit. Although when preliminarily recommended for quantifying the aspects of human movement, the inertial measurement was not sufficiently developed for application as a wearable and wireless system. With the steady advance from other industries accelerometers became feasible as wearable applications for monitoring activity status and other biomedical and rehabilitation themed scenarios. Eventually wearable accelerometer systems developed from data logger configurations to devices with local wireless connectivity.

Over the span of the past decade machine learning has been applied to distinguishing between disparate health status scenarios with considerable classification accuracy. Recent examples pertain to notable classification accuracy with regards to gait and reflex response disparity, especially in the context of a hemiplegic affected leg and unaffected leg. Machine learning classification serves as an instrumental post-processing methodology for the signal acquired through a wearable and wireless accelerometer or gyroscope. A summary of machine learning platforms is presented. The application and demonstration of machine learning as a diagnostic tool is described within the scope of gait, reflex response, and associated subjects. The amalgamation of machine learning and wearable and wireless systems is anticipated to further evolve Network Centric Therapy with capabilities, such as prognostic assessment of rehabilitation, objective consideration of therapy efficacy, therapy optimization, and diagnosis of appropriate transitional phases of therapy strategy.