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This book provides a far-sighted perspective on the role of
wearable and wireless systems for movement disorder evaluation,
such as Parkinson's disease and Essential tremor. These
observations are brought together in the application of quantified
feedback for deep brain stimulation systems using the wireless
accelerometer and gyroscope of a smartphone to determine tuning
efficacy. The perspective of the book ranges from the pioneering
application of these devices, such as the smartphone, for
quantifying Parkinson's disease and Essential tremor
characteristics, to the current state of the art. Dr. LeMoyne has
published multiple first-of-their-kind applications using
smartphones to quantify movement disorder, with associated
extrapolation to portable media devices.
This book provides visionary perspective and interpretation
regarding the role of wearable and wireless systems for the domain
of gait and reflex response quantification. These observations are
brought together in their application to smartphones and other
portable media devices to quantify gait and reflex response in the
context of machine learning for diagnostic classification and
integration with the Internet of things and cloud computing. The
perspective of this book is from the first-in-the-world application
of these devices, as in smartphones, for quantifying gait and
reflex response, to the current state of the art. Dr. LeMoyne has
published multiple groundbreaking applications using smartphones
and portable media devices to quantify gait and reflex response.
This book provides visionary perspective and interpretation
regarding the role of wearable and wireless systems for the domain
of gait and reflex response quantification. These observations are
brought together in their application to smartphones and other
portable media devices to quantify gait and reflex response in the
context of machine learning for diagnostic classification and
integration with the Internet of things and cloud computing. The
perspective of this book is from the first-in-the-world application
of these devices, as in smartphones, for quantifying gait and
reflex response, to the current state of the art. Dr. LeMoyne has
published multiple groundbreaking applications using smartphones
and portable media devices to quantify gait and reflex response.
The book presents the confluence of wearable and wireless inertial
sensor systems, such as a smartphone, for deep brain stimulation
for treating movement disorders, such as essential tremor, and
machine learning. The machine learning distinguishes between
distinct deep brain stimulation settings, such as '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 quantification of
a reach and grasp task for essential tremor using a smartphone as a
wearable and wireless accelerometer system.Furthermore, 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.
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