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Applied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulationRobert LeMoyne, Timothy Mastroianni.
remainder title:	Applied software development with Python and machine learning by wearable and wireless systems for movement disorder treatment via deep brain stimulation
Author:	LeMoyne, Robert
other author:	Mastroianni, Timothy.
Published:	Singapore :World Scientific,c2022.
Description:	1 online resource (248 p.)
Subject:	Movement disordersTreatment
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/12249#t=toc
ISBN:	9789811235962$q(ebook)

Applied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulation
LeMoyne, Robert

Applied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulation[electronic resource] /Applied software development with Python and machine learning by wearable and wireless systems for movement disorder treatment via deep brain stimulationRobert LeMoyne, Timothy Mastroianni. - Singapore :World Scientific,c2022. - 1 online resource (248 p.)

Includes bibliographical references and index.

"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."--

Mode of access: World Wide Web.

ISBN: 9789811235962$q(ebook)Subjects--Topical Terms:

931271
Movement disorders
--Treatment

LC Class. No.: RC376.5 / .L559 2022

Dewey Class. No.: 616.830285

Applied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulation
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050 4 $a RC376.5 $b .L559 2022
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100 1 $a LeMoyne, Robert $q (Robert Charles) $3 931270
245 1 0 $a Applied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulation $h [electronic resource] / $c Robert LeMoyne, Timothy Mastroianni.
246 3 $a Applied software development with Python and machine learning by wearable and wireless systems for movement disorder treatment via deep brain stimulation
260 $a Singapore : $b World Scientific, $c c2022.
300 $a 1 online resource (248 p.)
504 $a Includes bibliographical references and index.
520 $a "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."-- $c Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Movement disorders $x Treatment $x Computer programs. $3 931271
700 1 $a Mastroianni, Timothy. $3 801404
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/12249#t=toc