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008 211002s2021 si o 000 0 eng d
040 _aWSPC
_beng
_cWSPC
020 _a9789811235962
_q(ebook)
020 _a9811235961
_q(ebook)
020 _z9789811235955
_q(hbk.)
020 _z9811235953
_q(hbk.)
050 4 _aRC376.5
_b.L559 2021
072 7 _aCOM
_x051000
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072 7 _aCOM
_x044000
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082 0 4 _a616.830285
_223
049 _aMAIN
100 1 _aLeMoyne, Robert
_q(Robert Charles)
_9178494
245 1 0 _aApplied software development with Python & machine learning by wearable & wireless systems for movement disorder treatment via deep brain stimulation
_h[electronic resource] /
_cRobert LeMoyne, Timothy Mastroianni.
246 3 _aApplied software development with Python and machine learning by wearable and wireless systems for movement disorder treatment via deep brain stimulation
260 _aSingapore :
_bWorld Scientific,
_c2021.
300 _a1 online resource (248 p.)
505 0 _aIntroduction -- General concept of preliminary network centric therapy applying deep brain stimulation for ameliorating movement disorders with machine learning classification using Python based on feedback from a smartphone as a wearable and wireless system -- Global algorithm development -- Incremental software development using Python -- Automation of feature set extraction using Python -- Waikato environment for knowledge analysis (WEKA) a perspective consideration of multiple machine learning classification algorithms and applications -- Machine learning classification of essential tremor using a reach and grasp task with deep brain stimulation system set to 'on' and 'off' status -- Advanced concepts.
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."--
_cPublisher's website.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
650 0 _aMovement disorders
_xTreatment
_xComputer programs.
_9178495
655 0 _aElectronic books.
_93294
700 1 _aMastroianni, Timothy.
_9178496
856 4 0 _uhttps://www.worldscientific.com/worldscibooks/10.1142/12249#t=toc
_zAccess to full text is restricted to subscribers.
942 _cEBK
999 _c97799
_d97799