What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Artificial neural network
Artificial intelligence, Signal processing, Proportional myoelectric control, Signal and Artificial neural network are his primary areas of study.
Much of his study explores Artificial intelligence relationship to Control system.
His work is dedicated to discovering how Signal processing, Myoelectric signal are connected with Control engineering, Control, Artificial limbs and Prosthesis and other disciplines.
His Proportional myoelectric control research focuses on subjects like Upper limb, which are linked to Wrist, Biomechanics, Isometric exercise and Physical therapy.
His study in Signal is interdisciplinary in nature, drawing from both Series, Time delay neural network, Hopfield network, Electromyography and Perceptron.
His Artificial neural network research incorporates elements of Speech recognition, Cluster analysis and Pattern recognition.
His most cited work include:
- A new strategy for multifunction myoelectric control (1413 citations)
- Electromyography. Physiology, engineering and non invasive applications (852 citations)
- A wavelet-based continuous classification scheme for multifunction myoelectric control (546 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Signal processing, Signal, Artificial intelligence, Myoelectric signal and Electronic engineering.
His Signal processing course of study focuses on Control theory and Signal-to-noise ratio and Electromyography.
His work on Noise as part of general Signal research is frequently linked to Efferent, bridging the gap between disciplines.
In his study, Multilayer perceptron is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence.
His Myoelectric signal research incorporates themes from Human physiology, Biomedical engineering, Artificial limbs and Anatomy.
Philip A. Parker has included themes like Proportional myoelectric control, Speech recognition and Target acquisition in his Artificial neural network study.
He most often published in these fields:
- Signal processing (25.61%)
- Signal (23.17%)
- Artificial intelligence (21.95%)
What were the highlights of his more recent work (between 2008-2014)?
- Artificial intelligence (21.95%)
- Artificial neural network (13.41%)
- Proportional myoelectric control (10.98%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Artificial neural network, Proportional myoelectric control, Signal and Speech recognition.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in State, Myoelectric signal and Pattern recognition.
His Artificial neural network research includes themes of Electromyography and Target acquisition.
The Proportional myoelectric control study combines topics in areas such as Upper limb and Isometric exercise.
His work in the fields of Signal, such as Signal processing, overlaps with other areas such as Efferent.
His work deals with themes such as Electronic engineering, Bandwidth, Amplifier and CAN bus, which intersect with Signal processing.
Between 2008 and 2014, his most popular works were:
- Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal (306 citations)
- Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review (282 citations)
- Simultaneous and Proportional Force Estimation for Multifunction Myoelectric Prostheses Using Mirrored Bilateral Training (171 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Electrical engineering
Philip A. Parker spends much of his time researching Proportional myoelectric control, Upper limb, Control system, Signal and Speech recognition.
The study incorporates disciplines such as Prosthesis, Artificial intelligence, Pattern recognition and Electromyography in addition to Proportional myoelectric control.
Philip A. Parker interconnects Training set and Human–computer interaction in the investigation of issues within Prosthesis.
His Pattern recognition research is within the category of Pattern recognition.
His Electromyography research integrates issues from Physical therapy, Isometric exercise, Wrist and Biomechanics.
Speech recognition is often connected to Artificial neural network in his work.
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