What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Prosthesis
- Pattern recognition
Artificial intelligence, Pattern recognition, Electromyography, Simulation and Pattern recognition are his primary areas of study.
His research integrates issues of Control system, Speech recognition and Gait analysis in his study of Artificial intelligence.
His Pattern recognition research integrates issues from Controllability and Computer vision.
Levi J. Hargrove studied Electromyography and Linear discriminant analysis that intersect with Binary classification.
His Simulation study combines topics in areas such as Lower limb, Dynamic Bayesian network, Intent recognition and Stairs.
His studies deal with areas such as Robotics, Physical medicine and rehabilitation, Wrist, Control theory and Muscle activation as well as Pattern recognition.
His most cited work include:
- A Comparison of Surface and Intramuscular Myoelectric Signal Classification (399 citations)
- Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (263 citations)
- Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay (253 citations)
What are the main themes of his work throughout his whole career to date?
Levi J. Hargrove spends much of his time researching Artificial intelligence, Electromyography, Pattern recognition, Simulation and Prosthesis.
His work carried out in the field of Artificial intelligence brings together such families of science as Control system and Speech recognition.
The concepts of his Electromyography study are interwoven with issues in Lower limb, Wrist and Forearm.
His Pattern recognition study combines topics from a wide range of disciplines, such as Reinnervation, Controllability and Linear model.
His Simulation study incorporates themes from Stairs, Gait, Control, Torque and Joint.
His research investigates the link between Prosthesis and topics such as Ankle that cross with problems in Physical therapy, Knee Joint, Robotics and Gait analysis.
He most often published in these fields:
- Artificial intelligence (45.69%)
- Electromyography (37.93%)
- Pattern recognition (32.76%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (45.69%)
- Physical medicine and rehabilitation (27.59%)
- Electromyography (37.93%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Physical medicine and rehabilitation, Electromyography, Wearable computer and Simulation.
His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition.
His research in Pattern recognition is mostly focused on Pattern recognition.
His study looks at the relationship between Physical medicine and rehabilitation and fields such as Wrist, as well as how they intersect with chemical problems.
The Electromyography study combines topics in areas such as Stairs and Biomechanics.
His Simulation research incorporates elements of Sensory system, Control, Controller design, Joint and Prosthesis.
Between 2017 and 2021, his most popular works were:
- Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring. (72 citations)
- Online adaptive neural control of a robotic lower limb prosthesis. (38 citations)
- Online adaptive neural control of a robotic lower limb prosthesis. (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Prosthesis
- Surgery
His primary areas of study are Electromyography, Artificial intelligence, Wearable computer, Physical medicine and rehabilitation and Gait.
As part of his studies on Electromyography, Levi J. Hargrove frequently links adjacent subjects like Stairs.
The various areas that Levi J. Hargrove examines in his Artificial intelligence study include Biomedical engineering, Chronic stroke, Computer vision and Pattern recognition.
His work on Linear discriminant analysis as part of general Pattern recognition study is frequently linked to Hemiparesis, bridging the gap between disciplines.
His Upper limb study in the realm of Physical medicine and rehabilitation interacts with subjects such as Correlation.
His work focuses on many connections between Gait and other disciplines, such as Lower limb, that overlap with his field of interest in Benchmark data, Benchmark, Biomechanics, Exoskeleton and Simulation.
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