What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Electrical engineering
- Operating system
His primary areas of investigation include Wireless sensor network, Microsystem, Chip, System on a chip and Electrical engineering.
His studies in Wireless sensor network integrate themes in fields like Torso, Wearable technology, Wearable computer and Simulation.
His work in Microsystem covers topics such as Instrumentation which are related to areas like Digestive tract, Remote patient monitoring and Application-specific integrated circuit.
The Chip study combines topics in areas such as Microelectronics and Integrated circuit.
His research on Electrical engineering often connects related topics like Heart rate measurement.
The concepts of his Transmission study are interwoven with issues in Electronic engineering and Signal.
His most cited work include:
- Multichannel Reflective PPG Earpiece Sensor With Passive Motion Cancellation (132 citations)
- Sensing device, apparatus and system, and method for operating the same (125 citations)
- Heart rate measurement (113 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Electronic engineering, Electrical engineering and Robot are his primary areas of study.
His work carried out in the field of Artificial intelligence brings together such families of science as Frame, Machine learning and Pattern recognition.
His Electronic engineering study often links to related topics such as Signal.
Chip and Instrumentation is closely connected to Microsystem in his research, which is encompassed under the umbrella topic of Electrical engineering.
His Chip study incorporates themes from System on a chip and Embedded system.
His research in Robot intersects with topics in Control engineering and Control system.
He most often published in these fields:
- Artificial intelligence (19.50%)
- Computer vision (13.50%)
- Electronic engineering (12.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (19.50%)
- Physical medicine and rehabilitation (6.50%)
- Efficient energy use (2.50%)
In recent papers he was focusing on the following fields of study:
Lei Wang mostly deals with Artificial intelligence, Physical medicine and rehabilitation, Efficient energy use, Catheter and Wearable computer.
His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence.
His work in the fields of Physical medicine and rehabilitation, such as Electromyography, overlaps with other areas such as Modalities.
Lei Wang interconnects Stenosis, Robot, Percutaneous and Cardiac interventions in the investigation of issues within Catheter.
His Wearable computer research incorporates themes from Vital sign monitoring and Response time.
Lei Wang performs multidisciplinary study on Contact force and Electronic engineering in his works.
Between 2018 and 2021, his most popular works were:
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. (28 citations)
- Microchannel Structural Design For a Room-Temperature Liquid Metal Based Super-stretchable Sensor. (25 citations)
- Highly stretchable sensors for wearable biomedical applications (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Electrical engineering
- Operating system
The scientist’s investigation covers issues in Wearable computer, Strain gauge, Acoustics, Tactile sensor and Electronic skin.
The various areas that Lei Wang examines in his Wearable computer study include Vital sign monitoring and Systems engineering.
His Strain gauge research is multidisciplinary, incorporating perspectives in Soft robotics, Grinding and Haptic perception.
His Acoustics research includes themes of Silver particles, Modulation, Electric power transmission, Bending and Realization.
His Tactile sensor study combines topics from a wide range of disciplines, such as Electrical conductor, Piezoresistive effect, Composite number and Carbon nanotube.
The study incorporates disciplines such as Mechanical engineering, Wearable technology, Response time and Sensitivity in addition to Electronic skin.
This overview was generated by a machine learning system which analysed the scientist’s
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