What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Electrical engineering
His primary areas of study are Artificial intelligence, Wearable computer, Computer vision, Wireless sensor network and Inertial measurement unit.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
He combines subjects such as Key distribution in wireless sensor networks, Real-time computing, Simulation and Signal processing with his study of Wearable computer.
In the field of Computer vision, his study on Sensor fusion overlaps with subjects such as Action recognition, Set and Movement.
His Wireless sensor network research is multidisciplinary, incorporating perspectives in Distributed algorithm, Distributed computing, Postural control and Swing.
His Inertial measurement unit study combines topics in areas such as Control system, Physical medicine and rehabilitation, Balance ability, Balance and Muscle activity.
His most cited work include:
- UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (326 citations)
- Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications (315 citations)
- A survey of depth and inertial sensor fusion for human action recognition (190 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Wearable computer, Artificial intelligence, Wireless sensor network, Computer vision and Real-time computing.
The various areas that he examines in his Wearable computer study include Wireless, Signal, Signal processing and Human–computer interaction.
His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
His study explores the link between Wireless sensor network and topics such as Embedded system that cross with problems in Pattern recognition and Control reconfiguration.
His work in the fields of Computer vision, such as Sensor fusion, Inertial measurement unit and Orientation, intersects with other areas such as Action recognition and Fusion.
His research in Real-time computing intersects with topics in Dynamic time warping and Power optimization.
He most often published in these fields:
- Wearable computer (34.36%)
- Artificial intelligence (29.07%)
- Wireless sensor network (26.87%)
What were the highlights of his more recent work (between 2019-2021)?
- Wearable computer (34.36%)
- Artificial intelligence (29.07%)
- Activity recognition (7.49%)
In recent papers he was focusing on the following fields of study:
Roozbeh Jafari spends much of his time researching Wearable computer, Artificial intelligence, Activity recognition, Robot and Control theory.
His Wearable computer research includes elements of Ambulatory blood pressure, Cardiology, Artificial neural network, Human–computer interaction and Process.
His study in Process is interdisciplinary in nature, drawing from both Transfer of learning and Signal processing.
His Artificial intelligence research focuses on Deep learning in particular.
His studies deal with areas such as Linear-quadratic regulator, PID controller and Control theory as well as Robot.
His Feature extraction study which covers Segmentation that intersects with Signal.
Between 2019 and 2021, his most popular works were:
- Personalizing Activity Recognition Models Through Quantifying Different Types of Uncertainty Using Wearable Sensors (9 citations)
- ImpediBands: Body Coupled Bio-Impedance Patches for Physiological Sensing Proof of Concept (7 citations)
- Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Electrical engineering
His scientific interests lie mostly in Artificial intelligence, Wearable computer, Robot, Control theory and Control theory.
In his study, which falls under the umbrella issue of Artificial intelligence, Interbeat interval, Cardiac monitoring, Artifact and Photoplethysmogram is strongly linked to Pattern recognition.
His Wearable computer research is multidisciplinary, incorporating elements of Signal reconstruction, Vital signs, Blind signal separation and Machine learning, Activity recognition.
His studies in Activity recognition integrate themes in fields like Feature extraction, Deep learning, Convolutional neural network and Usability.
His Robot research incorporates elements of Linear-quadratic regulator, Oscillation and Angular velocity.
His work in the fields of PID controller overlaps with other areas such as Modular design and Pipeline transport.
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