What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Cognition
Her primary areas of investigation include Human–computer interaction, Cognitive psychology, Affect, Artificial intelligence and Body movement.
The concepts of her Human–computer interaction study are interwoven with issues in Multimedia, Expression and Cognitive science.
Her Cognitive psychology study combines topics from a wide range of disciplines, such as Modality, Perception and Human communication.
Her Affect research includes themes of Facial expression and Pattern recognition.
Artificial intelligence is closely attributed to Machine learning in her research.
Her study looks at the intersection of Sonification and topics like Rehabilitation with Physical medicine and rehabilitation and Chronic pain.
Her most cited work include:
- Affective Body Expression Perception and Recognition: A Survey (371 citations)
- Does Body Movement Engage You More in Digital Game Play? and Why? (217 citations)
- Understanding the role of body movement in player engagement (166 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Human–computer interaction, Artificial intelligence, Cognitive psychology, Body movement and Perception.
Nadia Bianchi-Berthouze has included themes like Multimedia and Gesture in her Human–computer interaction study.
Her work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision.
The various areas that Nadia Bianchi-Berthouze examines in her Cognitive psychology study include Context, Affect, Movement, Modality and Expression.
Her work in Affect addresses issues such as Affective computing, which are connected to fields such as Speech recognition.
In Chronic pain, she works on issues like Electromyography, which are connected to Rehabilitation.
She most often published in these fields:
- Human–computer interaction (26.05%)
- Artificial intelligence (25.58%)
- Cognitive psychology (19.07%)
What were the highlights of her more recent work (between 2018-2021)?
- Chronic pain (14.88%)
- Artificial intelligence (25.58%)
- Movement (6.05%)
In recent papers she was focusing on the following fields of study:
Nadia Bianchi-Berthouze focuses on Chronic pain, Artificial intelligence, Movement, Cognitive psychology and Rehabilitation.
Her Chronic pain research incorporates elements of Intervention, Electromyography, Deep learning and Human–computer interaction.
Her Human–computer interaction research is multidisciplinary, incorporating perspectives in Affect and Identification.
Her research integrates issues of Global Workspace Theory and Machine learning in her study of Artificial intelligence.
Nadia Bianchi-Berthouze studied Cognitive psychology and Sonification that intersect with Perception and Design technology.
Her studies deal with areas such as Classifier, Facial expression and Physical medicine and rehabilitation as well as Rehabilitation.
Between 2018 and 2021, her most popular works were:
- How Can Affect Be Detected and Represented in Technological Support for Physical Rehabilitation (24 citations)
- Recurrent network based automatic detection of chronic pain protective behavior using MoCap and sEMG data (18 citations)
- Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging (16 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Cognition
- Machine learning
Chronic pain, Rehabilitation, Physical medicine and rehabilitation, Wearable technology and Artificial intelligence are her primary areas of study.
Her Chronic pain research is multidisciplinary, relying on both Intervention and Human–computer interaction.
Her Rehabilitation study integrates concerns from other disciplines, such as Classifier, Inference, Support vector machine and Personalization.
Her research in Physical medicine and rehabilitation intersects with topics in Virtual rehabilitation and Random forest.
Her Deep learning, Motion capture, Artificial neural network and Feature study in the realm of Artificial intelligence connects with subjects such as Workload.
Her Deep learning study incorporates themes from Cognitive psychology, Movement, Activities of daily living, Variety and Electromyography.
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