Ronaldus Maria Aarts

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Quantum mechanics
• Internal medicine

Ronaldus Maria Aarts focuses on Speech recognition, Artificial intelligence, Acoustics, Signal and Sleep Stages. As a member of one scientific family, he mostly works in the field of Speech recognition, focusing on Cohen's kappa and, on occasion, Slow-wave sleep. His Artificial intelligence research incorporates themes from Respiratory rate, Computer vision and Pattern recognition.

His Acoustics study combines topics in areas such as Audio signal processing, Audio signal flow, Audio signal and Microphone. Ronaldus Maria Aarts interconnects Motion sensors, Position sensor and Collar in the investigation of issues within Signal. The concepts of his Sleep Stages study are interwoven with issues in Cardiology, Internal medicine and Non-rapid eye movement sleep.

His most cited work include:

• A survey of stimulation methods used in SSVEP-based BCIs (403 citations)
• Single-accelerometer-based daily physical activity classification (170 citations)
• The acoustics of snoring (154 citations)

What are the main themes of his work throughout his whole career to date?

Ronaldus Maria Aarts spends much of his time researching Acoustics, Signal, Artificial intelligence, Speech recognition and Loudspeaker. His Acoustics research includes elements of Electronic engineering, Audio frequency and Audio signal. The study incorporates disciplines such as Electroencephalography, Computer vision and Pattern recognition in addition to Artificial intelligence.

His research integrates issues of Actigraphy, Slow-wave sleep, Linear discriminant analysis and Sleep Stages, Polysomnography in his study of Speech recognition. His Polysomnography study combines topics from a wide range of disciplines, such as Heart rate variability and Non-rapid eye movement sleep. His Loudspeaker research includes themes of Sound recording and reproduction and Active listening.

He most often published in these fields:

• Acoustics (28.36%)
• Signal (22.27%)
• Artificial intelligence (17.86%)

What were the highlights of his more recent work (between 2016-2021)?

• Artificial intelligence (17.86%)
• Electroencephalography (6.93%)
• Photoplethysmogram (11.55%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Artificial intelligence, Electroencephalography, Photoplethysmogram, Cardiology and Internal medicine. His work deals with themes such as Signal, Computer vision and Pattern recognition, which intersect with Artificial intelligence. Ronaldus Maria Aarts has researched Electroencephalography in several fields, including Support vector machine, Audiology and Brain development.

His study focuses on the intersection of Audiology and fields such as Cardiorespiratory fitness with connections in the field of Obstructive sleep apnea and Speech recognition. His studies deal with areas such as Blood pressure and Accelerometer as well as Photoplethysmogram. His Heart rate variability study integrates concerns from other disciplines, such as Sleep Stages and Non-rapid eye movement sleep.

Between 2016 and 2021, his most popular works were:

• Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults. (47 citations)
• Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults. (47 citations)
• Unobtrusive sleep state measurements in preterm infants - A review. (45 citations)

In his most recent research, the most cited papers focused on:

• Quantum mechanics
• Artificial intelligence
• Internal medicine

Ronaldus Maria Aarts mostly deals with Polysomnography, Photoplethysmogram, Heart rate variability, Heart rate and Blood pressure. Ronaldus Maria Aarts works on Polysomnography which deals in particular with Sleep Stages. His research investigates the connection between Photoplethysmogram and topics such as Accelerometer that intersect with problems in Filter, Algorithm, Signal and Periodic function.

His biological study spans a wide range of topics, including Artificial neural network and Artificial intelligence. His Artificial intelligence research integrates issues from Gold standard, Machine learning, Sleep architecture and Pattern recognition. His work carried out in the field of Speech recognition brings together such families of science as Slow-wave sleep, Linear discriminant analysis, Cohen's kappa and Standard deviation.