What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Her main research concerns Artificial intelligence, Computer vision, Aging in place, Activities of daily living and Gait.
Her Artificial intelligence study frequently involves adjacent topics like Pattern recognition.
Her Computer vision research includes themes of Event, Cross-validation and Time series.
The study incorporates disciplines such as Retirement community, Registered nurse, Home automation, Medical emergency and Geriatrics in addition to Aging in place.
Marjorie Skubic works mostly in the field of Activities of daily living, limiting it down to concerns involving Nursing and, occasionally, Housing for the Elderly and Home health.
Her Gait analysis study in the realm of Gait connects with subjects such as Work and Fall risk assessment.
Her most cited work include:
- Older adults' attitudes towards and perceptions of ‘smart home’ technologies: a pilot study (469 citations)
- Fall Detection in Homes of Older Adults Using the Microsoft Kinect (325 citations)
- Senior residents' perceived need of and preferences for "smart home" sensor technologies. (224 citations)
What are the main themes of her work throughout her whole career to date?
Marjorie Skubic mostly deals with Artificial intelligence, Computer vision, Robot, Human–computer interaction and Gait.
Her studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition.
Much of her study explores Computer vision relationship to Computer graphics.
Her Robot research includes elements of Object and Spatial relation.
The Human–computer interaction study combines topics in areas such as Natural language and Human–robot interaction.
Her Gait research integrates issues from Preferred walking speed, Physical therapy and STRIDE.
She most often published in these fields:
- Artificial intelligence (41.99%)
- Computer vision (24.20%)
- Robot (13.17%)
What were the highlights of her more recent work (between 2016-2021)?
- Physical medicine and rehabilitation (8.19%)
- Artificial intelligence (41.99%)
- Rehabilitation (4.27%)
In recent papers she was focusing on the following fields of study:
Marjorie Skubic mainly investigates Physical medicine and rehabilitation, Artificial intelligence, Rehabilitation, Pattern recognition and Human–computer interaction.
Many of her research projects under Physical medicine and rehabilitation are closely connected to Vestibular system with Vestibular system, tying the diverse disciplines of science together.
Her research in Artificial intelligence intersects with topics in Ballistocardiography, Probability density function and Computer vision.
The concepts of her Computer vision study are interwoven with issues in Radio wave, Simulation and Correlation coefficient.
Her research integrates issues of Data stream, Big data and Heart rate in her study of Pattern recognition.
Her work carried out in the field of Human–computer interaction brings together such families of science as Classification result, Echo and Server.
Between 2016 and 2021, her most popular works were:
- Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls. (24 citations)
- Comparison of 3D Joint Angles Measured With the Kinect 2.0 Skeletal Tracker Versus a Marker-Based Motion Capture System (23 citations)
- Validation of a Kinect V2 based rehabilitation game. (17 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Her main research concerns Simulation, Physical medicine and rehabilitation, Artificial intelligence, Motion capture and Gait.
Marjorie Skubic interconnects Noise, Doppler radar and Elderly care in the investigation of issues within Simulation.
Marjorie Skubic has included themes like Video game, Range, Computer vision and Pattern recognition in her Artificial intelligence study.
Her Computer vision research is multidisciplinary, relying on both Functional movement, Root-mean-square deviation, Trajectory and Pelvis.
Her Motion capture study also includes
- Correlation coefficient which is related to area like Correlation, Signal-to-noise ratio and Standard error,
- Rehabilitation, which have a strong connection to Activity recognition, Activities of daily living and Stroke patient.
Her Gait research is multidisciplinary, incorporating perspectives in Gait speed, Independent living and Stride length.
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