H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 38 Citations 6,921 198 World Ranking 2769 National Ranking 1113

Research.com Recognitions

Awards & Achievements

2018 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Older adults' attitudes towards and perceptions of ‘smart home’ technologies: a pilot study

George Demiris;Marilyn J. Rantz;Myra A. Aud;Karen D. Marek.
Medical Informatics and The Internet in Medicine (2004)

746 Citations

Fall Detection in Homes of Older Adults Using the Microsoft Kinect

Erik E. Stone;Marjorie Skubic.
IEEE Journal of Biomedical and Health Informatics (2015)

465 Citations

Senior residents' perceived need of and preferences for "smart home" sensor technologies.

George Demiris;Brian K. Hensel;Marjorie Skubic;Marilyn Rantz.
International Journal of Technology Assessment in Health Care (2008)

384 Citations

Spatial language for human-robot dialogs

M. Skubic;D. Perzanowski;S. Blisard;A. Schultz.
systems man and cybernetics (2004)

297 Citations

A smart home application to eldercare: Current status and lessons learned

Marjorie Skubic;Gregory Alexander;Mihail Popescu;Marilyn Rantz.
Technology and Health Care (2009)

281 Citations

Linguistic summarization of video for fall detection using voxel person and fuzzy logic

Derek Anderson;Robert H. Luke;James M. Keller;Marjorie Skubic.
Computer Vision and Image Understanding (2009)

269 Citations

Recognizing falls from silhouettes.

Derek Anderson;James M. Keller;Marjorie Skubic;Xi Chen.
international conference of the ieee engineering in medicine and biology society (2006)

250 Citations

Histogram of oriented normal vectors for object recognition with a depth sensor

Shuai Tang;Xiaoyu Wang;Xutao Lv;Tony X. Han.
asian conference on computer vision (2012)

241 Citations

Needing smart home technologies: the perspectives of older adults in continuing care retirement communities.

Karen L Courtney;George Demiris;Marilyn Rantz;Marjorie Skubic.
Journal of innovation in health informatics (2008)

218 Citations

An acoustic fall detector system that uses sound height information to reduce the false alarm rate

Mihail Popescu;Yun Li;Marjorie Skubic;Marilyn Rantz.
international conference of the ieee engineering in medicine and biology society (2008)

214 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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