Projects

B Soran, L Lowes, KM Steele (2016) “Evaluation of infants with spinal muscular atrophy using convolutional neural networks.” European Conference on Computer Vision

Dr. Steele JournalArticle, UbiquitousRehabilitation , , ,
Experimental set-up with infant positioned below Kinect depth camera.

Peer-reviewed paper at European Conference on Computer Vision:

30-second videos from a depth camera can be used in the evaluation of infants with spinal muscular atrophy.

Experimental set-up with infant positioned below Kinect depth camera.Abstract: Spinal Muscular Atrophy is the most common genetic cause of infant death. Due to its severity, there is a need for methods for automated estimation of disease progression. In this paper we propose a Convolutional-Neural-Network (CNN) model to estimate disease progression during infants’ natural behavior. With the proposed methodology, we were able to predict each child’s score on current behavior-based clinical exams with an average per-subject error of 6.96 out of 72 points (<10 % difference), using 30-second videos in leave-one-subject-out-cross-validation setting. When simple statistics were used over 30-second video-segments to estimate a score for longer videos, we obtained an average error of 5.95 (∼8 % error rate). By showing promising results on a small dataset (N = 70, 2-minute samples, which were handled as 1487, 30-second video segments), our methodology demonstrates that it is possible to benefit from CNNs on small datasets by proper design and data handling choices.

Jessica Zistatsis awarded Mechanical Engineering Fellowship

Keshia Awards, News, Orthoses, Projects

jessicazCongratulations to Jessica for being acknowledged by the Department of Mechanical Engineering for her academic achievements and potential for success within her masters studies.

Jessica is dedicated to creating a pediatric exoskeleton which promotes improved walking patterns during daily life, outside of therapy sessions. This fellowship will allow Jessica to devote more time towards her research and studies. Congrats!

 

UW Together – Featured Project

Keshia Media, News, Orthoses, Projects

Here at the Ability & Innovation Lab we are fortunate to partner with amazing families and people who are our user experts for feedback and ideas when creating new devices and designs. Jayna and her family are fantastic partners in the design project for Jayna, alongside our undergraduate students. The second prototype is now underway to improve the comfort, donning and doffing, and applicability of Jayna’s elbow-driven device to enable the use of her left arm during two handed tasks.

UW Together presents Jayna’s story HERE.

Jayna and Bradley work on bi-manual tasks (two-handed) during Jayna's visit to the Ability and Innovation Lab

Can Technology Make a Difference in Pediatric Rehabilitation? – A NCMRR Webcast

Keshia AccessEngineering, News, Outreach, Projects, UbiquitousRehabilitation

Interested in how technology can be used to make a difference in pediatric rehabilitation? A video cast from the National Center for Medical Rehabilitation Research (NCMRR) discusses the topic in Bethesda MD. The workshop is organized by the Motion Analysis Laboratory and supported by the National Science Foundation and the National Institutes of Health.

The workshop on August 9th, 2016 brought together a group of experts in rehabilitation to discuss how technology can help us to address pressing needs in pediatric rehabilitation. To follow all of the talks this past week and listen to “Can Technology Make a Difference in Pediatric Rehabilitation?”, follow this link, CLICK HERE.

J Wu, BR Shuman, BW Brunton, KM Steele, JD Olson, RPN Rao (2016) “Multistep model for predicting upper-limb 3D isometric force application from pre-movement electrocorticographic features.” IEEE Engineering Medicine & Biology

Dr. Steele JournalArticle, MotorControl ,
Example of ECoG recording during upper-extremity force production.

Peer-reviewed paper at IEEE Engineering in Medicine & Biology Annual Conference:

Can we estimate upper-extremity force production from electrocorticographic recordings?

Example of ECoG recording during upper-extremity force production.Abstract: Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning.