1. Nicole Lehrer
  2. http://www.igert.org/profiles/1515
  3. Graduate Student
  4. Presenter’s IGERT
  5. Arizona State University
  1. Michael Baran
  2. http://www.igert.org/profiles/4766
  3. Graduate Student
  4. Presenter’s IGERT
  5. Arizona State University
Judges’ Queries and Presenter’s Replies
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Presentation Discussion
  • Icon for: Rena Stroud

    Rena Stroud

    Project Staff
    May 22, 2012 | 02:42 p.m.

    Very interesting and important work! Reading through your poster I noticed that the home system tracks significantly fewer kinematic features than the clinical system. Is the home-based system meant to supplement therapy in the clinical setting, or is it a replacement?

  • Icon for: Nicole Lehrer

    Nicole Lehrer

    Lead Presenter
    May 24, 2012 | 04:04 a.m.

    Thank you for your comment and question. Yes, the home system primarily focuses on monitoring movement of the hand (end-point) over time and space, which can be tracked reliably by using three cameras. We also use lower cost pressure-sensing solutions embedded within the chair and tangible objects to provide coarse measures of torso movement and object manipulation by the hand.

    The home system is designed as a long-term extension of clinical therapy, where the participant receives a home system following their treatment within the clinic. To provide interactive training within an unsupervised home environment, we have selected a subset of the full kinematics that we monitor in the clinic, of which we can reliably track in real-time within the home setting and provide feedback on performance. From this starting point we are exploring the extent to which we can effectively evaluate and characterize the patient’s movement performance over time based on measuring these smaller set of features. Ongoing work includes research into capturing other kinematic attributes, such as elbow extension, using inexpensive off the shelf technologies such as the Kinect.

  • Icon for: Elizabeth Torres

    Elizabeth Torres

    Faculty: Project Co-PI
    May 23, 2012 | 09:55 a.m.

    Very impressive team of artists and movement rehab fellows. I see that you are monitoring what the patient voluntarily controls in these goal-directed motions. This is a very important part of the action and the kinematics and timing of these movement trajectories are indeed rich in information that can be tracked over time as you are doing here. Have you thought of tracking the retracting motions as well? I find in my research with stroke patients, de-afferented patients and patients with Parkinson’s disease that these movements while neglected by our research community are actually more informative of the progress/deficits than the forwards segments. They amplify what is wrong with the sensing of the movements and also highlight what gets better. Just curious…
    Your team has done an excellent excellent work!!! Best of luck in the competition -Liz Torres

  • Icon for: Nicole Lehrer

    Nicole Lehrer

    Lead Presenter
    May 24, 2012 | 09:00 a.m.

    Thank you for your question and comment, that’s very interesting. We do track and record the return movement, but at this time do not analyze or provide real-time feedback on its quality. Exploring the quality of the return is certainly something that can be analyzed, and eventually integrated into our evaluation and feedback in the future.

    We do however provide basic feedback that helps guide the participant in returning to a consistent rest zone, prior to initiating another reaching movement. This is in order to encourage the participant to begin movement with a correct and consistent initial posture (set by the therapist).

    In the hospital system, we previously focused on measuring movement in the context of completing a single action and not past the point its goal achievement. But in the home system, we are beginning to track movement with multiple phases by providing a new training scenario in which the participant must complete a sequence of tasks, where he reaches to a target, picks the target up, and transfers it to a new location. We could also potentially integrate a task where the person must transport an object back towards their body. But as you suggest, we could certainly look at the quality of their movement in a return following a single action since we will be capturing this data as well.

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