Daniel K. Zondervan
University of California, Irvine
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Featured researches published by Daniel K. Zondervan.
Neurorehabilitation and Neural Repair | 2015
Daniel K. Zondervan; Renee Augsburger; Barbara Bodenhoefer; Nizan Friedman; David J. Reinkensmeyer; Steven C. Cramer
Background. Few therapeutic options exist for the millions of persons living with severe arm impairment after stroke to increase their dose of arm rehabilitation. This study compared self-guided, high-repetition home therapy with a mechanical device (the resonating arm exerciser [RAE]) to conventional therapy in patients with chronic stroke and explored RAE use for patients with subacute stroke. Methods. A total of 16 participants with severe upper-extremity impairment (mean Fugl-Meyer [FM] score = 21.4 ± 8.8 out of 66) >6 months poststroke were randomized to 3 weeks of exercise with the RAE or conventional exercises. The primary outcome measure was FM score 1 month posttherapy. Secondary outcome measures included Motor Activity Log, Visual Analog Pain Scale, and Ashworth Spasticity Scale. After a 1-month break, individuals in the conventional group also received a 3-week course of RAE therapy. Results. The change in FM score was significant in both the RAE and conventional groups after training (2.6 ± 1.4 and 3.4 ± 2.4, P = .008 and .016, respectively). These improvements were not significant at 1 month. Exercise with the RAE led to significantly greater improvements in distal FM score than conventional therapy at the 1-month follow-up (P = .02). In a separate cohort of patients with subacute stroke, the RAE was found feasible for exercise. Discussion. In those with severe arm impairment after chronic stroke, home-based training with the RAE was feasible and significantly reduced impairment without increasing pain or spasticity. Gains with the RAE were comparable to those found with conventional training and also included distal arm improvement.
Journal of Neuroengineering and Rehabilitation | 2013
Daniel K. Zondervan; Lorena Palafox; Jorge Hernández; David J. Reinkensmeyer
BackgroundRobotic arm therapy devices that incorporate actuated assistance can enhance arm recovery, motivate patients to practice, and allow therapists to deliver semi-autonomous training. However, because such devices are often complex and actively apply forces, they have not achieved widespread use in rehabilitation clinics or at home. This paper describes the design and pilot testing of a simple, mechanically passive device that provides robot-like assistance for active arm training using the principle of mechanical resonance.MethodsThe Resonating Arm Exerciser (RAE) consists of a lever that attaches to the push rim of a wheelchair, a forearm support, and an elastic band that stores energy. Patients push and pull on the lever to roll the wheelchair back and forth by about 20 cm around a neutral position. We performed two separate pilot studies of the device. In the first, we tested whether the predicted resonant properties of RAE amplified a user’s arm mobility by comparing his or her active range of motion (AROM) in the device achieved during a single, sustained push and pull to the AROM achieved during rocking. In a second pilot study designed to test the therapeutic potential of the device, eight participants with chronic stroke (35 ± 24 months since injury) and a mean, stable, initial upper extremity Fugl-Meyer (FM) score of 17 ± 8 / 66 exercised with RAE for eight 45 minute sessions over three weeks. The primary outcome measure was the average AROM measured with a tilt sensor during a one minute test, and the secondary outcome measures were the FM score and the visual analog scale for arm pain.ResultsIn the first pilot study, we found people with a severe motor impairment after stroke intuitively found the resonant frequency of the chair, and the mechanical resonance of RAE amplified their arm AROM by a factor of about 2. In the second pilot study, AROM increased by 66% ± 20% (p = 0.003). The mean FM score increase was 8.5 ± 4 pts (p = 0.009). Subjects did not report discomfort or an increase in arm pain with rocking. Improvements were sustained at three months.ConclusionsThese results demonstrate that a simple mechanical device that snaps onto a manual wheelchair can use resonance to assist arm training, and that such training shows potential for safely increasing arm movement ability for people with severe chronic hemiparetic stroke.
ieee international conference on rehabilitation robotics | 2013
Daniel K. Zondervan; Brendan W. Smith; David J. Reinkensmeyer
People with severe arm impairment have limited technologies available for retraining their arms, and, if they also have difficulty walking, they often cannot effectively use a manual wheelchair because they cannot grasp and push the pushrim. We are using Lever-Actuated Resonance Assistance (LARA) to solve these problems. A LARA-based device can attach to a manual wheelchair and allow it to be used by people with severe arm weakness in a stationary exercise mode, or for self-powered overground ambulation. LARA uses a lever drive and arm support to appropriately position the arm and to reduce the dexterity required to operate the wheelchair. It also uses mechanical resonance implemented with elastic bands to provide assistance for both stationary exercise and overground ambulation. We first review here pilot results in which we used the LARA method to provide arm therapy to individuals with chronic stroke in stationary exercise mode. We then describe a novel motion-based user interface that allows individuals to control a video game with LARA while operating a wheelchair in resonance. Finally, for overground ambulation mode, we show in simulation that the mechanical resonance provided by LARA theoretically allows people with severe arm weakness to propel themselves with reduced effort and obtain speeds previously unattainable.
Assistive Technology | 2015
Daniel K. Zondervan; Riccardo Secoli; Aurelia Mclaughlin Darling; John Farris; Jan Furumasu; David J. Reinkensmeyer
Background: Children with severe disabilities are sometimes unable to access powered mobility training. Thus, we developed the Kinect-Wheelchair Interface Controlled (KWIC) smart wheelchair trainer that converts a manual wheelchair into a powered wheelchair. The KWIC Trainer uses computer vision to create a virtual tether with adaptive shared-control between the wheelchair and a therapist during training. It also includes a mixed-reality video game system. Methods: We performed a year-long usability study of the KWIC Trainer at a local clinic, soliciting qualitative and quantitative feedback on the device after extended use. Results: Eight therapists used the KWIC Trainer for over 50 hours with 8 different children. Two of the children obtained their own powered wheelchair as a result of the training. The therapists indicated the device allowed them to provide mobility training for more children than would have been possible with a demo wheelchair, and they found use of the device to be as safe as or safer than conventional training. They viewed the shared control algorithm as counter-productive because it made it difficult for the child to discern when he or she was controlling the chair. They were enthusiastic about the video game integration for increasing motivation and engagement during training. They emphasized the need for additional access methods for controlling the device. Conclusion: The therapists confirmed that the KWIC Trainer is a useful tool for increasing access to powered mobility training and for engaging children during training sessions. However, some improvements would enhance its applicability for routine clinical use.
international conference of the ieee engineering in medicine and biology society | 2014
Brendan W. Smith; Daniel K. Zondervan; Thomas J. Lord; Vicky Chan; David J. Reinkensmeyer
Individuals with severe arm impairment after stroke are thought to be unable to use a manual wheelchair in the conventional bimanual fashion, because they cannot grip and push the pushrim with their impaired hand. Instead, they are often taught to propel a wheelchair with their good arm and leg, a compensatory strategy that encourages disuse and may cause asymmetric tone. Here, we show that four stroke survivors (9, 27 50 and 16 months post stroke) with severe arm impairment (upper extremity Fugl Meyer scores of 21, 17, 16 and 15 of 66 respectively) were able to propel themselves overground during ten, 3.3 meter movement trials, using a specially designed lever-driven wheelchair adapted with a splint and elastic bands. Their average speed on the tenth trial was about 0.1 m/sec. These results suggest that individuals with stroke could use bimanual wheelchair propulsion for mobility, both avoiding the problems associated with good-arm/good-leg propulsion and increasing the number of daily arm movements they achieve, which may improve arm movement recovery.
Archive | 2016
David J. Reinkensmeyer; Daniel K. Zondervan
We describe the development of the spring-based orthosis approach (as exemplified by T-WREX and ArmeoSpring) to enhance upper-extremity movement therapy after neurologic injury. This approach is based around the concept of using springs to assist a patient in moving his or her weakened arm as he or she practices computerized movement tasks. This chapter first traces the development of spring orthoses for arm therapy within the context of the development of robot-assisted therapy. Then, this chapter evaluates the spring orthosis approach in light of recent evidence concerning the role of mechanical assistance, functional exercise, and computer gaming in promoting upper-extremity movement recovery after stroke. This evidence suggests a path forward toward simplified spring-based orthoses for home use. As an example, we discuss the design and initial testing of a simple lever-based spring orthosis, the Resonating Arm Exerciser.
Journal of Rehabilitation Research and Development | 2016
Daniel K. Zondervan; Nizan Friedman; Enoch H. Chang; Xing Zhao; Renee Augsburger; David J. Reinkensmeyer; Steven C. Cramer
Studies in health technology and informatics | 2012
Riccardo Secoli; Daniel K. Zondervan; David J. Reinkensmeyer
Experimental Brain Research | 2014
Daniel K. Zondervan; Jaime E. Duarte; Justin B. Rowe; David J. Reinkensmeyer
Archive | 2015
Nizan Friedman; Daniel K. Zondervan; David J. Reinkensmayer; Mark Bachman