Biomedical subjects

David J Reinkensmeyer

Publications and source records attributed to David J Reinkensmeyer.

23 records · Page 2Linked to original sources

PubMed2002

Using robotics to teach the spinal cord to walk.

We have developed a robotic device (e.g. the rat stepper) that can be used to impose programmed forces on the hindlimbs of rats during stepping. In the present paper we describe initial experiments using this robotic device to determine the feasibility of robotically assisted locomotor training in complete spinally transected adult rats. The present results show that using the robots to increase the amount of load during stance by applying a downward force on the ankle improved lift during swing. The trajectory pattern during swing was also improved when the robot arms were programmed to move the ankle in a path that approximated the normal swing trajectory. These results suggest that critical elements for successful training of hindlimb stepping in spinal cord injured rats can be implemented rigorously and evaluated using the rat stepper.

Animals↗

PubMed2002

Alterations in reaching after stroke and their relation to movement direction and impairment severity.

OBJECTIVES: To examine the effects of stroke severity and target location on reaching (1) to identify regions in space that are difficult to reach, (2) to determine whether specific alterations in reaching are associated with particular clinical impairment levels, and (3) to characterize relationships between reaching alterations. DESIGN: Participants reached toward a screen of 75 targets spanning an approximate range from +/-90 degrees side to side and from waist to head. SETTING: Rehabilitation research center. PARTICIPANTS: Sixteen chronic stroke patients with a wide range in residual arm function and 4 control subjects. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Chedoke-McMaster Stroke Arm Assessment, distance, velocity, smoothness, straightness, and direction of the hand path during each reach. Hand position trajectories were recorded with an electromagnetic sensor. RESULTS: Reaches performed with the impaired arms showed significant degradation in all performance measures. Although only modestly dependent on the target location, these features correlated strongly with impairment level, as well as with each other. Reaching distance showed the strongest correlations with the other parameters. CONCLUSIONS: Stroke alters a broad array of features of reaching, yet largely the same degree of movement control is preserved across a range of target locations. The only consistently problematic task is to reach far out from the torso, independent of the movement direction. Thus, active range of motion (AROM), rather than control over a specific subset of movement directions, is a logical focus for therapy. In addition, measuring AROM is a simple clinical measure that yields much information.

Adult↗

PubMed2002

Web-based telerehabilitation for the upper extremity after stroke.

Stroke is a leading cause of disability in the United States and yet little technology is currently available for individuals with stroke to practice and monitor rehabilitation therapy on their own. This paper provides a detailed design description of a telerehabilitation system for arm and hand therapy following stroke. The system consists of a Web-based library of status tests, therapy games, and progress charts, and can be used with a variety of input devices, including a low-cost force-feedback joystick capable of assisting or resisting in movement. Data from home-based usage by a chronic stroke subject are presented that demonstrate the feasibility of using the system to direct a therapy program, mechanically assist in movement, and track improvements in movement ability.

Arm↗

PubMedDate not supplied

Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does?

Robot-assisted movement training improves arm movement ability following acute and chronic stroke. Such training involves two interacting processes: the patient trying to move and the robot applying forces to the patient's arm. A fundamental principle of motor learning is that movement practice improves motor function; the role of applied robotic forces in improving motor function is still unclear. This article reviews our work addressing this question. Our pilot study using the Assisted Rehabilitation and Measurement (ARM) Guide, a linear robotic trainer, found that mechanically assisted reaching improved motor recovery similar to unassisted reaching practice. This finding is inconclusive because of the small sample size (n = 19), but suggest that future studies should carefully control the amount of voluntary movement practice delivered to justify the use of robotic forces. We are optimistic that robotic forces will ultimately show additional therapeutic benefits when coupled with movement practice. We justify this optimism here by comparing results from the ARM Guide and the Mirror Image Movement Enabler robotic trainer. This comparison suggests that requiring a patient to generate specific patterns of force before allowing movement is more effective than mechanically completing movements for the patient. We describe the engineering implementation of this "guided-force training" algorithm.

Algorithms↗

PubMedDate not supplied

Tools for understanding and optimizing robotic gait training.

This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

Computer Simulation↗