BACKGROUND AND PURPOSE: Balance and mobility training consists of activities that carry a high risk for falling. The purpose of this article is to describe a novel robotic system for allowing challenging, yet safe, balance and mobility training in persons at high risk for falls. METHOD: With no initial preconceptions of what device we would build, a user-needs analysis led us to focus on increasing the level of challenge to a patient's ability to maintain balance during gait training and also on maintaining direct involvement of a physical therapist (rather than attempting robotic replacement). The KineAssist is a robotic device for gait and balance training that has emerged from a unique design process of a start-up product of a small company and a team of therapists, engineers, mechanical design experts, and rehabilitation scientists. RESULTS: The KineAssist provides partial body weight support and postural control on the torso; allows many axes of motion of the trunk and pelvis; leaves the patient's legs accessible to a physical therapist's manipulation during walking; follows a patient's walking motions overground in forward, rotation, and sidestepping directions; and catches an individual who loses balance and begins to fall. DISCUSSION AND CONCLUSION: Design and development of the KineAssist proceeded more rapidly in the context of a small company than would have been possible in most institutional research contexts. A prototype KineAssist has been constructed and has received US Food and Drug Administration (FDA) classification and institutional review board clearance for initial human studies. The acceptance of KineAssist will ultimately depend on improved patient outcomes, the use of this new tool by therapists, the ease of use of the system, and the recognition of the unique value it brings to therapeutic recovery.
BACKGROUND AND PURPOSE: Balance and mobility training consists of activities that carry a high risk for falling. The purpose of this article is to describe a novel robotic system for allowing challenging, yet safe, balance and mobility training in persons at high risk for falls. METHOD: With no initial preconceptions of what device we would build, a user-needs analysis led us to focus on increasing the level of challenge to a patient's ability to maintain balance during gait training and also on maintaining direct involvement of a physical therapist (rather than attempting robotic replacement). The KineAssist is a robotic device for gait and balance training that has emerged from a unique design process of a start-up product of a small company and a team of therapists, engineers, mechanical design experts, and rehabilitation scientists. RESULTS: The KineAssist provides partial body weight support and postural control on the torso; allows many axes of motion of the trunk and pelvis; leaves the patient's legs accessible to a physical therapist's manipulation during walking; follows a patient's walking motions overground in forward, rotation, and sidestepping directions; and catches an individual who loses balance and begins to fall. DISCUSSION AND CONCLUSION: Design and development of the KineAssist proceeded more rapidly in the context of a small company than would have been possible in most institutional research contexts. A prototype KineAssist has been constructed and has received US Food and Drug Administration (FDA) classification and institutional review board clearance for initial human studies. The acceptance of KineAssist will ultimately depend on improved patient outcomes, the use of this new tool by therapists, the ease of use of the system, and the recognition of the unique value it brings to therapeutic recovery.
Authors: David J Reinkensmeyer; Sarah Blackstone; Cathy Bodine; John Brabyn; David Brienza; Kevin Caves; Frank DeRuyter; Edmund Durfee; Stefania Fatone; Geoff Fernie; Steven Gard; Patricia Karg; Todd A Kuiken; Gerald F Harris; Mike Jones; Yue Li; Jordana Maisel; Michael McCue; Michelle A Meade; Helena Mitchell; Tracy L Mitzner; James L Patton; Philip S Requejo; James H Rimmer; Wendy A Rogers; W Zev Rymer; Jon A Sanford; Lawrence Schneider; Levin Sliker; Stephen Sprigle; Aaron Steinfeld; Edward Steinfeld; Gregg Vanderheiden; Carolee Winstein; Li-Qun Zhang; Thomas Corfman Journal: J Neuroeng Rehabil Date: 2017-11-06 Impact factor: 4.262
Authors: Alan J Pearce; Brooke Adair; Kimberly Miller; Elizabeth Ozanne; Catherine Said; Nick Santamaria; Meg E Morris Journal: J Aging Res Date: 2012-12-04