Multidirectional Overground Robotic Training Leads to Improvements in Balance in Older Adults

Abstract

For the rapidly growing aging demographic worldwide, robotic training methods could be impactful towards improving balance critical for everyday life. Here, we investigated the hypothesis that non-bodyweight supportive (nBWS) overground robotic balance training would lead to improvements in balance performance and balance confidence in older adults. Sixteen healthy older participants (69.7 ± 6.7 years old) were trained while donning a harness from a distinctive NaviGAITor robotic system. A control group of 11 healthy participants (68.7 ± 5.0 years old) underwent the same training but without the robotic system. Training included 6 weeks of standing and walking tasks while modifying: (1) sensory information (i.e., with and without vision (eyes-open/closed), with more and fewer support surface cues (hard or foam surfaces)) and (2) base-of-support (wide, tandem and single-leg standing exercises). Prior to and post-training, balance ability and balance confidence were assessed via the balance error scoring system (BESS) and the Activities specific Balance Confidence (ABC) scale, respectively. Encouragingly, results showed that balance ability improved (i.e., BESS errors significantly decreased), particularly in the nBWS group, across nearly all test conditions. This result serves as an indication that robotic training has an impact on improving balance for healthy aging individuals.

Keywords: aging; assistive robotics; balance; elderly; falls; rehabilitation robots; sensory training.

Figure 1.

Study Flow Diagram.

Figure 2.

Overview of NaviGAITor robotic system: (a) the NaviGAITor system consisting of an I-beam support structure (frame), gantry, motor, hoist, overhead harness, as well as sensors and a control system; the vicon motion capture set up shown is not part of the NaviGAITor system; (b) NaviGAITor harness with torso support and two thigh straps; (c) demonstrative use of NaviGAITor system for overground walking and balance training.

Figure 3.

BESS Test Conditions: (a) double-leg stance/hard surface (DL/Hard), (b) double-leg stance/foam surface (DL/Foam), (c) tandem stance/hard surface (T/Hard), (d) tandem stance/foam surface (T/Foam), (e) single-leg/hard surface (SL/Hard), (f) single-leg/foam surface (SL/Foam).

Figure 4.

BESS errors as a function of test condition for the (a) control group and (b) nBWS (NaviGAITor) group for pre- training (filled circles) and post-training (open circles). Per condition, maximum possible errors (or stance deviations) = 10, minimum possible errors (or stance deviations) = 0. BESS test conditions are: double-leg stance/hard surface (DL/Hard), double-leg stance/foam surface (DL/Foam), tandem stance/hard surface (T/Hard), tandem stance/foam surface (T/Foam), single-leg/hard surface (SL/Hard), most challenging: single-leg/foam surface (SL/Foam). For group for each condition, means and standard error of the mean bars are shown. Significance levels are as follows: * = p ~ 0.02 and ** = p < 0.00001.

Figure 5.

ABC balance confidence results for nBWS group (square) and control group (circle) with standard deviation bars shown for pre training (filled icon) and post training (open icon).