Abstract
ABSTRACTEnd-effector robots for gait training have potential for cardiovascular fitness therapy. We developed and tested a heart rate (HR) controller for end-effector robots, operated in stair-climbing mode. The structure has an inner loop for volitional control of exercise work rate and an automatic outer loop to compute target work rate and control HR. Feedback design focused on disturbances caused by HR variability, by shaping the input-sensitivity function to give low-pass loop characteristics. Using five able-bodied subjects, command response tests revealed consistent, accurate and stable performance for all subjects with root-mean-square (RMS) HR tracking error bpm (mean ± SD) and average control signal power W2. Disturbances in cadence were successfully rejected with RMS HR tracking error bpm and average control signal power W2. Feasibility of the HR control strategy for end-effector robots was proven. The controller showed consistent behaviour for all command response and disturbance rejection tasks. Robustness was proven since the single LTI controller used a nominal model which was not specific to any of the five subjects. Physiological HR variability is the principal feedback design issue for HR control, while parametric/structural plant uncertainty is secondary.
Highlights
Gait rehabilitation is an important part of the treatment of patients with neurological impairments resulting from stroke or other conditions
The standard G-EO system was augmented for this study with a visual biofeedback system to allow each subject to perform volitional control of exercise work rate; this human-in-the-loop volitional work rate controller is embedded within the overall heart rate controller structure (Figure 2)
This value is slightly higher than previous reports of heart rate control which used a conventional treadmill: using a similar low-pass input-sensitivityshaping approach, Hunt and Fankhauser (2016) reported RMSEHR = 2.96 ± 0.85 bpm; using two non-low-pass control approaches, one linear and one nonlinear, Hunt and Maurer (2016) reported RMSEHR = 2.3 ± 0.5 bpm
Summary
Gait rehabilitation is an important part of the treatment of patients with neurological impairments resulting from stroke or other conditions. The annual incidence of stroke is approximately 180 per 100,000 (KolominskyRabas & Heuschmann, 2002). Due to this high number of new stroke patients, with most patients able to benefit from gait rehabilitation, efficient training is needed. There are two main types of robotics-assisted gait rehabilitation systems: robotic exoskeletons (Westlake & Patten, 2009) and end-effector gait rehabilitation robots (Hesse, Waldner, & Tomelleri, 2010; Stoller, Schindelholz, Bichsel, & Hunt, 2014). In order to imitate locomotion as naturally as possible, assisted stair climbing has been introduced in end-effector robots as an effective method of task specific training. It has been shown that the task of climbing up and down a flight of stairs takes less time when conventional therapy is accompanied by robotics-assisted gait training (Hesse, Tomelleri, Bardeleben, Werner, & Waldner, 2012)
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