Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot

Abstract

Background: Selecting and maintaining an engaging and challenging training difficulty level in robot-assisted stroke rehabilitation remains an open challenge. Despite the ability of robotic systems to provide objective and accurate measures of function and performance, the selection and adaptation of exercise difficulty levels is typically left to the experience of the supervising therapist.

Methods: We introduce a patient-tailored and adaptive robot-assisted therapy concept to optimally challenge patients from the very first session and throughout therapy progress. The concept is evaluated within a four-week pilot study in six subacute stroke patients performing robot-assisted rehabilitation of hand function. Robotic assessments of both motor and sensory impairments of hand function conducted prior to the therapy are used to adjust exercise parameters and customize difficulty levels. During therapy progression, an automated routine adapts difficulty levels from session to session to maintain patients' performance around a target level of 70%, to optimally balance motivation and challenge.

Results: Robotic assessments suggested large differences in patients' sensorimotor abilities that are not captured by clinical assessments. Exercise customization based on these assessments resulted in an average initial exercise performance around 70% (62% ± 20%, mean ± std), which was maintained throughout the course of the therapy (64% ± 21%). Patients showed reduction in both motor and sensory impairments compared to baseline as measured by clinical and robotic assessments. The progress in difficulty levels correlated with improvements in a clinical impairment scale (Fugl-Meyer Assessment) (r s = 0.70), suggesting that the proposed therapy was effective at reducing sensorimotor impairment.

Conclusions: Initial robotic assessments combined with progressive difficulty adaptation have the potential to automatically tailor robot-assisted rehabilitation to the individual patient. This results in optimal challenge and engagement of the patient, may facilitate sensorimotor recovery after neurological injury, and has implications for unsupervised robot-assisted therapy in the clinic and home environment.

Trial registration: ClinicalTrials.gov, NCT02096445.

Figure 1

Neurocognitive therapy with the ReHapticKnob. A: direct vision of the hand is blocked through the placement of the computer monitor over the patient’s hand. The screen shows information relevant to the execution of the respective exercise. B and C: thumb and fingers are attached to the finger supports of the ReHapticKnob and held in place with Velcro straps.

Figure 2

Detailed description of exercises E₁- E₇. The heuristically defined exercise parameters used to customize the difficulty levels are shown within curly brackets in the “Exercise parameters” column. Refer to the flowchart in Figure 3 for a description of the patient-tailored and adaptive therapy concept.

Figure 3

Patient-tailored and adaptive therapy concept. The difficulty levels of the neurocognitive robot-assisted exercises (detailed in Figure 2) are customized before the therapy onset using the assessed rotational range of motion (ROM_φ), the just perceptible difference threshold in grasping aperture (DL) and the stiffness Weber fraction (Wf). An automatic difficulty adaptation routine adjusts the exercise difficulty level on a session-by-session basis according to the performance during the last session of the respective exercise (performance computed over the entire exercise session).

Figure 4

Results of the robotic assessments A1-A3 during the pre assessment (week 0). Top: pronosupination (left) and grasping aperture (right) range of motion. Bottom: Patient-wise evolution of the presented stimulus differences Δd and Δ k_% to assess proprioception during hand opening/closing and haptic perception during grasping. Presented stimulus levels are adaptively selected by the PEST algorithm and converge to the smallest perceptible difference. Four assessment runs did not converge within the predefined time constraint of 20 minutes and are indicated with a cross (x) for the last trial. Healthy performance is indicated with dashed lines for comparison (forearm pronation 70°, forearm supination -85° [38], distance DL = 1 mm [35], stiffness WF = 7 %[37]), except for translational ROM, which depends on the hand size.

Figure 5

Therapy exercise performance. Top: Average therapy session performance (mean and standard deviation over all patients) tracks the desired 70% level (left y-axis). During therapy progression, difficulty levels (averaged over all patients) continuously increase (right y-axis, blue circles). Bottom: Exercise-wise performance evolution and corresponding difficulty level adjustments for a representative patient (P4). Note that only a subset of 3 exercises was performed during each therapy session.

Figure 6

Improvement in clinical and robotic assessment scores. mean and 95% confidence interval of the change in the clinical (total FMA-UE score and FMA hand/wrist subscore) and robotic assessment scores (rotational and translational range of motion, proprioception (A2) and haptic perception (A3)) from pre to post assessment (left panel) and from the pre to follow-up assessment (right panel). Positive changes (negative changes in the case of A2 and A3) indicate an improvement on the assessment scale, i.e. an impairment reduction.

Figure 7

Difficulty level increase correlates with clinical scores. The difficulty level improvement summed over all 7 exercises correlates with the changes in the FMA-UE (total score and hand/wrist subscore) from the pre to the post assessment. r_s is the Spearman’s rank correlation coefficient. The line through the data points was fitted by linear regression.