Abstract
BackgroundThe clinical evaluation of the upper limb of severely impaired stroke patient is challenging. Sensor-based assessments may allow for an objective evaluation of this patient population. This study investigated the validity of a device-assisted approach in comparison to the clinical outcome that it is supposed to reflect.MethodsIn nineteen severely impaired chronic stroke patients, we applied a gravity-compensating, multi-joint arm exoskeleton (Armeo Spring) and compared this sensor-based assessment with the clinical outcome measure Upper Extremity Fugl-Meyer Assessment (UE-FMA) scale. Specifically, we assessed separately and subsequently the range of motion in joint space for four single joints (i.e., wrist, elbow and shoulder flexion/extension (FE), and shoulder internal/external rotation (IER)), and the closing and opening of the hand with a pressure sensor placed in the handle.ResultsWithin the kinematic parameters, a strong correlation was observed between wrist and elbow FE (r > 0.7, p < 0.003; Bonferroni corrected). The UE-FMA was significantly predicted by a multiple regression model (F (5, 13) = 12.22, p < 0.0005, adj. R2 = 0.83). Both shoulder IER and grip pressure added significantly (p < 0.05) to the prediction with the standardized coefficients β of 0.55 and 0.38, respectively.ConclusionsBy applying an exoskeleton-based self-contained evaluation of single-joint movements, a clinically valid assessment of the upper limb range of motion in severely impaired stroke patients is feasible. Shoulder IER contributed most relevantly to the prediction of the clinical status. These findings need to be confirmed in a large, independent patient cohort.
Highlights
The clinical evaluation of the upper limb of severely impaired stroke patient is challenging
Using the real-time sensor data of the exoskeleton to display a three-dimensional multi-joint visualization of the user’s arm in virtual reality (VR), we extended these features in-house to provide both visual and auditory instructions and feedback for the patient
One operator was present during the assessment but did not need to intervene in the evaluation procedure since the instructions, feedback and exercises ran smoothly
Summary
The clinical evaluation of the upper limb of severely impaired stroke patient is challenging. Such movement data may be acquired by various mechanical or optical systems, e.g., CyberGlove [7], Grimm et al J NeuroEngineering Rehabil (2021) 18:92 orthotic exoskeletons [8,9,10,11,12,13,14], gaming systems [15,16,17], or in combination with robotic systems for haptic feedback such as Rutgers Master II-ND haptic glove, MIT-Manus [18] or ARMIN [19] Devices such as the Armeo Spring [8,9,10,11,12,13,14], Armeo Power [20], ARMIN [19], Pneu-Wrex [21], ULEXO7 [22], ANYexo [23] and Harmony [24] have the advantage of providing at least partial kinematic registration of the upper limb movement for different joints. Such indirect measurements may miss small improvements in severely impaired patients
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