Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton

Assessing something is useless with no protocols specified to recover the disability. So no protocols; totally useless crapola.  Why are mentors and senior researchers allowing this crap?

 

Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton

• Florian Grimm,
• Jelena Kraugmann,
• Georgios Naros &
• Alireza Gharabaghi 

Journal of NeuroEngineering and Rehabilitation volume 18, Article number: 92 (2021) Cite this article

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Abstract

Background

The 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.

Methods

In 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.

Results

Within 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.

Conclusions

By 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.

Background

Currently, the majority of stroke patients will not regain full function of the affected upper limb [1, 2]. This impairment is a decisive factor for their diminished quality of life [3]. Early and high-dose movement therapies are relevant for clinically meaningful improvements [4]. Furthermore, the assessment of upper limb movements is crucial in monitoring and understanding sensorimotor recovery [5]. An increase in the assessment frequency by means of kinematic parameters could, therefore, optimize clinical assessment procedures and enhance the effectiveness of rehabilitation treatments [6]. Particularly in severely impaired stroke patients, objective assessments are necessary to identify even small improvements in the course of a therapeutic intervention.

Such movement data may be acquired by various mechanical or optical systems, e.g., CyberGlove [7], 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. By contrast, systems such as the MIT-Manus [18], ReaPLAN [25, 26], ReoGo [27], Planar robot [27], and PUPArm [28] allow for endpoint-based alignment. With these latter devices, the movement of the shoulder and upper arm is estimated as a surrogate parameter, and not directly via sensors. However, such indirect measurements may miss small improvements in severely impaired patients.

There is a large variety of kinematic parameters that have been applied for upper limb evaluations such as movement accuracy, efficacy, planning, precision, smoothness, speed, spatial and temporal posture; some of them have also been correlated to clinical outcome measures following stroke [6, 29, 30], while the Upper-Extremity Fugl–Meyer Assessment (UE-FMA) scale [31] was most frequently being applied for the estimation of the clinical impairment level [32]. A number of kinematic parameters showed a significant association with the clinical evaluation (correlation coefficient of more than 0.7); however, the majority of the kinematic parameters showed either weak (less than 0.3) or moderate (0.3–0.7) associations [6]. This limited association may be related to the fact that kinematic measurements of the proximal component of the upper limb are often missing [3].

Most of the applied kinematic parameters resulted from rather complex training exercises and were not acquired for each upper limb segment separately. Specifically, either 2D pointing, 2D shape drawing, 3D pointing or 3D reach-to-grasp tasks were performed first, and then a posthoc segmental evaluation was conducted, e.g., of angle data for shoulder movement [33], range of arm elevation [15], elbow flexion/extension (FE) [33], and wrist FE [15,16,17]. These previous approaches are, however, at odds with the most recent systematic review on kinematic assessments of upper limb movements after stroke [29]. It suggested that the measures should be acquired with the help of a self-contained task and not during the exercises that the patient is doing for rehabilitation training, since the latter would confound the results for upper limb evaluation by including exercise-specific learning effects [34].

In this context, the first study that quantified the active range of motion for each segment separately found overall promising correlations to motor function [17], but did not use the clinical gold standard measure UE-FMA scale for this purpose. For the evaluation of severely impaired stroke patients, who are often not able to move the upper limb against gravity, the assessment device would, furthermore, need to balance gravity and capture even small movements of single joints. Therefore, multi-joint exoskeletons such as the passive Armeo Spring [8, 9, 11, 12] or the active (i.e., robotic) Armeo Power [20] are suitable for this purpose. These exoskeletons show high interaction forces between the measurement system and patient due to friction, inertia and arm weight support. More recent devices reduce friction by actuator choice, and lower inertia by lightweight design and the use of interaction force sensors [23, 24, 35, 36]. However, exoskeleton-based assessment tools necessitate a systematic evaluation to estimate their clinical validity. Specifically, there is currently no study that assessed the upper limb of severely impaired stroke patients with a multi-joint exoskeleton in comparison to the UE-FMA scale. The present study intended to close this gap.

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