Clinical Validation of Kinematic Assessments of Post-Stroke Upper Limb Movements with a Multi-Joint Arm Exoskeleton

Abstract Background: Robotic and gravity-balancing exoskeletons, originally designed for the rehabilitation training of neurological patients, are now being increasingly applied in objective and fine-grained sensor-based assessments of upper limb function. However, gravity compensation, inertia and damping properties of the exoskeleton interfere with the natural sensorimotor interaction, proprioceptive and visual feedback during movement execution. This may endanger the validity of the kinematic assessments in relation to the clinical outcome measures that they were supposed to reflect. Here, we appliedMethods: In a proof of concept study involving nineteen severely impaired chronic stroke patients, we assessed sensor-based kinematic data acquired with a multi-joint arm exoskeleton and compared it to the clinical outcome measure Upper Extremity Fugl-Meyer Assessment (UE-FMA) scale. During this assessment, real-time movement feedback of the system’s seven degrees of freedom was provided with a biomorphic 3D virtual representation of the upper limb, including the proximal component of the arm. To align posture and to minimize the exoskeleton-patient interaction, the same position (neutral zero) with a distance of 90 degrees between forearm and upper arm was taken as the starting position for all assessments. Within self-contained tasks, we assessed separately and subsequently the range of motion/spatial posture of four single joints (i.e., joint angles of wrist, elbow, arm, and shoulder movement) and the closing and opening of the hand with a pressure sensor placed in the handle.Results: A strong correlation was observed between wrist and elbow movements within the kinematic parameters (r > 0.7, p<0.003; Bonferroni corrected). A multiple regression model predicted the UE-FMA significantly (F (5, 13) = 12.22, p < 0.0005, adj. R2 = 0.83). Both shoulder rotation and grip pressure added significantly (p < 0.05) to the prediction with the standardized coefficients β of 0.55 and 0.38, respectively.Conclusions: Exoskeleton-based evaluation of single-joint movements and grip force facilitates the assessment of upper limb kinematics after stroke with high structural and convergent validity. Proximal and distal measures may contribute independently to the prediction of the clinical status.

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

10.21203/rs.3.rs-52210/v2 ◽

2020 ◽

Author(s):

Florian Grimm ◽

Jelena Kraugmann ◽

Georgios Naros ◽

Alireza Gharabaghi

Keyword(s):

Clinical Outcome ◽

Range Of Motion ◽

Upper Limb ◽

Clinical Status ◽

Objective Evaluation ◽

Joint Space ◽

Multiple Regression Model ◽

Stroke Patients ◽

Starting Position ◽

Upper Limb Movements

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., angles of wrist, elbow, arm, and shoulder movement), and the closing and opening of the hand with a pressure sensor placed in the handle. The same position (neutral zero) with a distance of 90 degrees between forearm and upper arm was taken as the starting position for all assessments. RESULTS: Within the kinematic parameters, a strong correlation was observed between wrist and elbow movements (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 rotation 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 rotation contributed most relevantly to the prediction of the clinical status. These findings need to be confirmed in a large, independent patient cohort.

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

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00875-7 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Florian Grimm ◽

Jelena Kraugmann ◽

Georgios Naros ◽

Alireza Gharabaghi

Keyword(s):

Clinical Outcome ◽

Range Of Motion ◽

Upper Limb ◽

External Rotation ◽

Clinical Status ◽

Objective Evaluation ◽

Joint Space ◽

Multiple Regression Model ◽

Stroke Patients ◽

Flexion Extension

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.

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Analysis of Upper-Limb and Trunk Kinematic Variability: Accuracy and Reliability of an RGB-D Sensor

Multimodal Technologies and Interaction ◽

10.3390/mti4020014 ◽

2020 ◽

Vol 4 (2) ◽

pp. 14

Author(s):

Alessandro Scano ◽

Robert Mihai Mira ◽

Pietro Cerveri ◽

Lorenzo Molinari Tosatti ◽

Marco Sacco

Keyword(s):

Upper Limb ◽

Gold Standard ◽

Degrees Of Freedom ◽

Cost Effective ◽

Human Motion ◽

Working Environments ◽

Wide Range ◽

Upper Limb Movements ◽

Clinical Environments ◽

Point To Point

In the field of motion analysis, the gold standard devices are marker-based tracking systems. Despite being very accurate, their cost, stringent working environments, and long preparation time make them unsuitable for small clinics as well as for other scenarios such as industrial application. Since human-centered approaches have been promoted even outside clinical environments, the need for easy-to-use solutions to track human motion is topical. In this context, cost-effective devices, such as RGB-Depth (RBG-D) cameras have been proposed, aiming at a user-centered evaluation in rehabilitation or of workers in industry environment. In this paper, we aimed at comparing marker-based systems and RGB-D cameras for tracking human motion. We used a Vicon system (Vicon Motion Systems, Oxford, UK) as a gold standard for the analysis of accuracy and reliability of the Kinect V2 (Microsoft, Redmond, WA, USA) in a variety of gestures in the upper limb workspace—targeting rehabilitation and working applications. The comparison was performed on a group of 15 adult healthy subjects. Each subject had to perform two types of upper-limb movements (point-to-point and exploration) in three workspace sectors (central, right, and left) that might be explored in rehabilitation and industrial working scenarios. The protocol was conceived to test a wide range of the field of view of the RGB-D device. Our results, detailed in the paper, suggest that RGB-D sensors are adequate to track the upper limb for biomechanical assessments, even though relevant limitations can be found in the assessment and reliability of some specific degrees of freedom and gestures with respect to marker-based systems.

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Wearable upper limb motion assist robot for eating activity

Applied Research and Smart Technology (ARSTech) ◽

10.23917/arstech.v1i1.28 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1-10

Author(s):

Uzair Kashtwari ◽

Norsinnira Zainul Azlan ◽

Ifrah Shahdad

Keyword(s):

Upper Limb ◽

Daily Living ◽

Degrees Of Freedom ◽

Low Cost ◽

Elbow Flexion ◽

Disabled People ◽

Flexion Extension ◽

Feeding Cycle ◽

The Disabled ◽

Upper Limb Movements

Many people all around the world are suffering from various types of disabilities and need to depend on others to perform activities of daily living. One of the essential daily living activities is eating. The disabled people should be able to eat their food independently at any time and place, without relying on the caregivers. This paper presents the development of a new wearable upper limb motion assist robot for helping the disabled to eat by themselves. The motion assists robot consists of two degrees of freedom (DOF) movement, focusing on the two most crucial upper limb movements in eating activity, which is the elbow flexion/extension and forearm pronation/supination. A light-weight material was used for the fabrication of the wearable motion assist robot, and Arduino was utilized as the microcontroller. The originality of the study was in terms of the design, operational sequence setting, and kinematic analysis of the wearable upper limb motion assist robot that was explicitly focusing on eating activity. The resulted prototype was portable, compact, light in weight, simple and low cost. The experimental results have proven that the proposed wearable upper limb motion assist robot for eating activity was successful in helping the users to perform the main upper extremity motions in eating. The success rate of the proposed system was 80%, and it took 6 seconds for the system to complete one feeding cycle.

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Occupational Therapy Assessment for Upper Limb Rehabilitation: A Multisensor-Based Approach

Frontiers in Digital Health ◽

10.3389/fdgth.2021.784120 ◽

2021 ◽

Vol 3 ◽

Author(s):

Seedahmed S. Mahmoud ◽

Zheng Cao ◽

Jianming Fu ◽

Xudong Gu ◽

Qiang Fang

Keyword(s):

Muscle Strength ◽

Occupational Therapy ◽

Upper Limb ◽

Measurement Unit ◽

Fine Motor ◽

Upper Limb Rehabilitation ◽

Therapy Assessment ◽

Upper Limb Function ◽

Upper Limb Movements ◽

Highly Correlated

Most post-stroke patients experience varying degrees of impairment in upper limb function and fine motor skills. Occupational therapy (OT) with other rehabilitation trainings is beneficial in improving the strength and dexterity of the impaired upper limb. An accurate upper limb assessment should be conducted before prescribing upper limb OT programs. In this paper, we present a novel multisensor method for the assessment of upper limb movements that uses kinematics and physiological sensors to capture the movement of the limbs and the surface electromyogram (sEMG). These sensors are Kinect, inertial measurement unit (IMU), Xsens, and sEMG. The key assessment features of the proposed model are as follows: (1) classification of OT exercises into four classes, (2) evaluation of the quality and completion of the OT exercises, and (3) evaluation of the relationship between upper limb mobility and muscle strength in patients. According to experimental results, the overall accuracy for OT-based motion classification is 82.2%. In addition, the fusing of Kinect and Xsens data reveals that muscle strength is highly correlated with the data with a correlation coefficient (CC) of 0.88. As a result of this research, occupational therapy specialists will be able to provide early support discharge, which could alleviate the problem of the great stress that the healthcare system is experiencing today.

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