Treatment of the Paretic Hand with a Robotic Glove Combined with Physiotherapy in a Patient Suffering from Traumatic Tetraparesis: A Case Report.

BACKGROUND: cervical spinal cord injury leads to loss of upper limb functionality, which causes a decrease in autonomy to perform activities of daily living. The use of robotic technologies in rehabilitation could contribute to improving upper limb functionality and treatment quality. This case report aims to describe the potential of robotic hand treatment with Gloreha Sinfonia, in combination with conventional rehabilitation, in a tetraparetic patient. MATERIAL: fifteen rehabilitative sessions were performed. Evaluations were conducted pre-treatment (T0), post-treatment (T1), and at two-months follow-up (T2) based on: the upper-limb range of motion and force assessment, the FMA-UE, the 9-Hole Peg Test (9HPT), and the DASH questionnaire. A virtual reality game-based rating system was used to evaluate the force control and modulation ability. RESULTS: the patient reported greater ability to use hands with less compensation at T1 and T2 assessments. Improvements in clinical scales were reported in both hands at T1, however, at T2 only did the dominant hand show further improvement. Improved grip strength control and modulation ability were reported for T1. However a worsening was found in both hands at T2, significant only for the non-dominant hand. The maximum force exerted increased from T0 to T2 in both hands. CONCLUSION: hand treatment combining physical therapy and Gloreha Sinfonia seems to have benefits in functionality and dexterity in tetraparetic patient in the short term. Further studies are needed to confirm these findings, to verify long-term results, and to identify the most appropriate modalities of robotic rehabilitation.

A marker-based approach to determine the centers of rotation of finger joints.

BACKGROUND AND OBJECTIVE: The methods proposed in literature to estimate the position of hand joints Centers of Rotation (CoRs) typically require computationally non-trivial optimization routines and exploit a high number of markers to calculate CoRs positions from surface marker trajectories. Moreover, most of the existing works evaluated the accuracy only in simulation. This work proposes a new procedure, based on the Pratt circle fit, to estimate joints CoRs position in 2D through marker-based acquisitions. METHODS: The advantage of the Pratt circle fit lies in its simplicity and computational speed, and in the possibility of exploiting a reduced markerset for calculating CoRs. By applying simplifying assumptions regarding the movement of the fingers (i.e., planar and decoupled flexion-extension movements of each joint occurring in the same flexion plane for all the joints of the finger), it is possible to determine the position of the CoR of each joint in 2D. For this reason, the estimation of the Carpo-MetaCarpal joint of the thumb was not included in this work, as it exhibits a more complex movement associated to the combination of a flexion-extension and adduction-abduction degree of freedom. The errors in estimating CoRs were evaluated by conducting experimental acquisitions on an anthropomorphic robotic hand and comparing the position of the estimated CoR with the real position of the CoR. The repeatability of the method and its capability to estimate anatomically plausible CoRs were evaluated through experimental acquisitions conducted on five healthy volunteers. RESULTS: Errors in estimating finger joints CoRs were in the order of 0.70 mm and 0.18 mm respectively along the finger longitudinal direction (i.e., x coordinate) and thickness (i.e., y coordinate). Standard Deviations of CoRs positions were comparable to the ones obtained in literature (i.e., below 2 mm and 1 mm respectively for the x and y coordinates), thus demonstrating the repeatability of the method. The Anatomical Plausibility Rate of the proposed approach was between 80% and 100%. CONCLUSIONS: The performance of the Pratt-based CoRs estimation procedure proposed in this work was comparable to other existing methods, with the advantage of exploiting a simple fitting algorithm and a reduced markerset with respect to the state-of-the-art techniques.

Development and Validation of a System for the Assessment and Recovery of Grip Force Control.

The ability to finely control hand grip forces can be compromised by neuromuscular or musculoskeletal disorders. Therefore, it is recommended to include the training and assessment of grip force control in rehabilitation therapy. The benefits of robot-mediated therapy have been widely reported in the literature, and its combination with virtual reality and biofeedback can improve rehabilitation outcomes. However, the existing systems for hand rehabilitation do not allow both monitoring/training forces exerted by single fingers and providing biofeedback. This paper describes the development of a system for the assessment and recovery of grip force control. An exoskeleton for hand rehabilitation was instrumented to sense grip forces at the fingertips, and two operation modalities are proposed: (i) an active-assisted training to assist the user in reaching target force values and (ii) virtual reality games, in the form of tracking tasks, to train and assess the user's grip force control. For the active-assisted modality, the control of the exoskeleton motors allowed generating additional grip force at the fingertips, confirming the feasibility of this modality. The developed virtual reality games were positively accepted by the volunteers and allowed evaluating the performance of healthy and pathological users.

A user-friendly automatic toolbox for hand kinematic analysis, clinical assessment and postural synergies extraction.

The clinical assessment of the human hand is typically conducted through questionnaires or tests that include objective (e.g., time) and subjective (e.g., grasp quality) outcome measures. However, there are other important indicators that should be considered to quantify grasp and movement quality in addition to the time needed by a subject to execute a task, and this is essential for human and artificial hands that attempt to replicate the human hand properties. The correct estimation of hand kinematics is fundamental for computing these indicators with high fidelity, and a technical background is typically required to perform this analysis. In addition, to understand human motor control strategies as well as to replicate them on artificial devices, postural synergies were widely explored in recent years. Synergies should be analyzed not only to investigate possible modifications due to musculoskeletal and/or neuromuscular disorders, but also to test biomimetic hands. The aim of this work is to present an open source toolbox to perform all-in-one kinematic analysis and clinical assessment of the hand, as well as to perform postural synergies extraction. In the example provided in this work, the tool takes as input the position of 28 retroreflective markers with a diameter of 6 mm, positioned on specific anatomical landmarks of the hand and recorded with an optoelectronic motion capture system, and automatically performs 1) hand kinematic analysis (i.e., computation of 23 joint angles); 2) clinical assessment, by computing indicators that allow quantifying movement efficiency (Peak Grip Aperture), smoothness (Normalized Dimensionless Jerk Grasp Aperture) and speed (Peak Velocity of Grasp Aperture), planning capabilities (Time to Peak Grip Aperture), spatial posture (Wrist and Finger Joint Angles) and grasp stability (Posture of Hand Finger Joints), and 3) postural synergies extraction and analysis through the Pareto, Scree and Loadings plots. Two examples are described to demonstrate the applicability of the toolbox: the first one aiming at performing a clinical assessment of a volunteer and the second one aiming at extracting and analyzing the volunteer's postural synergies. The tool allows calculating joint angles with high accuracy (reconstruction errors below 4 mm and 3.2 mm for the fingers and wrist respectively) and automatically performing clinical assessment and postural synergies extraction. Results can be visually inspected, and data can be saved for any desired post processing analysis. Custom-made protocols to extract joint angles, based on different markersets, could be also integrated in the toolbox. The tool can be easily exploitable in clinical contexts, as it does not require any particular technical knowledge to be used, as confirmed by the usability evaluation conducted (perceived usability = 94.2 ± 5.4). In addition, it can be integrated with the SynGrasp toolbox to perform grasp analysis of underactuated virtual hands based on postural synergies.