Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation

<p>Abstract</p> <p>Background</p> <p>Dexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands.</p> <p>Methods</p> <p>The central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances.</p> <p>Results</p> <p>The system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only).</p> <p>Conclusions</p> <p>The original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training.</p

Finger Flexor Tendon Orientation and Location as a Function of Postural Changes of the Wrist and Forearm: The Quantification of Musculoskeletal Loading in Jobs with Deviated Forearms

Forearm pronation/supination is common during manual activities, and has been linked to upper limb disorders in the workplace (Hughes et al. 1997). Forearm deviations from neutral (palm of the hand facing medially) can increase discomfort and forearm musculature activity (EMG) (Khan 2009a; Domizio & Keir, 2010), particularly when combined with wrist postures deviated from neutral. Yet ergonomic tools commonly used to assess the risk of developing distal upper limb disorders (e.g., Strain Index and RULA), often disregard or only minimally account for forearm pronation/supination posture. As a result, the risk of injury may be underestimated. This dissertation first examined methods of measuring pronation in the workplace by testing instantaneous agreement of forearm posture measurements between Inertial Motion Units (Xsens, Netherlands) and a laboratory-based motion capture system (Vicon, UK). Participants turned metallic and non-metallic handles in front of them, in order to quantify the effect of magnetic disturbance and sensor orientation on the Xsens. On average, RMSE errors of 12.6 deg around metal, and 8.6 deg around plastic were observed on instantaneous measures. Higher rotational velocities appeared associated with larger errors. Summarized data revealed smaller discrepancies. Second, this dissertation examined the effect of forearm pronation/supination coupled with wrist flexion/extension on the orientation and location of finger flexor tendons with respect to a radial coordinate system, using MRI of 4 healthy wrists. Pronation/supination caused movement almost exclusively in the frontal plane. Radial tendons exhibited larger angular deviations in pronation, whereas ulnar tendons were nearly straight, and the opposite was observed in supination. Larger angular deviations were thought to increase contact forces within the tunnel in the direction of the bend, which combined with finger movement could increase the risk of tenosynovitis. Finally the results of these studies were combined to measure tendon movement during a repetitive task. The three tendons with the greatest angular movement in the tunnel were: FDP2 (0.16 deg/pronation/supination degree), FDS3 (0.15 deg/ pronation/supination degree), and FDS4 (0.17 deg/ pronation/supination degree)

Artificial intelligence in musculoskeletal ultrasound imaging

Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.11Nsciescopu

Non-contact measures to monitor hand movement of people with rheumatoid arthritis using a monocular RGB camera

Hand movements play an essential role in a person’s ability to interact with the environment. In hand biomechanics, the range of joint motion is a crucial metric to quantify changes due to degenerative pathologies, such as rheumatoid arthritis (RA). RA is a chronic condition where the immune system mistakenly attacks the joints, particularly those in the hands. Optoelectronic motion capture systems are gold-standard tools to quantify changes but are challenging to adopt outside laboratory settings. Deep learning executed on standard video data can capture RA participants in their natural environments, potentially supporting objectivity in remote consultation. The three main research aims in this thesis were 1) to assess the extent to which current deep learning architectures, which have been validated for quantifying motion of other body segments, can be applied to hand kinematics using monocular RGB cameras, 2) to localise where in videos the hand motions of interest are to be found, 3) to assess the validity of 1) and 2) to determine disease status in RA. First, hand kinematics for twelve healthy participants, captured with OpenPose were benchmarked against those captured using an optoelectronic system, showing acceptable instrument errors below 10°. Then, a gesture classifier was tested to segment video recordings of twenty-two healthy participants, achieving an accuracy of 93.5%. Finally, OpenPose and the classifier were applied to videos of RA participants performing hand exercises to determine disease status. The inferred disease activity exhibited agreement with the in-person ground truth in nine out of ten instances, outperforming virtual consultations, which agreed only six times out of ten. These results demonstrate that this approach is more effective than estimated disease activity performed by human experts during video consultations. The end goal sets the foundation for a tool that RA participants can use to observe their disease activity from their home.Open Acces

Individualisation des paramètres musculaires pour la modélisation musculo-squelettique de la main : application à la compréhension de l'arthrose

Hand osteoarthritis is a pathology which results in pain and functional impotency which are problematic for everyday life. Unfortunately, because of the complexity of hand biomechanics and the lack of quantification of finger joint loading, the prevention and the rehabilitation of this pathology remain problematic. The objective of this doctoral work was to develop the musculoskeletal modelling of the hand to improve the understanding of hand osteoarthritis from a biomechanical point of view. A complete model of the hand, including the five fingers and the wrist, as well as an experimental protocol for measuring hand kinematics and grip forces were first developed to estimate all the muscle forces and joint forces during prehension tasks. These methodological tools have then been used to clarify the risk factors of hand osteoarthritis associated to prehension tasks and to specific joints. To investigate more precisely the risk factors associated to individuals, a method has been developed to individualise muscle parameters of the hand musculoskeletal model in order to provide a better representation of the real performances of each subject. This method has then been applied to the analysis of two osteoarthritis patients and allowed a complete characterization of the specific biomechanical adaptations and consequences associated to their specific affections. The hand musculoskeletal model and the experimental protocols developed during this doctoral work provided quantified data which represents a concrete interest to improve prevention but also to elaborate and evaluate rehabilitation programs.L’arthrose de la main est une pathologie qui engendre des douleurs et des impotences fonctionnelles fortement handicapantes pour la vie quotidienne. Malheureusement, du fait de la complexité biomécanique de la main et du manque de quantification des forces subies par les articulations des doigts, la prévention et la réhabilitation de cette pathologie demeurent problématiques. L’objectif de ce travail doctoral a été de développer une modélisation musculo-squelettique de la main pour améliorer la compréhension de l’arthrose du point de vue biomécanique. Un modèle complet de la main, incluant les cinq doigts et le poignet, ainsi qu’un protocole expérimental de mesure de la cinématique et des forces externes appliquées à la main ont d’abord été développés pour estimer l’ensemble des forces musculaires et des forces articulaires durant la préhension. Ces outils méthodologiques ont permis de clarifier les risques d’arthrose associés aux types de préhension ainsi que ceux spécifiques aux articulations. Afin d’analyser plus précisément les facteurs de risques associés à chaque individu, une méthode d’individualisation des paramètres musculaires a été développée afin de mettre le modèle de la main à l’échelle des capacités réelles des individus. Cette méthode a été employée pour l’analyse de deux patientes et a permis de caractériser les adaptations et les conséquences biomécaniques associées à leurs affections spécifiques. Le modèle de la main et les protocoles expérimentaux développés ont ainsi fournit des données quantifiées qui représentent un intérêt concret pour l’amélioration de la prévention ainsi que pour l’élaboration et l’évaluation de programmes de réhabilitation