Activation of individual extrinsic thumb muscles and compartments of extrinsic finger muscles

Evolution, biomechanics, and neurobiology converge to explain selective finger motor control

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.

A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks

OBJECTIVE

The purpose of this work was to develop an open-source musculoskeletal model of the hand and wrist and to evaluate its performance during simulations of functional tasks.

METHODS

The current model was developed by adapting and expanding upon existing models. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization was used to simulate maximum grip and pinch force. Active and passive hand opening were simulated to evaluate coordinated kinematic hand movements.

RESULTS

The model's maximum grip force production matched experimental measures of grip force, force distribution amongst the digits, and displayed sensitivity to wrist flexion. Simulated lateral pinch strength replicated in vivo palmar pinch strength data. Additionally, predicted activations for 7 of 8 muscles fell within variability of EMG data during palmar pinch. The active and passive hand opening simulations predicted reasonable activations and demonstrated passive motion mimicking tenodesis, respectively.

CONCLUSION

This work advances simulation capabilities of hand and wrist models and provides a foundation for future work to build upon.

SIGNIFICANCE

This is the first open-source musculoskeletal model of the hand and wrist to be implemented during both functional kinetic and kinematic tasks. We provide a novel simulation framework to predict maximal grip and pinch force which can be used to evaluate how potential surgical and rehabilitation interventions influence these functional outcomes while requiring minimal experimental data.

The neural mechanisms of manual dexterity

The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.

Safe Approach for Flexor Digitorum Profundus I and II Using the Palmaris Longus Tendon

OBJECTIVE

To investigate a safe and accurate approach to achieve needle insertion for electromyography (EMG) of the flexor digitorum profundus (FDP) I and II muscles by identifying the anatomic relationship between the palmaris longus (PL) tendon, FDP muscle, and neurovascular bundle using ultrasonography.

DESIGN

Descriptive study SETTING: Department of physical medicine and rehabilitation.

PARTICIPANTS

Healthy individuals (age, 20-70y) without any diseases (N=29; 15 men, 14 women; 58 forearms).

INTERVENTIONS

Ultrasonography.

MAIN OUTCOME MEASURES

The FDP I and II muscles were transversely scanned on the volar aspect of the forearm at the junction of the middle and distal third between the medial epicondyle and ulnar styloid process. The distances and angles from the medial border of the PL tendon to FDP I, FDP II, and median nerve were measured.

RESULTS

The probability of damage to the neurovascular structures and the accuracy of entering the FDP I and II muscles were calculated for 3 imaginary needle insertion angles (61.7°, 100.6°, and 90°). When the needle was inserted at an angle of 61.7°, it reached FDP I with an accuracy of 91.4%. Upon needle insertions at 90° and 100.6°, the needle reached FDP II with accuracies of 90% and 89.6%, respectively. In all 3 cases (61.7°, 90°, and 100.6°), there was no chance of penetrating the blood vessels or nerves.

CONCLUSION

EMG of FDP I and II can be performed precisely and safely with the anterior approach at the distal one-third between the medial epicondyle and ulnar styloid process using the PL tendon.

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

Effect of User Practice on Prosthetic Finger Control With an Intuitive Myoelectric Decoder

Machine learning-based myoelectric control is regarded as an intuitive paradigm, because of the mapping it creates between muscle co-activation patterns and prosthesis movements that aims to simulate the physiological pathways found in the human arm. Despite that, there has been evidence that closed-loop interaction with a classification-based interface results in user adaptation, which leads to performance improvement with experience. Recently, there has been a focus shift toward continuous prosthesis control, yet little is known about whether and how user adaptation affects myoelectric control performance in dexterous, intuitive tasks. We investigate the effect of short-term adaptation with independent finger position control by conducting real-time experiments with 10 able-bodied and two transradial amputee subjects. We demonstrate that despite using an intuitive decoder, experience leads to significant improvements in performance. We argue that this is due to the lack of an utterly natural control scheme, which is mainly caused by differences in the anatomy of human and artificial hands, movement intent decoding inaccuracies, and lack of proprioception. Finally, we extend previous work in classification-based and wrist continuous control by verifying that offline analyses cannot reliably predict real-time performance, thereby reiterating the importance of validating myoelectric control algorithms with real-time experiments.

A comparison of fine wire insertion techniques for deep finger flexor muscle electromyography

INTRODUCTION

Intramuscular electromyography electrodes targeting flexor digitorum profundus (FDP) are inserted via the anterior or medial aspect of the forearm. These two methods pose different risks to neurovascular structures which overly FDP. This study aimed to compare the insertion depth and consider advantages and limitations of two different techniques to insert intramuscular electrodes into FDP.

METHODS

Using ultrasound imaging, neurovascular structures were identified along the path of FDP electrode insertion at the junction of the proximal and middle third of the ulna, bilaterally, in ten healthy individuals. Insertion depth was compared between the anterior and medial approaches for the mid muscle belly and targeted insertion to the index finger fascicle of FDP.

RESULTS

In our sample the ulnar artery was superficial to the FDP muscle when viewed anteriorly and was beyond the furthest border of FDP when viewed medially. Compared to the anterior approach, the medial insertion depth was 1.5 cm (95%CI 1.4-1.7, p < 0.001) less to the mid-belly of FDP and 0.6 cm (95%CI 0.4-0.7, p < 0.001) less to the index finger fascicle of FDP.

DISCUSSION

The medial approach involves less depth and lower risk for perforation of neurovascular structures when inserting intramuscular electrodes into the FDP muscle.

Subject-specific thumb muscle activity during functional tasks of daily life

BACKGROUND

The trapeziometacarpal joint is subjected to high compressive forces during powerful pinch and grasp tasks due to muscle loading. In addition, muscle contraction is important for stability of the joint. The aim of the present study is to explore if different muscle activation patterns can be found between three functional tasks.

METHODS

Isometric forces and fine-wire electromyographic (fEMG) activity produced by three intrinsic and four extrinsic thumb muscles were measured in 10 healthy female volunteers. The participants performed isometric contractions in a lateral key pinch, a power grasp and a jar twist task. The tasks were executed with and without EMG recording to verify if electrode placement influenced force production.

RESULTS

A subject-specific muscle recruitment was found which remained largely unchanged across tasks. Extrinsic thumb muscles were significantly more active than intrinsic muscles in all tasks. Insertion of the fEMG electrodes decreased force production significantly in all tasks.

CONCLUSION

The thumb muscles display a high variability in muscle activity during functional tasks of daily life. The results of this study suggest that to produce a substantial amount of force, a well-integrated, but subject-specific, co-contraction between the intrinsic and extrinsic thumb muscles is necessary.

Extracting extensor digitorum communis activation patterns using high-density surface electromyography

The extensor digitorum communis muscle plays an important role in hand dexterity during object manipulations. This multi-tendinous muscle is believed to be controlled through separate motoneuron pools, thereby forming different compartments that control individual digits. However, due to the complex anatomical variations across individuals and the flexibility of neural control strategies, the spatial activation patterns of the extensor digitorum communis compartments during individual finger extension have not been fully tracked under different task conditions. The objective of this study was to quantify the global spatial activation patterns of the extensor digitorum communis using high-density (7 × 9) surface electromyogram (EMG) recordings. The muscle activation map (based on the root mean square of the EMG) was constructed when subjects performed individual four finger extensions at the metacarpophalangeal joint, at different effort levels and under different finger constraints (static and dynamic). Our results revealed distinct activation patterns during individual finger extensions, especially between index and middle finger extensions, although the activation between ring and little finger extensions showed strong covariance. The activation map was relatively consistent at different muscle contraction levels and for different finger constraint conditions. We also found that distinct activation patterns were more discernible in the proximal–distal direction than in the radial–ulnar direction. The global spatial activation map utilizing surface grid EMG of the extensor digitorum communis muscle provides information for localizing individual compartments of the extensor muscle during finger extensions. This is of potential value for identifying more selective control input for assistive devices. Such information can also provide a basis for understanding hand impairment in individuals with neural disorders.

Extrinsic finger and thumb muscles command a virtual hand to allow individual finger and grasp control

Fine-wire intramuscular electrodes were used to obtain electromyogram (EMG) signals from six extrinsic hand muscles associated with the thumb, index, and middle fingers. Subjects' EMG activity was used to control a virtual three-degree-of-freedom (DOF) hand as they conformed the hand to a sequence of hand postures testing two controllers: direct EMG control and pattern recognition control. Subjects tested two conditions using each controller: starting the hand from a predefined neutral posture before each new posture and starting the hand from the previous posture in the sequence. Subjects demonstrated their abilities to simultaneously, yet individually, move all three DOFs during the direct EMG control trials; however, results showed subjects did not often utilize this feature. Performance metrics such as failure rate and completion time showed no significant difference between the two controllers.

On the usability of intramuscular EMG for prosthetic control: a Fitts' Law approach

Previous studies on intramuscular EMG based control used offline data analysis. The current study investigates the usability of intramuscular EMG in two degree-of-freedom using a Fitts' Law approach by combining classification and proportional control to perform a task, with real time feedback of user performance. Nine able-bodied subjects participated in the study. Intramuscular and surface EMG signals were recorded concurrently from the right forearm. Five performance metrics (Throughput,Path efficiency, Average Speed, Overshoot and Completion Rate) were used for quantification of usability. Intramuscular EMG based control performed significantly better than surface EMG for Path Efficiency (80.5±2.4% vs. 71.5±3.8%, P=0.004) and Overshoot (22.0±3.0% vs. 45.1±6.6%, P=0.01). No difference was found between Throughput and Completion Rate. However the Average Speed was significantly higher for surface (51.8±5.5%) than for intramuscular EMG (35.7±2.7%). The results obtained in this study imply that intramuscular EMG has great potential as control source for advanced myoelectric prosthetic devices.

Dexterous control of a prosthetic hand using fine-wire intramuscular electrodes in targeted extrinsic muscles

Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless human-machine interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees of freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into 12 postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.