Grip strength, forearm muscle fatigue and the response to handgrip exercise in rheumatoid arthritis

Weakness and subjective fatigue are common features of rheumatoid arthritis (RA). However, whether there is a true increase in the fatigability of rheumatoid skeletal muscle, in which fibre atrophy has been frequently reported, is unclear. Such factors may influence the ability to respond to exercise programmes. In this work, a reliable and sensitive technique for the objective measurement of forearm muscle fatigue during sustained grip was developed, using power spectral analysis of the surface myoelectric signal (SMES).The inter-relationships between grip force (hand function) and the activity and severity of the rheumatoid disease process with muscle fatigue (defined as the decline in the median frequency of the SMES with work, (MDFG)) and the initial median frequency of the SMES (IMF) were examined. It has been previously suggested that the IMF of the SMES may reflect the fibre type of the underlying muscle. The response to a 12-week progressive right hand grip strengthening programme in healthy females and those with RA was also evaluated. Potential predictors of outcome and the mechanisms of strength gain were examined. Forearm muscle fatigue in RA was not significantly greater than in healthy controls. However, higher levels of fatigue were associated with greater systemic disease activity and greater disease severity. The IMF of the SMES was shown to be stable over a wide range of grip forces for a given individual. It was significantly elevated in rheumatoid subjects, and showed a direct association with greater disease severity. Handgrip exercise was highly effective in improving hand function in females with RA. Strength gains were also demonstrated in healthy controls. Subjects with more severe disease and greater IMF of the SMES showed the greatest improvement in hand function. Greater systemic and local disease activity during the 12-week programme were limiting factors to improvement in grip. Local (right hand) disease activity remained stable or improved in the RA group overall, in spite of a trend towards deteriorating systemic and left handed disease activity. The two main potential mechanisms of strength gain (neural adaptation and gains in muscle mass) were assessed in both rheumatoid and healthy groups. The former was assessed by evaluation of the neuromuscular efficiency, derived from the relationship of the root mean square of the SMES at a given grip force. Gains in muscle mass were also assessed using this technique and by volumetric analysis of forearm musculature using magnetic resonance imaging. Although significant gains in muscle mass were demonstrated in the control group, no such gains were seen in the rheumatoid subjects. This indicates that neural adaptation was an effective method of strength gain in the rheumatoid group

Towards Natural Control of Artificial Limbs

The use of implantable electrodes has been long thought as the solution for a more natural control of artificial limbs, as these offer access to long-term stable and physiologically appropriate sources of control, as well as the possibility to elicit appropriate sensory feedback via neurostimulation. Although these ideas have been explored since the 1960’s, the lack of a long-term stable human-machine interface has prevented the utilization of even the simplest implanted electrodes in clinically viable limb prostheses.In this thesis, a novel human-machine interface for bidirectional communication between implanted electrodes and the artificial limb was developed and clinically implemented. The long-term stability was achieved via osseointegration, which has been shown to provide stable skeletal attachment. By enhancing this technology as a communication gateway, the longest clinical implementation of prosthetic control sourced by implanted electrodes has been achieved, as well as the first in modern times. The first recipient has used it uninterruptedly in daily and professional activities for over one year. Prosthetic control was found to improve in resolution while requiring less muscular effort, as well as to be resilient to motion artifacts, limb position, and environmental conditions.In order to support this work, the literature was reviewed in search of reliable and safe neuromuscular electrodes that could be immediately used in humans. Additional work was conducted to improve the signal-to-noise ratio and increase the amount of information retrievable from extraneural recordings. Different signal processing and pattern recognition algorithms were investigated and further developed towards real-time and simultaneous prediction of limb movements. These algorithms were used to demonstrate that higher functionality could be restored by intuitive control of distal joints, and that such control remains viable over time when using epimysial electrodes. Lastly, the long-term viability of direct nerve stimulation to produce intuitive sensory feedback was also demonstrated.The possibility to permanently and reliably access implanted electrodes, thus making them viable for prosthetic control, is potentially the main contribution of this work. Furthermore, the opportunity to chronically record and stimulate the neuromuscular system offers new venues for the prediction of complex limb motions and increased understanding of somatosensory perception. Therefore, the technology developed here, combining stable attachment with permanent and reliable human-machine communication, is considered by the author as a critical step towards more functional artificial limbs

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results

On the development of a cybernetic prosthetic hand

The human hand is the end organ of the upper limb, which in humans serves the important function of prehension, as well as being an important organ for sensation and communication. It is a marvellous example of how a complex mechanism can be implemented, capable of realizing very complex and useful tasks using a very effective combination of mechanisms, sensing, actuation and control functions. In this thesis, the road towards the realization of a cybernetic hand has been presented. After a detailed analysis of the model, the human hand, a deep review of the state of the art of artificial hands has been carried out. In particular, the performance of prosthetic hands used in clinical practice has been compared with the research prototypes, both for prosthetic and for robotic applications. By following a biomechatronic approach, i.e. by comparing the characteristics of these hands with the natural model, the human hand, the limitations of current artificial devices will be put in evidence, thus outlining the design goals for a new cybernetic device. Three hand prototypes with a high number of degrees of freedom have been realized and tested: the first one uses microactuators embedded inside the structure of the fingers, and the second and third prototypes exploit the concept of microactuation in order to increase the dexterity of the hand while maintaining the simplicity for the control. In particular, a framework for the definition and realization of the closed-loop electromyographic control of these devices has been presented and implemented. The results were quite promising, putting in evidence that, in the future, there could be two different approaches for the realization of artificial devices. On one side there could be the EMG-controlled hands, with compliant fingers but only one active degree of freedom. On the other side, more performing artificial hands could be directly interfaced with the peripheral nervous system, thus establishing a bi-directional communication with the human brain