The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements

<p>Abstract</p> <p>Background</p> <p>Individuals with severe physical disabilities and minimal motor behaviour may be unable to use conventional mechanical switches for access. These persons may benefit from access technologies that harness the volitional activity of muscles. In this study, we describe the design and demonstrate the performance of a binary switch controlled by mechanomyogram (MMG) signals recorded from the frontalis muscle during eyebrow movements.</p> <p>Methods</p> <p>Muscle contractions, detected in real-time with a continuous wavelet transform algorithm, were used to control a binary switch for computer access. The automatic selection of scale-specific thresholds reduced the effect of artefact, such as eye blinks and head movement, on the performance of the switch. Switch performance was estimated by cued response-tests performed by eleven participants (one with severe physical disabilities).</p> <p>Results</p> <p>The average sensitivity and specificity of the switch was 99.7 ± 0.4% and 99.9 ± 0.1%, respectively. The algorithm performance was robust against typical participant movement.</p> <p>Conclusions</p> <p>The results suggest that the frontalis muscle is a suitable site for controlling the MMG-driven switch. The high accuracies combined with the minimal requisite effort and training show that MMG is a promising binary control signal. Further investigation of the potential benefits of MMG-control for the target population is warranted.</p

Mechanomyographic amplitude and frequency responses during dynamic muscle actions: a comprehensive review

The purpose of this review is to examine the literature that has investigated mechanomyographic (MMG) amplitude and frequency responses during dynamic muscle actions. To date, the majority of MMG research has focused on isometric muscle actions. Recent studies, however, have examined the MMG time and/or frequency domain responses during various types of dynamic activities, including dynamic constant external resistance (DCER) and isokinetic muscle actions, as well as cycle ergometry. Despite the potential influences of factors such as changes in muscle length and the thickness of the tissue between the muscle and the MMG sensor, there is convincing evidence that during dynamic muscle actions, the MMG signal provides valid information regarding muscle function. This argument is supported by consistencies in the MMG literature, such as the close relationship between MMG amplitude and power output and a linear increase in MMG amplitude with concentric torque production. There are still many issues, however, that have yet to be resolved, and the literature base for MMG during both dynamic and isometric muscle actions is far from complete. Thus, it is important to investigate the unique applications of MMG amplitude and frequency responses with different experimental designs/methodologies to continually reassess the uses/limitations of MMG

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