Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed,3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are usedcoherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus,the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional liftto provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper

Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed,3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are usedcoherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus,the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional liftto provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper

Time-Varying Delay Estimation Using Common Local All-Pass Filters with Application to Surface Electromyography

Estimation of conduction velocity (CV) is an important task in the analysis of surface electromyography (sEMG). The problem can be framed as estimation of a time-varying delay (TVD) between electrode recordings. In this paper we present an algorithm which incorporates information from multiple electrodes into a single TVD estimation. The algorithm uses a common all-pass filter to relate two groups of signals at a local level. We also address a current limitation of CV estimators by providing an automated way of identifying the innervation zone from a set of electrode recordings, thus allowing incorporation of the entire array into the estimation. We validate the algorithm on both synthetic and real sEMG data with results showing the proposed algorithm is both robust and accurate

Applications of normalised mutual information in high density surface electromyography

Normalised mutual information (NMI) is a measure derived from Shannon's entropy that has been used in a variety of fields to measure the similarity or dependence between random variables. In terms of biomedical signal processing, NMI has been used in electroencephalography to identify the functional connectivity between different regions of the brain by calculating the NMI between electrodes. Researchers have adopted this method for surface electromyography (sEMG) and have used NMI to find the functional connectivity between pairs of muscles. While these studies have been able to demonstrate that NMI can be used to measure the functional connectivity between muscles, there is little to no literature exploring other forms of sEMG signal analysis using NMI. Therefore, this research focussed on investigating alternative applications for the NMI of sEMG, the results and observations of which are discussed in this thesis.    During this research four applications for NMI were identified and investigated using high density sEMG (HD-sEMG). These applications were monitoring the progression of muscle fatigue, estimating the border between superficial muscles, locating the innervation zones (IZ) of a muscle, and identifying noisy electrodes. Initially, a method was developed to analyse HD-sEMG data using NMI, this method created NMI distributions which describe the similarity of each electrode with every other electrode. In order to summarise the NMI distributions, two additional methods were developed. The first method produced interaction maps which illustrate the number of electrodes that are similar to each electrode. The second method produced total NMI magnitude maps which show the similarity of each electrode with all the other electrodes through a sum of the NMI distributions. These methods were used to observe how muscle fatigue, IZs, noisy electrodes, and multiple muscle masses affected the NMI between electrodes. For each of the applications these observations were then used to determine whether the NMI was an appropriate measure. In each case changes in the NMI between electrodes were observed. For muscle fatigue the NMI was shown to significantly increase as the muscles fatigued, while the effect of contraction strength did not have a significant effect. This significant increase was observed in row wise electrode pairs, column wise electrode pairs, interaction maps, and total NMI magnitude maps. When an electrode array was positioned over an IZ changes in the NMI distribution shape were observed around the estimated location of the IZ. Similarly, when noise was introduced to the HD-sEMG recordings the NMI distributions of the noisy electrodes were significantly affected. And, placing the electrode array across two muscles showed that the NMI distributions of the electrodes from each muscle were distinctly dissimilar.   Based on these observations a method for identifying noisy electrodes was developed and tested using artificial data. This method was able to achieve an average accuracy above 90% for most scenarios. Another method was then developed that used the intersections between all NMI distributions to estimate the location of the border between the two muscles. It was able to achieve an average accuracy above 80% during strong contractions with 50% of the false positives being within 10mm of the target. All these results demonstrate that NMI has the potential to be used in a variety of applications outside of the functional connectivity when analysing sEMG signals. Additionally, these observations demonstrate how the NMI can change spatially over a muscle and that the value can change drastically depending on the inter-electrode distance and the electrode's position relative to the muscle