Gesture recognition for transhumeral prosthesis control using EMG and NIR

IET Cyber-Syst and Robotics

Samuel

Asogbon

et al. 2021

Self Cite

In recent years, the electroencephalography (EEG) brain-computer interface (BCI) has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive electrodes. This is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography (EMG) directly from the stump, which possesses residual limb anatomy post-amputation. In this study, the sequential forward selection algorithm to form a 10-optimal-channel representation, alongside an extended signal feature vector was applied, to investigate the motion intent decoding performance of EMG-only, EEG-only, and a fused EMG-EEG sensing configuration for four transhumeral amputees with varying stump lengths. The results showed a considerable improvement for the EMG-only configuration with the advanced feature vector, but only a small increase for the EEG-only, and thus a marginal improvement when information from both signals was fused together. This is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent decoding. Further work will now involve optimisation studies to find a greater representation of electrode representation and parsimony, to minimise the number of channels while boosting motion intent decoding accuracy. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Section: Feature Extractionmentioning

confidence: 99%

“…The fourth order AR model was used in this case, as was seen previously with the Akaike Information Criterion, and the coefficients were used as input features [27]. A mathematical structure of the AR model can be seen in Nsugbe et al [4].…”

Section: Predictive and Non-linear Complexity Featuresmentioning

confidence: 99%

Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

IET Cyber-Syst and Robotics

Samuel

Asogbon

et al. 2021

Self Cite

“…EMG signals are superimposed electrical signal representations of action potentials from motor neurons which are dependent on the physiological and anatomical properties of an individual [3,5]. Using dipole theory, an EMG signal can be mathematically modelled as a continuous extracellular action potential from a multiple-source dipole, as seen in equation 1 [6]:…”

Section: Electromyography (Emg)mentioning

confidence: 99%

“…The EMG instrumentation used for data acquisition by Li et al [5] was the Refa 128 high-density electrodes by TMS International BV, Netherlands, with 32 electrode channels [3]. The acquisition electronics comprised a bandpass filter in the 10-500 Hz frequency range, 24-bit resolution and a sample rate of 1024 Hz [3].…”

Section: -Emg Sensorsmentioning

confidence: 99%

“…As per Fougner et al [2], a number of control schemes have been trialled for the control of the myoelectric limb, and the pattern recognition scheme has been viewed as the favoured control method due to the level of intuitiveness which it affords its users. An artificial intelligence classifier is typically employed as part of this exercise and is usually calibrated using the favoured supervised learning framework, which is based on an iterative optimisation sequence where an algorithm learns the best internal model configuration that allows it to best identify labelled examples of the dataset for which it is being calibrated [3]. The shortcomings of the supervised learning framework include the lag-time induced by the reliance on an 'expert in loop' for tasks such as labelling of the training set, which requires more expert knowledge depending on the format and size of the training data, in addition to associated computational training times [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Self-Learning and Adaptive Control Scheme for Phantom Prosthesis Control Using Combined Neuromuscular and Brain-Wave Bio-Signals

7th International Electronic Conference on Sensors and Applications

Samuel

2020

Self Cite

The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user’s intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a three-phase identification framework is formulated to design a control architecture capable of self-learning patterns from bio-signal inputs from electromyography (neuromuscular) and electroencephalography (brain wave) biosensors, for a transhumeral amputee case study. The results show that the designed self-learning framework can help reduce lead time in prosthesis control interface customisation, and can also be extended as an adaptive control scheme to minimise the performance degradation of the prosthesis controller.

Towards an affordable magnetomyography instrumentation and low model complexity approach for labour imminency prediction using a novel multiresolution analysis

Sanusi

2021

Applied AI Letters

Self Cite

The ability to predict the onset of labour is seen to be an important tool in a clinical setting. Magnetomyography has shown promise in the area of labour imminency prediction, but its clinical application remains limited due to high resource consumption associated with its broad number of channels. In this study, five electrode channels, which account for 3.3% of the total, are used alongside a novel signal decomposition algorithm and low complexity classifiers (logistic regression and linear‐SVM) to classify between labour imminency due within 0 to 48 hours and >48 hours. The results suggest that the parsimonious representation comprising of five electrode channels and novel signal decomposition method alongside the candidate classifiers could allow for greater affordability and hence clinical viability of the magnetomyography‐based prediction model, which carries a good degree of model interpretability. The results showed around a 20% increase on average for the novel decomposition method, alongside a reduced group of features across the various classification metrics considered for both the logistic regression and support vector machine.