Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning

Prahm C, Schulz A, Paaßen B, et al. Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2019;27(5):956-962.Research on machine learning approaches for upper limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge, because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

CMOS Magnetic Sensors for Wearable Magnetomyography

Magnetomyography utilizes magnetic sensors to record small magnetic fields produced by the electrical activity of muscles, which also gives rise to the electromyogram (EMG) signal typically recorded with surface electrodes. Detection and recording of these small fields requires sensitive magnetic sensors possibly equipped with a CMOS readout system. This paper presents a highly sensitive Hall sensor fabricated in a standard 0.18 μm CMOS technology for future low-field MMG applications. Compared with previous works, our experimental results show that the proposed Hall sensor achieves a higher current mode sensitivity of approximately 2400 V/A/mT. Further refinement is required to enable measurement of MMG signals from muscles

Data and Sensor Fusion Using FMG, sEMG and IMU Sensors for Upper Limb Prosthesis Control

Whether someone is born with a missing limb or an amputation occurs later in life, living with this disability can be extremely challenging. The robotic prosthetic devices available today are capable of giving users more functionality, but the methods available to control these prostheses restrict their use to simple actions, and are part of the reason why users often reject prosthetic technologies. Using multiple myography modalities has been a promising approach to address these control limitations; however, only two myography modalities have been rigorously tested so far, and while the results have shown improvements, they have not been robust enough for out-of-lab use. In this work, a novel multi-modal device that allows data to be collected from three myography modalities was created. Force myography (FMG), surface electromyography (sEMG), and inertial measurement unit (IMU) sensors were integrated into a wearable armband and used to collect signal data while subjects performed gestures important for the activities of daily living. An established machine learning algorithm was used to decipher the signals to predict the user\u27s intent/gesture being held, which could be used to control a prosthetic device. Using all three modalities provided statistically-significant improvements over most other modality combinations, as it provided the most accurate and consistent classification results. This work provides justification for using three sensing modalities and future work is suggested to explore this modality combination to decipher more complex actions and tasks with more sophisticated pattern recognition algorithms

Exploring the relationship between EMG feature space characteristics and control performance in machine learning myoelectric control

In myoelectric machine learning (ML) based control, it has been demonstrated that control performance usually increases with training, but it remains largely unknown which underlying factors govern these improvements. It has been suggested that the increase in performance originates from changes in characteristics of the Electromyography (EMG) patterns, such as separability or repeatability. However, the relation between these EMG metrics and control performance has hardly been studied. We assessed the relation between three common EMG feature space metrics (separability, variability and repeatability) in 20 able bodied participants who learned ML myoelectric control in a virtual task over 15 training blocks on 5 days. We assessed the change in offline and real-time performance, as well as the change of each EMG metric over the training. Subsequently, we assessed the relation between individual EMG metrics and offline and real-time performance via correlation analysis. Last, we tried to predict real-time performance from all EMG metrics via L2-regularized linear regression. Results showed that real-time performance improved with training, but there was no change in offline performance or in any of the EMG metrics. Furthermore, we only found a very low correlation between separability and real-time performance and no correlation between any other EMG metric and real-time performance. Finally, real-time performance could not be successfully predicted from all EMG metrics employing L2-regularized linear regression. We concluded that the three EMG metrics and real-time performance appear to be unrelated

Hannes Prosthesis Control Based on Regression Machine Learning Algorithms

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex multi-degrees of freedom (DoFs) movements. However, there is currently a trade-off between the intuitiveness of the control and the number of active DoFs. We here address this challenge by performing simultaneous multi-joint control of the Hannes system and testing several state-of-the-art classifiers to decode hand and wrist movements. The algorithms discriminated multi-DoF movements from forearm EMG signals of 10 healthy subjects reproducing hand opening-closing, wrist flexion-extension and wrist pronation-supination. We first explored the effect of the number of employed EMG electrodes on device performance through the classifiers optimization in terms of F1Score. We further improved classifiers by tuning their respective hyperparameters in terms of the Embedding Optimization Factor. Finally, three mono-lateral amputees tested the optimized algorithms to intuitively and simultaneously control the Hannes system. We found that the algorithms performances were similar to that of healthy subjects, particularly identifying the Non-Linear Regression classifier as the ideal candidate for prosthetic applications

Selected Computing Research Papers Volume 4 June 2015

A Critical Study of Current Natural Language Processing Methods for the Semantic Web (Lee Bodak) .................................................................................................................. 1 A Critical Evaluation on Current Wireless Wearable Sensors in Monitoring of Patients (Mmoloki Gogontle Gontse) ................................................................................... 7 Evaluation on Research targeted towards Worm Viruses and Detection Methods (Adam Keith) ...................................................................................................................... 13 Evaluation of Security Techniques in Cloud Storage Systems (Aone Maenge) ................ 21 An Evaluation of Current Power Management Techniques Used In Mobile Devices (Gabriel Tshepho Masabata) ............................................................................................... 27 An Evaluation Of Current Wide Area Network Cyber Attack Detection Methods Aimed At Improving Computer Security (Hayley Roberts) ............................................... 35 Current EMG Pattern Recognition Research Aimed At Improving Upper Limb Prosthesis Control (Molly Sturman) ................................................................................... 41 Positive and Negative: Effects Video Game Use Can Have on Personality Development (Shaun Watson) ............................................................................................ 4

User training for machine learning controlled upper limb prostheses:a serious game approach

BACKGROUND: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requires separable and consistent electromyogram (EMG) patterns. Whereas user training can improve EMG pattern quality, conventional training methods might limit user potential. Training with serious games might lead to higher quality EMG patterns and better functional outcomes. In this explorative study we compare outcomes of serious game training with conventional training, and machine learning control with the users' own one DoF prosthesis. METHODS: Participants with upper limb absence participated in 7 training sessions where they learned to control a 3 DoF prosthesis with two grips which was fitted. Participants received either game training or conventional training. Conventional training was based on coaching, as described in the literature. Game-based training was conducted using two games that trained EMG pattern separability and functional use. Both groups also trained functional use with the prosthesis donned. The prosthesis system was controlled using a neural network regressor. Outcome measures were EMG metrics, number of DoFs used, the spherical subset of the Southampton Hand Assessment Procedure and the Clothespin Relocation Test. RESULTS: Eight participants were recruited and four completed the study. Training did not lead to consistent improvements in EMG pattern quality or functional use, but some participants improved in some metrics. No differences were observed between the groups. Participants achieved consistently better results using their own prosthesis than the machine-learning controlled prosthesis used in this study. CONCLUSION: Our explorative study showed in a small group of participants that serious game training seems to achieve similar results as conventional training. No consistent improvements were found in either group in terms of EMG metrics or functional use, which might be due to insufficient training. This study highlights the need for more research in user training for machine learning controlled prosthetics. In addition, this study contributes with more data comparing machine learning controlled prosthetics with Direct Controlled prosthetics