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Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Here, we present ultra-low-power digital signal processing algorithms for an insulated EMG sensor which couples the EMG signal capacitively. These sensors require neither conductive connection to the skin nor electrolytic paste or skin preparation. Capacitive sensors allow straightforward application. However, they make a sophisticated signal amplification and noise suppression necessary. A low-cost sensor has been developed for real-time myoelectric prostheses control. The major hurdles in measuring the EMG are movement artifacts and external noise. We designed various digital filters to attenuate this noise. Optimal system setup and filter parameters for the trade-off between attenuation of this noise and sufficient EMG signal power for high signal quality were investigated. Additionally, an algorithm for movement artifact suppression, enabling robust application in real-world environments, is presented. The algorithms, which require minimal calculation resources and memory, are implemented on an ultra-low-power microcontroller.

INPUT (Intuitive Natural Prosthesis UTilization) Project resentation poster

Missing an upper limb dramatically impairs daily-life activities. Significant efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective, we focus on myocontrol algorithms for upper limb prostheses and we emphasize that one relevant factor determining the relatively small clinical impact of these methods is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively substantiate this argument with data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reached relatively high classification accuracy offline, their clinical scores were largely different and were not strongly predicted by classification accuracy. As argued in previous reports, we reinforce the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field.

Myoelectric prostheses use electromyography (EMG) signals to control the movements of the prosthesis. EMG-signals are electric potentials on the skin which originate from voluntarily contracted muscles within a person’s residual limb. Thus prostheses of this type utilize the residual neuro-muscular system of the human body to control the functions of an electrically powered prosthesis. Standard measurements are done using conductive electrodes on the skin surface. For technical reasons a capacitive coupling of the EMG to the prosthesis control would be preferable. To design optimal settings of the sensors, a detailed knowledge of the temporal electric potential distribution is vital. Here we show the simulation of the EMG using finite elements employing COMSOL based on MRI data. Then a node-based approach in MATLAB was derived and the comparison with the FE-results show that this approach yields excellent results and offers the advantage of high speed computation which allows for optimization of the sensor geometry. The simulation results were verified using measurements on volunteers showing that indeed our model assumptions and simplifications made are valid. The developed nodal analysis model enables fast and simple determination of the optimal prostheses-sensor geometry for the individual amputee.

Electrocutaneous stimulation is a promising approach to provide sensory feedback to amputees, and thus close the loop in upper limb prosthetic systems. However, the stimulation introduces artifacts in the recorded electromyographic (EMG) signals, which may be detrimental for the control of myoelectric prostheses. In this study, artifact blanking with three data segmentation approaches was investigated as a simple method to restore the performance of pattern recognition in prosthesis control (eight motions) when EMG signals are corrupted by stimulation artifacts. The methods were tested over a range of stimulation conditions and using four feature sets, comprising both time and frequency domain features. The results demonstrated that when stimulation artifacts were present, the classification performance improved with blanking in all tested conditions. In some cases, the classification performance with blanking was at the level of the benchmark (artifact-free data). The greatest pulse duration and frequency that allowed a full performance recovery were 400 $\mu{\rm s}$ and 150 Hz, respectively. These results show that artifact blanking can be used as a practical solution to eliminate the negative influence of the stimulation artifact on EMG pattern classification in a broad range of conditions, thus allowing to close the loop in myoelectric prostheses using electrotactile feedback.

As motion artefacts are a major problem with electromyography sensors, a new algorithm is developed to differentiate artefacts to contraction EMG. The performance of myoelectric prosthesis is increased with this algorithm. The implementation is done for an ultra-low-power microcontroller with limited calculation resources and memory. Short Time Fourier Transformation is used to enable real-time application. The sum of the differences (SOD) of the currently measured EMG to a reference contraction EMG is calculated. The SOD is a new parameter introduced for EMG classification. The satisfactory error rates are determined by measurements done with the capacitively coupling EMG prototype, recently developed by the research group.

Upper limb myoelectric prostheses drives are controlled by muscle signal, generated by electrochemical effects in the tissue. State of the art electrodes use the conductive measurement principle for acquiring the signal to control these active prostheses. Some problems, resulting from the necessary conductive skin contact, are associated with the state of the art electrodes. For this reason, a capacitive electromyography (EMG) prototype, consisting of a flexible sensor and measurement electronics, was developed. The electronic circuit, used for signal amplification and filtering, is described. An ultra-low-power microcontroller was used for the implementation of algorithms for EMG signal processing. DSP algorithms are optimized for real time processing and minimal computing power. Muscle signals, measured with this prototype, are presented. The developed capacitive EMG system shall enable a better quality of life and give more independence to amputees.

We review recent studies that aimed at designing an intuitive and robust myoelectric control system for transradial amputees. The methods developed assume that the forearm muscles are controlled in a synergistic manner and capture this synergistic structure hidden in the electromyographic signal patterns by factorization algorithms. We have shown that this system is capable of providing robust control over multiple degrees of freedom relying on 6 electrodes only and that it is robust to electrode shift. However, a pure factorization approach may result in some unwanted movements when the user is willing to activate only one function, which is mitigated by combining a synergistic controller with pattern recognition.

Critical soft tissue injuries may lead to a non-functional and insensate limb. In these cases standard reconstructive techniques will not suffice to provide a useful outcome, and solutions outside the biological arena must be considered and offered to these patients. We propose a concept which, after all reconstructive options have been exhausted, involves an elective amputation along with a bionic substitution, implementing an actuated prosthetic hand via a structured tech-neuro-rehabilitation program. Here, three patients are presented in whom this concept has been successfully applied after mutilating hand injuries. Clinical tests conducted before, during and after the procedure, evaluating both functional and psychometric parameters, document the benefits of this approach. Additionally, in one of the patients, we show the possibility of implementing a highly functional and natural control of an advanced prosthesis providing both proportional and simultaneous movements of the wrist and hand for completing tasks of daily living with substantially less compensatory movements compared to the traditional systems. It is concluded that the proposed procedure is a viable solution for re-gaining highly functional hand use following critical soft tissue injuries when existing surgical measures fail. Our results are clinically applicable and can be extended to institutions with similar resources.

Research conducted over the last decades indicates a necessity of having larger number of EMG sensors in order to extract sufficient information needed for natural control of upper limb prosthetics. Various studies have addressed this issue, though clinical transition and evaluation of such systems on a larger pool of patients is still missing. We propose a specifically designed system which allows users to perform clinically relevant tests in an unobstructed way while handling dexterous prosthesis. Eight electrodes were embedded into customized sockets along with the controllers driving an algorithm recently tested in laboratory conditions that allows simultaneous manipulation of four out of seven prosthetic functions. The fully self-contained system was evaluated on seven amputees conducting the Southampton Hand Assessment Procedure. The scores achieved were compared to those obtained using their own commercial devices. The study shows the necessary steps to validate novel control algorithms in a clinically meaningful context.