FPGA-based acceleration of high density myoelectric signal processing

Boschmann, Alexander; Agne, Andreas; Witschen, Linus; Thombansen, Georg; Kraus, Florian; Platzner, Marco

doi:10.1109/reconfig.2015.7393312

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Reference [21] instead of measuring the threshold with the absolute value of the difference of the samples, uses only the absolute value of the current sample.…”

Section: ) Methodsmentioning

confidence: 99%

A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

2020

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Zero crossings are a practical and efficient feature to approximate the frequency of a sampled series of data. Some research describes in different ways how to compute the zero crossings feature starting from its definition, and in some of them, a threshold is included as part of it. This research compiles a comprehensive list of description methods for zero crossings, both with or without threshold. In addition, an improvement of one method is proposed, mainly to save time resources. Moreover, it increases the precision when the objective is to perform some classification. This feature is often used as a vector of a matrix of features in signal classification. To test the different variations of the zero crossings methods, a classification of electromyographic signals was performed using support vector machines. The results obtained by the proposed method threw near to a 40% improvement in the classification compared to those approaches that do not consider a threshold and more than 7% compared to those with a threshold. The processing time of this work is shortened compared to others that also take into account a threshold.

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“…Reference [21] instead of measuring the threshold with the absolute value of the difference of the samples, uses only the absolute value of the current sample.…”

Section: ) Methodsmentioning

confidence: 99%

A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

2020

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“…Approaches using field-programmable gate arrays (FPGA) have shown to be highly beneficial for accelerating the computation of pattern recognition algorithms [26], [27]. FPGAs represent a valuable solution for prosthetic control which is predestined to appear in future embedded MPR systems, but currently no clinical implementation of such system has been reported.…”

Section: Background and Related Workmentioning

confidence: 99%

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant

Mastinu

Doguet²,

Botquin³

et al. 2017

IEEE Trans. Biomed. Circuits Syst.

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Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

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“…It was able to perform real-time autoregressive analysis for motions classification and to properly actuate a robotic device. The feasibility of embedded MPR systems has been confirmed more recently by others [4] – [8] , albeit not tested in daily life outside controlled environments. To date, there is only a commercially available MPR system (Complete Control, COAPT).…”

Section: Background and Related Workmentioning

confidence: 91%

An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject

Mastinu¹,

Ahlberg²,

Lendaro³

et al. 2018

IEEE J. Transl. Eng. Health Med.

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The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases.

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FPGA-based acceleration of high density myoelectric signal processing

Cited by 11 publications

References 19 publications

A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant

An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject

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