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Open Access Research

BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

Max Ortiz-Catalan12*, Rickard Brånemark2 and Bo Håkansson1

Author Affiliations

1 Department of Signals and Systems, Biomedical Engineering Division, Chalmers University of Technology, Gothenburg, Sweden

2 Centre of Orthopaedic Osseointegration, Department of Orthopaedics, Sahlgrenska University Hospital, Gothenburg, Sweden

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Source Code for Biology and Medicine 2013, 8:11  doi:10.1186/1751-0473-8-11

Published: 18 April 2013

Abstract

Background

Processing and pattern recognition of myoelectric signals have been at the core of prosthetic control research in the last decade. Although most studies agree on reporting the accuracy of predicting predefined movements, there is a significant amount of study-dependent variables that make high-resolution inter-study comparison practically impossible. As an effort to provide a common research platform for the development and evaluation of algorithms in prosthetic control, we introduce BioPatRec as open source software. BioPatRec allows a seamless implementation of a variety of algorithms in the fields of (1) Signal processing; (2) Feature selection and extraction; (3) Pattern recognition; and, (4) Real-time control. Furthermore, since the platform is highly modular and customizable, researchers from different fields can seamlessly benchmark their algorithms by applying them in prosthetic control, without necessarily knowing how to obtain and process bioelectric signals, or how to produce and evaluate physically meaningful outputs.

Results

BioPatRec is demonstrated in this study by the implementation of a relatively new pattern recognition algorithm, namely Regulatory Feedback Networks (RFN). RFN produced comparable results to those of more sophisticated classifiers such as Linear Discriminant Analysis and Multi-Layer Perceptron. BioPatRec is released with these 3 fundamentally different classifiers, as well as all the necessary routines for the myoelectric control of a virtual hand; from data acquisition to real-time evaluations. All the required instructions for use and development are provided in the online project hosting platform, which includes issue tracking and an extensive “wiki”. This transparent implementation aims to facilitate collaboration and speed up utilization. Moreover, BioPatRec provides a publicly available repository of myoelectric signals that allow algorithms benchmarking on common data sets. This is particularly useful for researchers lacking of data acquisition hardware, or with limited access to patients.

Conclusions

BioPatRec has been made openly and freely available with the hope to accelerate, through the community contributions, the development of better algorithms that can potentially improve the patient’s quality of life. It is currently used in 3 different continents and by researchers of different disciplines, thus proving to be a useful tool for development and collaboration.

Keywords:
Prosthetic control; Pattern recognition; Artificial limbs; Myoelectric signals; EMG; Regulatory feedback networks