Comparison of sEMG Onset Detection Methods for Occupational Exoskeletons on Extensive Close-to-Application Data

The design of human-machine interfaces of occupational exoskeletons is essential for their successful application, but at the same time demanding. In terms of information gain, biosensoric methods such as surface electromyography (sEMG) can help to achieve intuitive control of the device, for exampl...

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Bibliographic Details
Published inBioengineering (Basel) Vol. 11; no. 2; p. 119
Main Authors Kreipe, Stefan, Helbig, Thomas, Witte, Hartmut, Schumann, Nikolaus-Peter, Anders, Christoph
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.01.2024
Subjects
Algorithms
Arm
Constant false alarm rate
Customer relations
Electromyography
Evaluation
Exoskeleton
Exoskeletons
Kinematics
Man-machine interfaces
Medical research
Medicine, Experimental
Movement disorders
myoelectric control
Noise sensitivity
occupational exoskeletons
onset detection
Prostheses
Reliability analysis
Signal processing
Signal to noise ratio
surface EMG
Teager-Kaiser-Energy-Operator
Visual signals
Wavelet transforms
Germany
onset detection
Teager-Kaiser-Energy-Operator
constant-false-alarm rate
surface EMG
myoelectric control
occupational exoskeletons
electromechanical delay
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Summary:The design of human-machine interfaces of occupational exoskeletons is essential for their successful application, but at the same time demanding. In terms of information gain, biosensoric methods such as surface electromyography (sEMG) can help to achieve intuitive control of the device, for example by reduction of the inherent time latencies of a conventional, non-biosensoric, control scheme. To assess the reliability of sEMG onset detection under close to real-life circumstances, shoulder sEMG of 55 healthy test subjects was recorded during seated free arm lifting movements based on assembly tasks. Known algorithms for sEMG onset detection are reviewed and evaluated regarding application demands. A constant false alarm rate (CFAR) double-threshold detection algorithm was implemented and tested with different features. Feature selection was done by evaluation of signal-to-noise-ratio (SNR), onset sensitivity and precision, as well as timing error and deviation. Results of visual signal inspection by sEMG experts and kinematic signals were used as references. Overall, a CFAR algorithm with Teager-Kaiser-Energy-Operator (TKEO) as feature showed the best results with feature SNR = 14.48 dB, 91% sensitivity, 93% precision. In average, sEMG analysis hinted towards impending movements 215 ms before measurable kinematic changes.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11020119