Linear regression with frequency division technique for robust simultaneous and proportional myoelectric control during medium and high contraction-level variation.

Myoelectric controlled prostheses systems have evolved significantly over the last few decades, however, there remains a need for more robust systems. Clinically, prosthesis control schemes must be robust to various Electromyography (EMG) signal non-stationarities such as unintended activations and contraction level variations to ensure appropriate operation of the prosthesis. This study compared performance measures between two control schemes, linear regression with frequency division technique (LR-FDT) and standard bandpass processing (LR-Bandpass) for two contraction levels (medium and high) to investigate robustness to EMG non-stationarities. Twenty able-bodied control (14 males and 6 females, age 23.4 ± 3.0) and four individuals with trans-radial amputations performed wrist movements (flexion/extension, rotations and combined movements) in two degrees-of-freedom (DOF) virtual tasks. For control participants, LR-FDT had a mean completion rate (CR) of 95.33%, which was significantly higher than LR-Bandpass with a CR of 64.08% (p<0.001). The clinical participants showed a CR>90% using LR-FDT and had an average CR of 69.8% using LR-Bandpass. LR-FDT method performed significantly better in all other performance indices in at least one movement type. There was no significant difference in the performance of LR-FDT between medium and high contraction levels. This study showed that LR-FDT is advantageous in online myoelectric control as it introduces a more accurate, robust and contraction level invariant control scheme for performing prosthetic hand movements. This study is the first to use frequency-based features with a simultaneous and proportional myoelectric control (SPEC) scheme.