Evaluation of generic EMG-Torque models across two Upper-Limb joints.

Advanced single-use dynamic EMG-torque models require burdensome subject-specific calibration contractions and have historically been assumed to produce lower error than generic models (i.e., models that are identical across subjects and muscles). To investigate this assumption, we studied generic one degree of freedom (DoF) models derived from the ensemble median of subject-specific models, evaluated across subject, DoF and joint. We used elbow (N = 64) and hand-wrist (N = 9) datasets. Subject-specific elbow models performed statistically better [5.79 ± 1.89 %MVT (maximum voluntary torque) error] than generic elbow models (6.21 ± 1.85 %MVT error). However, there were no statistical differences between subject-specific vs. generic models within each hand-wrist DoF. Next, we evaluated generic models across joints. The best hand-wrist generic model had errors of 6.29 ± 1.85 %MVT when applied to the elbow. The elbow generic model had errors of 7.04 ± 2.29 %MVT when applied to the hand-wrist. The generic elbow model was statistically better in both joints, compared to the generic hand-wrist model. Finally, we tested Butterworth filter models (a simpler generic model), finding no statistical differences between optimum Butterworth and subject-specific models. Overall, generic models simplified EMG-torque training without substantive performance degradation and provided the possibility of transfer learning between joints.

Data Management for Transfer Learning Approaches to Elbow EMG-Torque Modeling.

The performance of single-use subject-specific electromyogram (EMG)-torque models degrades significantly when used on a new subject, or even the same subject on a second day. Improving the generalization performance of models is essential but challenging. In this work, we investigate how data management strategies contribute to the performance of elbow joint EMG-torque models in cross-subject evaluation. Data management can be divided into two parts, namely data acquisition and data utilization. For data acquisition, analysis of data from 65 subjects shows that training set data diversity (number of subjects) is more important than data size (total data duration). For data utilization, we propose a correlation-based data weighting (COR-W) method for model calibration which is unsupervised in the modeling stage. We first evaluated the domain shift level between data in each training trial (source domain) and data acquired from a new subject (target domain) via the mismatch of feature correlation, using only EMG signals in the target domain without the synchronized torque values (hence unsupervised during model training). Data weights were assigned to each training trial according to different domain shift levels. The weighted least squares method using the obtained data weights was then employed to develop a calibrated EMG-torque model for the new subject. The COR-W method can achieve a low root mean square error (9.29% maximum voluntary contraction) in cross-subject evaluation, with significant performance improvement compared to models without calibration. Both the data acquisition and utilization strategies contribute to the performance of EMG-torque models in cross-subject evaluation.

Efficiently Training Two-DoF Hand-Wrist EMG-Force Models.

Single-use EMG-force models (i.e., a new model is trained each time the electrodes are donned) are used in various areas, including ergonomics assessment, clinical biomechanics, and motor control research. For one degree of freedom (1-DoF) tasks, input-output (black box) models are common. Recently, black box models have expanded to 2-DoF tasks. To facilitate efficient training, we examined parameters of black box model training methods in 2-DoF force-varying, constant-posture tasks consisting of hand open-close combined with one wrist DoF. We found that approximately 40-60 s of training data is best, with progressively higher EMG-force errors occurring for progressively shorter training durations. Surprisingly, 2-DoF models in which the dynamics were universal across all subjects (only channel gain was trained to each subject) generally performed 15-21% better than models in which the complete dynamics were trained to each subject. In summary, lower error EMG-force models can be formed through diligent attention to optimization of these factors.

Estimating Individual and Combined Fingertip Forces From Forearm EMG During Constant-Pose, Force-Varying Tasks.

Numerous applications in areas such as ergonomics assessment, clinical biomechanics and motor control research would benefit from accurately modeling the relationship between forearm EMG and fingertip force, using conventional electrodes. Herein, we describe a methodological study of relating 12 conventional surface EMGs, applied circumferentially about the forearm, to fingertip force during constant-pose, force-varying (dynamic) contractions. We studied independent contraction of one, two, three or four fingers (thumb excluded), as well as contraction of four fingers in unison. Using regression, we found that a pseudo-inverse tolerance (ratio of largest to smallest singular value) of 0.01 was optimal. Lower values produced erratic models and higher values produced models with higher errors. EMG-force errors using one finger ranged from 2.5-3.8% maximum voluntary contraction (MVC), using the optimal pseudo-inverse tolerance. With additional fingers (two, three or four), the average error ranged from 5-8 %MVC. When four fingers contracted in unison, the average error was 4.3 %MVC.

Comparison of Constant-Posture Force-Varying EMG-Force Dynamic Models About the Elbow.

Numerous techniques have been used to minimize error in relating the surface electromyogram (EMG) to elbow joint torque. We compare the use of three techniques to further reduce error. First, most EMG-torque models only use estimates of EMG standard deviation as inputs. We studied the additional features of average waveform length, slope sign change rate and zero crossing rate. Second, multiple channels of EMG from the biceps, and separately from the triceps, have been combined to produce two low-variance model inputs. We contrasted this channel combination with using each EMG separately. Third, we previously modeled nonlinearity in the EMG-torque relationship via a polynomial. We contrasted our model versus that of the classic exponential power law of Vredenbregt and Rau (1973). Results from 65 subjects performing constant-posture, force-varying contraction gave a "baseline" comparison error (i.e., error with none of the new techniques) of 5.5 ± 2.3% maximum flexion voluntary contraction (%MVCF). Combining the techniques of multiple features with individual channels reduced error to 4.8 ± 2.2 %MVCF, while combining individual channels with the power-law model reduced error to 4.7 ± 2.0 %MVCF. The new techniques further reduced error from that of the baseline by ≈ 15 %.