@article{park_berman_dodson_liu_armstrong_huang_kaber_ruiz_zahabi_2023, title={Assessing workload in using electromyography (EMG)-based prostheses}, volume={6}, ISSN={["1366-5847"]}, DOI={10.1080/00140139.2023.2221413}, abstractNote={Using prosthetic devices requires substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross validation were applied to select the most important features and find the optimal models. Classification accuracy, area under the receiver operation characteristic curve (AUC), precision, recall, and F1 scores were calculated to compare models' performance. The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naïve bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. The proposed algorithms can help manufacturers/clinicians predict cognitive workload of future EMG-based prosthetic devices in early design phases.}, journal={ERGONOMICS}, author={Park, Junho and Berman, Joseph and Dodson, Albert and Liu, Yunmei and Armstrong, Matthew and Huang, He and Kaber, David and Ruiz, Jaime and Zahabi, Maryam}, year={2023}, month={Jun} }
 @article{zhang_lambeth_sun_dodson_bao_sharma_2023, title={Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton}, volume={2}, ISSN={["1941-0468"]}, url={https://doi.org/10.1109/TRO.2023.3236958}, DOI={10.1109/TRO.2023.3236958}, abstractNote={This article presents an assist-as-needed (AAN) control framework for exoskeleton assistance based on human volitional effort prediction via a Hill-type neuromuscular model. A sequential processing algorithm-based multirate observer is applied to continuously estimate muscle activation levels by fusing surface electromyography (sEMG) and ultrasound (US) echogenicity signals from the ankle muscles. An adaptive impedance controller manipulates the exoskeleton's impedance for a more natural behavior by following a desired intrinsic impedance model. Two neural networks provide robustness to uncertainties in the overall ankle joint-exoskeleton model and the prediction error in the volitional ankle joint torque. A rigorous Lyapunov-based stability analysis proves that the AAN control framework achieves uniformly ultimately bounded tracking for the overall system. Experimental studies on five participants with no neurological disabilities walking on a treadmill validate the effectiveness of the designed ankle exoskeleton and the proposed AAN approach. Results illustrate that the AAN control approach with fused sEMG and US echogenicity signals maintained a higher human volitional effort prediction accuracy, less ankle joint trajectory tracking error, and less robotic assistance torque than the AAN approach with the sEMG-based volitional effort prediction alone. The findings support our hypotheses that the proposed controller increases human motion intent prediction accuracy, improves the exoskeleton's control performance, and boosts voluntary participation from human subjects. The new framework potentially paves a foundation for using multimodal biological signals to control rehabilitative or assistive robots.}, journal={IEEE TRANSACTIONS ON ROBOTICS}, author={Zhang, Qiang and Lambeth, Krysten and Sun, Ziyue and Dodson, Albert and Bao, Xuefeng and Sharma, Nitin}, year={2023}, month={Feb} }