@article{hong_zhao_berman_chi_li_huang_yin_2023, title={Angle-programmed tendril-like trajectories enable a multifunctional gripper with ultradelicacy, ultrastrength, and ultraprecision}, volume={14}, ISSN={["2041-1723"]}, DOI={10.1038/s41467-023-39741-6}, abstractNote={Abstract}, number={1}, journal={NATURE COMMUNICATIONS}, author={Hong, Yaoye and Zhao, Yao and Berman, Joseph and Chi, Yinding and Li, Yanbin and Huang, He and Yin, Jie}, year={2023}, month={Aug} }
 @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{berman_hinson_lee_huang_2023, title={Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface}, volume={70}, ISSN={["1558-2531"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85139470198&partnerID=MN8TOARS}, DOI={10.1109/TBME.2022.3210892}, abstractNote={Objective: In this study, we aimed to develop a novel electromyography (EMG)-based neural machine interface (NMI), called the Neural Network-Musculoskeletal hybrid Model (N2M2), to decode continuous joint angles. Our approach combines the concepts of machine learning and musculoskeletal modeling. Methods: We compared our novel design with a musculoskeletal model (MM) and 2 continuous EMG decoders based on artificial neural networks (ANNs): multilayer perceptrons (MLPs) and nonlinear autoregressive neural networks with exogenous inputs (NARX networks). EMG and joint kinematics data were collected from 10 non-disabled and 1 transradial amputee subject. The offline performance tested across 3 different conditions (i.e., varied arm postures, shifted electrode locations, and noise-contaminated EMG signals) and online performance for a virtual postural matching task was quantified. Finally, we implemented the N2M2 to operate a prosthetic hand and tested functional task performance. Results: The N2M2 made more accurate predictions than the MLP in all postures and electrode locations (p < 0.003). For estimated MCP joint angles, the N2M2 was less sensitive to noisy EMG signals than the MM or NARX network with respect to error (p < 0.032) as well as the NARX network with respect to correlation (p = 0.007). Additionally, the N2M2 had better online task performance than the NARX network (p ≤ 0.030). Conclusion: Overall, we have found that combining the concepts of machine learning and musculoskeletal modeling has resulted in a more robust joint kinematics decoder than either concept individually. Significance: The outcome of this study may result in a novel, highly reliable controller for powered prosthetic hands.}, number={4}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Berman, Joseph and Hinson, Robert and Lee, I-Chieh and Huang, He}, year={2023}, month={Apr}, pages={1125–1136} }
 @article{hinson_berman_lee_filer_huang_2023, title={Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application}, volume={31}, ISSN={["1558-0210"]}, DOI={10.1109/TNSRE.2023.3297448}, abstractNote={There has been controversy about the value of offline evaluation of EMG-based neural-machine interfaces (NMIs) for their real-time application. Often, conclusions have been drawn after studying the correlation of the offline EMG decoding accuracy/error with the NMI user’s real-time task performance without further considering other important human performance metrics such as adaptation rate, cognitive load, and physical effort. To fill this gap, this study aimed to investigate the relationship between the offline decoding accuracy of EMG-based NMIs and user adaptation, cognitive load, and physical effort in real-time NMI use. Twelve non-disabled subjects participated in this study. For each subject, we established three EMG decoders that yielded different offline accuracy (low, moderate, and high) in predicting continuous hand and wrist motions. The subject then used each EMG decoder to perform a virtual hand posture matching task in real time with and without a secondary task as the evaluation trials. Results showed that the high-level offline performance decoders yield the fastest adaptation rate and highest posture matching completion rate with the least muscle effort in users during online testing. A secondary task increased the cognitive load and reduced real-time virtual task competition rate for all the decoders; however, the decoder with high offline accuracy still produced the highest task completion rate. These results imply that the offline performance of EMG-based NMIs provide important insight to users’ abilities to utilize them and should play an important role in research and development of novel NMI algorithms.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Hinson, Robert M. and Berman, Joseph and Lee, I-Chieh and Filer, William G. and Huang, He}, year={2023}, pages={3055–3063} }
 @article{hinson_berman_filer_kamper_hu_huang_2023, title={Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-Time Operation of Electromyography-Based Neural-Machine Interfaces}, volume={31}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85144812628&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2022.3226229}, abstractNote={There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R2 ≈ 0.4), moderate (R2 ≈ 0.6), and high ( $\text {R}^{\vphantom {\text {D}^{\text {a}}}{2}} \approx 0.8$ ) offline performance levels. Twelve non-disabled subjects trained with each offline performance level decoder before evaluating final real-time posture matching performance. Moderate to strong relationships were detected between offline performance and all real-time task performance metrics: task completion percentage (r = 0.66, p < 0.001), normalized task completion time (r = −0.51, p = 0.001), path efficiency (r = 0.74, p < 0.001), and target overshoots (r = −0.79, p < 0.001). Significant improvements in each real-time task evaluation metric were also observed between the different offline performance levels. Additionally, subjects rated myoelectric controllers with higher offline performance more favorably. The results of this study support the use and validity of offline analyses for optimization of NMIs in myoelectric control research and development.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Hinson, Robert M. and Berman, Joseph and Filer, William and Kamper, Derek and Hu, Xiaogang and Huang, He}, year={2023}, pages={680–689} }
 @article{berman_hinson_huang_2021, title={Comparing Reinforcement Learning Agents and Supervised Learning Neural Networks for EMG-Based Decoding of Continuous Movements}, ISSN={["1558-4615"]}, url={http://dx.doi.org/10.1109/embc46164.2021.9630744}, DOI={10.1109/EMBC46164.2021.9630744}, abstractNote={Recent work on electromyography (EMG)-based decoding of continuous joint kinematics has included model-based approaches, such as musculoskeletal modeling, as well as model-free approaches such as supervised learning neural networks (SLNN). This study aimed to present a new kinematics decoding framework based on reinforcement learning (RL), which combines machine learning and model-based approaches together. We compared the performance and robustness of our new method with those of the SLNN approach. EMG and kinematic data were collected from 5 able-bodied subjects while they performed flexion and extension of the metacarpophalangeal (MCP) and wrist joints simultaneously at both a slow and fast tempo. The data were used to train an RL agent and a SLNN for each of the 2 tempos. All the trained agents and SLNNs were tested with both fast and slow kinematic data. Pearson’s correlation coefficient (r) and normalized root mean square error (NRMSE) between measured and estimated joint angles were used to determine performance. Our results suggest that the RL-based kinematics decoder is more robust to changes in movement speeds between training and testing data and has better performance than the SLNN.}, journal={2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)}, publisher={IEEE}, author={Berman, Joseph and Hinson, Robert and Huang, He}, year={2021}, pages={6297–6300} }