@article{pan_huang_2021, title={A robust model-based neural-machine interface across different loading weights applied at distal forearm}, volume={67}, ISSN={["1746-8108"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85101330574&partnerID=MN8TOARS}, DOI={10.1016/j.bspc.2021.102509}, abstractNote={Musculoskeletal models (MMs) have recently been proposed to decode electromyography (EMG) signals for movement intent recognition. Since the robustness is critical to retain the performance of neural-machine interface (NMI) during daily activities and the loading weight change is one of the critical factors that would affect the performance of NMI, this study aimed to further investigate the robustness of a generic MM-based NMI across different loading conditions. Eight able-bodied (AB) individuals and one individual with a transradial amputation were recruited and tested while performing a real-time virtual wrist/hand posture matching task under different loading weights (AB subjects: 0 kg, 0.567 kg, and 1.134 kg; amputee subject: 0 kg and 0.567 kg) applied at the distal forearm. All tasks were achieved by both AB individuals and the individual with the transradial amputation. There was no significant difference among the real-time performance (completion time, the number of overshoots, and path efficiency) of AB individuals under different loading conditions. We calculated the average muscle activations of each muscle during the initial 0.5 s and last 0.5 s respectively for each target across all subjects and trials. The analysis of muscle activations showed that additional weights caused muscle co-contractions. However, the subjects can cope with the increased muscle co-activation level, modifying muscle activation patterns, and still complete tasks successfully. We obtained similar results from the individual with the transradial amputation. These results demonstrated the robustness of MM-based NMI across different loading conditions. The outcomes indicate the potential of the multi-user NMI toward practical applications.}, journal={BIOMEDICAL SIGNAL PROCESSING AND CONTROL}, author={Pan, Lizhi and Huang, He}, year={2021}, month={May} }
 @article{stallrich_islam_staicu_crouch_pan_huang_2020, title={OPTIMAL EMG PLACEMENT FOR A ROBOTIC PROSTHESIS CONTROLLER WITH SEQUENTIAL, ADAPTIVE FUNCTIONAL ESTIMATION (SAFE)}, volume={14}, ISSN={["1932-6157"]}, url={http://dx.doi.org/10.1214/20-aoas1324}, DOI={10.1214/20-AOAS1324}, abstractNote={Robotic hand prostheses require a controller to decode muscle contraction information, such as electromyogram (EMG) signals, into the user’s desired hand movement. State-of-the-art decoders demand extensive training, require data from a large number of EMG sensors, and are prone to poor predictions. Biomechanical models of a single movement degree-of-freedom tell us that relatively few muscles, and hence fewer EMG sensors, are needed to predict movement. We propose a novel decoder based on a dynamic, functional linear model with velocity or acceleration as its response and the recent past EMG signals as functional covariates. The effect of each EMG signal varies with the recent position to account for biomechanical features of hand movement, increasing the predictive capability of a single EMG signal compared to existing decoders. The effects are estimated with a multi-stage, adaptive estimation procedure we call Sequential Adaptive Functional Estimation (SAFE). Starting with 16 potential EMG sensors, our method correctly identifies the few EMG signals that are known to be important for an able-bodied subject. Furthermore, the estimated effects are interpretable and can significantly improve understanding and development of robotic hand prostheses.}, number={3}, journal={ANNALS OF APPLIED STATISTICS}, publisher={Institute of Mathematical Statistics}, author={Stallrich, Jonathan and Islam, Md Nazmul and Staicu, Ana-Maria and Crouch, Dustin and Pan, Lizhi and Huang, He}, year={2020}, month={Sep}, pages={1164–1181} }
 @article{pan_crouch_huang_2019, title={Comparing EMG-Based Human-Machine Interfaces for Estimating Continuous, Coordinated Movements}, volume={27}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85073667144&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2019.2937929}, abstractNote={Electromyography (EMG)-based interfaces are trending toward continuous, simultaneous control with multiple degrees of freedom. Emerging methods range from data-driven approaches to biomechanical model-based methods. However, there has been no direct comparison between these two types of continuous EMG-based interfaces. The aim of this study was to compare a musculoskeletal model (MM) with two data-driven approaches, linear regression (LR) and artificial neural network (ANN), for predicting continuous wrist and hand motions for EMG-based interfaces. Six able-bodied subjects and one transradial amputee subject performed (missing) metacarpophalangeal (MCP) and wrist flexion/extension, simultaneously or independently, while four EMG signals were recorded from forearm muscles. To add variation to the EMG signals, the subjects repeated the MCP and wrist motions at various upper extremity postures. For each subject, the EMG signals collected from the neutral posture were used to build the EMG interfaces; the EMG signals collected from all postures were used to evaluate the interfaces. The performance of the interface was quantified by Pearson’s correlation coefficient (r) and the normalized root mean square error (NRMSE) between measured and estimated joint angles. The results demonstrated that the MM predicted movements more accurately, with higher r values and lower NRMSE, than either LR or ANN. Similar results were observed in the transradial amputee. Additionally, the variation in r across postures, an indicator of reliability against posture changes, was significantly lower (better) for the MM than for either LR or ANN. Our findings suggest that incorporating musculoskeletal knowledge into EMG-based human-machine interfaces could improve the estimation of continuous, coordinated motion.}, number={10}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Pan, Lizhi and Crouch, Dustin L. and Huang, He}, year={2019}, month={Oct}, pages={2145–2154} }
 @article{crouch_pan_filer_stallings_huang_2018, title={Comparing Surface and Intramuscular Electromyography for Simultaneous and Proportional Control Based on a Musculoskeletal Model: A Pilot Study}, volume={26}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85050633647&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2018.2859833}, abstractNote={Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for controlling many degrees of freedom). However, differences in signal characteristics between sEMG and iEMG may influence SPC performance. We conducted a pilot study to determine the effect of electrode type (sEMG and iEMG) on real-time task performance with SPC based on a novel 2-degree-of-freedom EMG-driven musculoskeletal model of the wrist and hand. Four able-bodied subjects and one transradial amputee performed a virtual posture matching task with either sEMG or iEMG. There was a trend of better task performance with sEMG than iEMG for both able-bodied and amputee subjects, though the difference was not statistically significant. Thus, while iEMG may permit targeted recording of EMG, its signal characteristics may not be as ideal for SPC as those of sEMG. The tradeoff between recording specificity and signal characteristics is an important consideration for development and clinical implementation of SPC for neural-machine interfaces.}, number={9}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Crouch, Dustin L. and Pan, Lizhi and Filer, William and Stallings, Jonathan W. and Huang, He}, year={2018}, month={Sep}, pages={1735–1744} }
 @article{pan_crouch_huang_2018, title={Myoelectric Control Based on a Generic Musculoskeletal Model: Toward a Multi-User Neural-Machine Interface}, volume={26}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85047202264&partnerID=MN8TOARS}, DOI={10.1109/tnsre.2018.2838448}, abstractNote={This paper aimed to develop a novel electromyography (EMG)-based neural-machine interface (NMI) that is user-generic for continuously predicting coordinated motion betweenmuscle contractionmetacarpophalangeal (MCP) and wrist flexion/extension. The NMI requires a minimum calibration procedure that only involves capturing maximal voluntary muscle contraction for themonitoredmuscles for individual users. At the center of the NMI is a user-generic musculoskeletal model based on the experimental data collected from six able-bodied (AB) subjects and nine different upper limb postures. The generic model was evaluated on-line on both AB subjects and a transradial amputee. The subjectswere instructed to performa virtual hand/wrist posture matching task with different upper limb postures. The on-line performanceof the genericmodelwas also compared with that of the musculoskeletal model customized to each individual user (called “specific model”). All subjects accomplished the assigned virtual tasks while using the user-generic NMI, although the AB subjects produced better performance than the amputee subject. Interestingly, compared with the specific model, the generic model produced comparable completion time, a reduced number of overshoots, and improved path efficiency in the virtual hand/wrist posture matching task. The results suggested that it is possible to design an EMG-driven NMI based on a musculoskeletalmodelthat could fit multiple users, including upper limb amputees, for predicting coordinated MCP and wrist motion. The present new method might address the challenges of existing advanced EMG-based NMI that require frequent and lengthy customization and calibration. Our future research will focus on evaluating the developed NMI for powered prosthetic arms.}, number={7}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Pan, Lizhi and Crouch, Dustin L. and Huang, He}, year={2018}, month={Jul}, pages={1435–1442} }