@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_huang_2017, title={Musculoskeletal model-based control interface mimics physiologic hand dynamics during path tracing task}, volume={14}, ISSN={["1741-2552"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020447492&partnerID=MN8TOARS}, DOI={10.1088/1741-2552/aa61bc}, abstractNote={Objective. We investigated the feasibility of a novel, customizable, simplified EMG-driven musculoskeletal model for estimating coordinated hand and wrist motions during a real-time path tracing task. Approach. A two-degree-of-freedom computational musculoskeletal model was implemented for real-time EMG-driven control of a stick figure hand displayed on a computer screen. After 5–10 minutes of undirected practice, subjects were given three attempts to trace 10 straight paths, one at a time, with the fingertip of the virtual hand. Able-bodied subjects completed the task on two separate test days. Main results. Across subjects and test days, there was a significant linear relationship between log-transformed measures of accuracy and speed (Pearson’s r  =  0.25, p  <  0.0001). The amputee subject could coordinate movement between the wrist and MCP joints, but favored metacarpophalangeal joint motion more highly than able-bodied subjects in 8 of 10 trials. For able-bodied subjects, tracing accuracy was lower at the extremes of the model’s range of motion, though there was no apparent relationship between tracing accuracy and fingertip location for the amputee. Our result suggests that, unlike able-bodied subjects, the amputee’s motor control patterns were not accustomed to the multi-joint dynamics of the wrist and hand, possibly as a result of post-amputation cortical plasticity, disuse, or sensory deficits. Significance. To our knowledge, our study is one of very few that have demonstrated the real-time simultaneous control of multi-joint movements, especially wrist and finger movements, using an EMG-driven musculoskeletal model, which differs from the many data-driven algorithms that dominate the literature on EMG-driven prosthesis control. Real-time control was achieved with very little training and simple, quick (~15 s) calibration. Thus, our model is potentially a practical and effective control platform for multifunctional myoelectric prostheses that could restore more life-like hand function for individuals with upper limb amputation.}, number={3}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Crouch, Dustin L. and Huang, He}, year={2017}, month={Jun} }
 @article{huang_crouch_liu_sawicki_wang_2016, title={A cyber expert system for auto-tuning powered prosthesis impedance control parameters}, volume={44}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944521097&partnerID=MN8TOARS}, DOI={10.1007/s10439-015-1464-7}, abstractNote={Typically impedance control parameters (e.g., stiffness and damping) in powered lower limb prostheses are fine-tuned by human experts (HMEs), which is time and resource intensive. Automated tuning procedures would make powered prostheses more practical for clinical use. In this study, we developed a novel cyber expert system (CES) that encoded HME tuning decisions as computer rules to auto-tune control parameters for a powered knee (passive ankle) prosthesis. The tuning performance of CES was preliminarily quantified on two able-bodied subjects and two transfemoral amputees. After CES and HME tuning, we observed normative prosthetic knee kinematics and improved or slightly improved gait symmetry and step width within each subject. Compared to HME, the CES tuning procedure required less time and no human intervention. Hence, using CES for auto-tuning prosthesis control was a sound concept, promising to enhance the practical value of powered prosthetic legs. However, the tuning goals of CES might not fully capture those of the HME. This was because we observed that HME tuning reduced trunk sway, while CES sometimes led to slightly increased trunk motion. Additional research is still needed to identify more appropriate tuning objectives for powered prosthetic legs to improve amputees' walking function.}, number={5}, journal={Annals of Biomedical Engineering}, author={Huang, He and Crouch, D. L. and Liu, M. and Sawicki, G. S. and Wang, D.}, year={2016}, pages={1613–1624} }
 @article{crouch_huang_2016, title={Lumped-parameter electromyogram-driven musculoskeletal hand model: A potential platform for real-time prosthesis control}, volume={49}, ISSN={["1873-2380"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85005810502&partnerID=MN8TOARS}, DOI={10.1016/j.jbiomech.2016.10.035}, abstractNote={Simple, lumped-parameter musculoskeletal models may be more adaptable and practical for clinical real-time control applications, such as prosthesis control. In this study, we determined whether a lumped-parameter, EMG-driven musculoskeletal model with four muscles could predict wrist and metacarpophalangeal (MCP) joint flexion/extension. Forearm EMG signals and joint kinematics were collected simultaneously from 5 able-bodied (AB) subjects. For one subject with unilateral transradial amputation (TRA), joint kinematics were collected from the sound arm during bilateral mirrored motion. Twenty-two model parameters were optimized such that joint kinematics predicted by EMG-driven forward dynamic simulation closely matched measured kinematics. Cross validation was employed to evaluate the model kinematic predictions using Pearson׳s correlation coefficient (r). Model predictions of joint angles were highly to very highly positively correlated with measured values at the wrist (AB mean r=0.94, TRA r=0.92) and MCP (AB mean r=0.88, TRA r=0.93) joints during single-joint wrist and MCP movements, respectively. In simultaneous multi-joint movement, the prediction accuracy for TRA at the MCP joint decreased (r=0.56), while r-values derived from AB subjects and TRA wrist motion were still above 0.75. Though parameters were optimized to match experimental sub-maximal kinematics, passive and maximum isometric joint moments predicted by the model were comparable to reported experimental measures. Our results showed the promise of a lumped-parameter musculoskeletal model for hand/wrist kinematic estimation. Therefore, the model might be useful for EMG control of powered upper limb prostheses, but more work is needed to demonstrate its online performance.}, number={16}, journal={JOURNAL OF BIOMECHANICS}, author={Crouch, Dustin L. and Huang, He}, year={2016}, month={Dec}, pages={3901–3907} }
 @article{crouch_santago_plate_li_saul_2016, title={Relationship between maximum isometric joint moment and functional task performance in patients with brachial plexus injury: A pilot study}, volume={44}, ISSN={["1879-2219"]}, DOI={10.1016/j.gaitpost.2015.12.038}, abstractNote={We evaluated whether subjects with brachial plexus injury (BPI) adapted their movements to reduce the mechanical demand on their impaired upper extremity. In 6 subjects with unilateral BPI with C5 and C6 involvement, we measured bilateral maximum isometric shoulder and elbow strength, and computed joint kinematics and net muscle-generated joint moments during 7 unimanual functional tasks. Compared to the unimpaired extremity, maximum strength in shoulder abduction, extension, and external rotation was 60% (p = 0.02), 49% (p = 0.02), and 75% (p = 0.02) lower, respectively, on the impaired side. Significant kinematic and kinetic differences were observed only when reaching to the back of the head. However, because of substantially reduced strength in their impaired upper extremities, subjects used a significantly higher percentage of their maximum strength during several tasks and along several directions of movement. The peak percentage of maximal strength subjects used across tasks was 32% (p = 0.03) and 29% (p = 0.03) more on their impaired side in shoulder extension and external rotation, respectively. Subjects had less reserve strength available for performing upper extremity tasks and, therefore, may be less adaptive to strength declines due to injury progression and normal aging. Quantitatively measuring maximal strength may help clinicians ensure that patients maintain sufficient upper extremity strength to preserve long-term functional ability.}, journal={GAIT & POSTURE}, author={Crouch, Dustin L. and Santago, Anthony C., II and Plate, Johannes F. and Li, Zhongyu and Saul, Katherine R.}, year={2016}, month={Feb}, pages={238–244} }
 @article{crouch_hutchinson_plate_antoniono_gong_cao_li_saul_2015, title={Biomechanical basis of shoulder osseous deformity and contracture in a rat model of brachial plexus birth palsy}, volume={97A}, number={15}, journal={Journal of Bone and Joint Surgery-American Volume}, author={Crouch, D. L. and Hutchinson, I. D. and Plate, J. F. and Antoniono, J. and Gong, H. and Cao, G. H. and Li, Z. Y. and Saul, K. R.}, year={2015}, pages={1264–1271} }
 @article{crouch_plate_li_saul_2014, title={Computational Sensitivity Analysis to Identify Muscles That Can Mechanically Contribute to Shoulder Deformity Following Brachial Plexus Birth Palsy}, volume={39}, ISSN={["1531-6564"]}, DOI={10.1016/j.jhsa.2013.10.027}, abstractNote={Purpose Two mechanisms, strength imbalance or impaired longitudinal muscle growth, potentially cause osseous and postural shoulder deformity in children with brachial plexus birth palsy. Our objective was to determine which muscles, via either deformity mechanism, were mechanically capable of producing forces that could promote shoulder deformity. Methods In an upper limb computational musculoskeletal model, we simulated strength imbalance by allowing each muscle crossing the shoulder to produce 30% of its maximum force. To simulate impaired longitudinal muscle growth, the functional length of each muscle crossing the shoulder was reduced by 30%. We performed a sensitivity analysis to identify muscles that, through either simulated deformity mechanism, increased the posteriorly directed, compressive glenohumeral joint force consistent with osseous deformity or reduced the shoulder external rotation or abduction range of motion consistent with postural deformity. Results Most of the increase in the posterior glenohumeral joint force by the strength imbalance mechanism was caused by the subscapularis, latissimus dorsi, and infraspinatus. Posterior glenohumeral joint force increased the most owing to impaired growth of the infraspinatus, subscapularis, and long head of biceps. Through the strength imbalance mechanism, the subscapularis, anterior deltoid, and pectoralis major muscles reduced external shoulder rotation by 28°, 17°, and 10°, respectively. Shoulder motion was reduced by 40° to 56° owing to impaired growth of the anterior deltoid, subscapularis, and long head of triceps. Conclusions The infraspinatus, subscapularis, latissimus dorsi, long head of biceps, anterior deltoid, pectoralis major, and long head of triceps were identified in this computational study as being the most capable of producing shoulder forces that may contribute to shoulder deformity following brachial plexus birth palsy. Clinical relevance The muscles mechanically capable of producing deforming shoulder forces should be the focus of experimental studies investigating the musculoskeletal consequences of brachial plexus birth palsy and are potentially critical targets for treating shoulder deformity. Two mechanisms, strength imbalance or impaired longitudinal muscle growth, potentially cause osseous and postural shoulder deformity in children with brachial plexus birth palsy. Our objective was to determine which muscles, via either deformity mechanism, were mechanically capable of producing forces that could promote shoulder deformity. In an upper limb computational musculoskeletal model, we simulated strength imbalance by allowing each muscle crossing the shoulder to produce 30% of its maximum force. To simulate impaired longitudinal muscle growth, the functional length of each muscle crossing the shoulder was reduced by 30%. We performed a sensitivity analysis to identify muscles that, through either simulated deformity mechanism, increased the posteriorly directed, compressive glenohumeral joint force consistent with osseous deformity or reduced the shoulder external rotation or abduction range of motion consistent with postural deformity. Most of the increase in the posterior glenohumeral joint force by the strength imbalance mechanism was caused by the subscapularis, latissimus dorsi, and infraspinatus. Posterior glenohumeral joint force increased the most owing to impaired growth of the infraspinatus, subscapularis, and long head of biceps. Through the strength imbalance mechanism, the subscapularis, anterior deltoid, and pectoralis major muscles reduced external shoulder rotation by 28°, 17°, and 10°, respectively. Shoulder motion was reduced by 40° to 56° owing to impaired growth of the anterior deltoid, subscapularis, and long head of triceps. The infraspinatus, subscapularis, latissimus dorsi, long head of biceps, anterior deltoid, pectoralis major, and long head of triceps were identified in this computational study as being the most capable of producing shoulder forces that may contribute to shoulder deformity following brachial plexus birth palsy.}, number={2}, journal={JOURNAL OF HAND SURGERY-AMERICAN VOLUME}, author={Crouch, Dustin L. and Plate, Johannes F. and Li, Zhongyu and Saul, Katherine R.}, year={2014}, month={Feb}, pages={303–311} }