Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury

Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.Peer ReviewedPostprint (published version

Design And Development of A Powered Pediatric Lower-limb Orthosis

Gait impairments from disorders such as cerebral palsy are important to address early in life. A powered lower-limb orthosis can offer therapists a rehabilitation option using robot-assisted gait training. Although there are many devices already available for the adult population, there are few powered orthoses for the pediatric population. The aim of this dissertation is to embark on the first stages of development of a powered lower-limb orthosis for gait rehabilitation and assistance of children ages 6 to 11 years with walking impairments from cerebral palsy. This dissertation presents the design requirements of the orthosis, the design and fabrication of the joint actuators, and the design and manufacturing of a provisional version of the pediatric orthosis. Preliminary results demonstrate the capabilities of the joint actuators, confirm gait tracking capabilities of the actuators in the provisional orthosis, and evaluate a standing balance control strategy on the under-actuated provisional orthosis in simulation and experiment. In addition, this dissertation presents the design methodology for an anthropometrically parametrized orthosis, the fabrication of the prototype powered orthosis using this design methodology, and experimental application of orthosis hardware in providing walking assistance with a healthy adult. The presented results suggest the developed orthosis hardware is satisfactorily capable of operation and functional with a human subject. The first stages of development in this dissertation show encouraging results and will act as a foundation for further iv development of the device for rehabilitation and assistance of children with walking impairments

Prediction of three-dimensional crutch walking patterns using a torque-driven model

Computational prediction of 3D crutch-assisted walking patterns is a challenging problem that could be applied to study different biomechanical aspects of crutch walking in virtual subjects, to assist physiotherapists to choose the optimal crutch walking pattern for a specific subject, and to help in the design and control of exoskeletons, when crutches are needed for balance. The aim of this work is to generate a method to predict three-dimensional crutch-assisted walking motions following different patterns without tracking any experimental data. To reach this goal, we collected gait data from a healthy subject performing a four-point non-alternating crutch walking pattern, and developed a 3D torque-driven full-body model of the subject including the crutches and foot- and crutch-ground contact models. First, we developed a predictive (i.e., no tracking of experimental data) optimal control problem formulation to predict crutch walking cycles following the same pattern as the experimental data collected, using different cost functions. To reduce errors with respect to reference data, a cost function combining minimization terms of angular momentum, mechanical power, joint jerk and torque change was chosen. Then, the problem formulation was adapted to handle different foot- and crutch-ground conditions to make it capable of predicting three new crutch walking patterns, one of them at different speeds. A key aspect of our algorithm is that having ground reactions as additional controls allows one to define phases inside the cycle without the need of formulating a multiple-phase problem, thus facilitating the definition of different crutch walking patterns.Postprint (author's final draft

Development of a Planar Piecewise Continuous Lumped Muscle Parameter Model for Investigation of Joint Stiffness in Walking on a Level Surface

When joint stiffnesses are affected by injuries or illnesses they can interfere with gait and with activities of daily living, work, and leisure. Biomechanical models have been proposed for describing the effects of various conditions and interventions on the phases of gait. This dissertation reports the development of a planar piecewise continuous lumped muscle parameter (PPCLMP) model for investigating how different joint stiffnesses affect the gait phases individually and collectively. The proposed PPCLMP model characterizes the movements of lower limbs during each gait phase by a simplified dynamic system: the single stance phase by an inverted pendulum, the double stance phase by a kinematic chain, and the swing phase by a double pendulum. The model uses lumped muscle parameters to characterize the joint torques during each phase. The phase continuity is achieved by setting the joint angles and angular velocities at the end of one phase equal to those at the start of the next phase. The model can predict gait movements from given initial conditions (initial joint angles and angular velocities), anthropometry, lumped muscle parameters, and joint stiffness in a forward-dynamic mode. Also, if the gait movements are known, the model could estimate the lumped muscle parameters in an inverse dynamic mode. In the first study, the model was used in the forward-dynamic mode to predict joint angles and gait parameters for six healthy subjects’ anthropometry, ankle joint stiffnesses (without ankle-foot orthosis (AFO), with a low-stiffness AFO, and with a high-stiffness AFO), initial conditions, and constant lumped muscle parameters. Results showed that the trend of gait parameters changings (longer step length and shorter swing time on the AFO side for higher AFO stiffness) with different AFO stiffnesses were qualitatively well predicted by the model but quantitative prediction accuracy was limited (the mean errors were 0.15 m and 5% for the predicted step length and swing time, respectively) due to the constant values of lump muscle parameters. The second study examined the use of the model in an inverse-dynamic mode using data from a single inertial measurement unit (IMU) attached to the lower shank in order to estimate the initial conditions and lumped muscle parameters for each gait cycle. These were used by the model in the forward-dynamic mode to enhance the gait prediction. Results from two patients wearing AFOs demonstrated that the model prediction was markedly improved comparing with the first study by utilizing the inverse-dynamic mode as the mean RMSE was 0.07 m and 2% for predicted step length and swing time, respectively. The third study investigated the PPCLMP model prediction accuracy using the inverse and forward dynamic processes proposed in the second study. Three male and three female healthy subjects were recruited to walk with IMU-instrumented AFOs on their left feet to measure step lengths and swing time, while surface electrodes measured selected muscle activities for comparison with lumped muscle parameters. Results showed that the model prediction accuracy of step lengths and walking speed improved significantly (p < 0.05) with increasing stature; however, model prediction accuracy of swing time unaffected by stature. It was concluded that the PPCLMP model of gait has the potential for predicting how the prescription of an AFO of a given stiffness will affect gait, but more research is needed to refine model predictions by improving the representation of joint torques during gait.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163175/1/qifu_1.pd