Daniel N. Bassett

An EMG-driven model to estimate muscle forces and joint moments in stroke patients

Individuals following stroke exhibit altered muscle activation and movement patterns. Improving the efficiency of gait can be facilitated by knowing which muscles are affected and how they contribute to the pathological pattern. In this paper we present an electromyographically (EMG) driven musculoskeletal model to estimate muscle forces and joint moments. Subject specific EMG for the primary ankle plantar and dorsiflexor muscles, and joint kinematics during walking for four subjects following stroke were used as inputs to the model to predict ankle joint moments during stance. The models ability to predict the joint moment was evaluated by comparing the model output with the moment computed using inverse dynamics. The model did predict the ankle moment with acceptable accuracy, exhibiting an average R(2) value ranging between 0.87 and 0.92, with RMS errors between 9.7% and 14.7%. The values are in line with previous results for healthy subjects, suggesting that EMG-driven modeling in this population of patients is feasible. It is our hope that such models can provide clinical insight into developing more effective rehabilitation therapies and to assess the effects of an intervention.

A Clinical Approach to Multi-Joint EMG-Driven Modelling

Most muscles span more than one joint. This can lead to problematic questions when making biomechanical models. How many joints need to be included in an accurate model? Do all joints that each muscle crosses need to be taken into account? If so, how many joints away from the joint of interest must be included, since they are all interconnected?Copyright

Prediction of Muscle Forces and Joint Moments in Stroke Patients Using an EMG-Driven Model

Abnormal kinematic and kinetic patterns are associated with disability following stroke. The estimation of internal forces and moments during movements is important for developing better rehabilitation regimens for this population. In this study, we used an EMG-driven model to estimate muscle forces and joint moments for stroke patients, and analyzed the kinetics of these patients. Although such models have been used in healthy people, this is the first study to model post-stroke patients.Copyright

MODELING FES PROTOCOLS FOR CORRECTING JOINT MOMENTS IN POST-STROKE SUBJECTS

INTRODUCTION EMG-driven neuromusculoskeletal models have been developed by many investigators to estimate joint moments and muscle forces during human movements. We have been exploring how to use these models as a platform for studying how to alter human movement by changing muscle activation patterns in patients with neurological disorders. Muscle activity can be changed (increased) using functional electrical stimulation. Hence, simulating the effects of changing muscle activation provides a useful tool to study the effects of this type of rehabilitation. modeling

Predicting Muscle Forces and Joint Moments Using Single Joint and Multi Joint EMG-Driven Models

The biomedical field thrives on computational devices. Clinicians, physical therapists, and researchers frequently use models as tools. The key to proper implementation of these tools is a good understanding of the limitations, advantages, and options available. Previous research on EMG-driven models demonstrated the ability of single joint models to predict joint moments with reasonable accuracy [1]. The advantage provided is the possibility of studying muscle and intersegmental forces in vivo.Copyright

Estimation of Corrective Changes in Muscle Activation Patterns for Stroke Patients During FES Intervention

Functional electrical stimulation (FES) has been used in the rehabilitation of stroke patients. It is important to know how to stimulate the muscles when using FES. Many control methods have been used to derive the required electrical stimulation patterns. However, these models were not developed based on biomechanical model of human neuromuscular system, thus can not account for sophisticated neurological control strategies during human movements. Based on our developed electromyography (EMG) driven model, we have created a biomechanical model to estimate the corrective increases in muscle activation patterns needed for a person following stroke to walk with an improved normal gait.Copyright