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

Abstract

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 model's 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.

Figure 1

Schematic of the Hill-type muscle model. The muscle-tendon unit has a muscle fiber in series with the tendons, with muscle-tendon force (F) passing through both components.

Figure 2

Examplar EMG for the primary ankle plantar and dorsiflexor muscles during stance for one subject. Muscle activations depicted in bold are the resulting profiles after tuning the model. The dashed line represents the muscle activity used when predicting the ankle moment for a novel walking trial. Note the differences in muscle activation patterns. These muscle activation patterns were used as inputs to generate the ankle moments shown in Figures 3 and 4.

Figure 3

The bold solid line is the ankle moment during stance for 1 subject computed using inverse dynamics (i.e., measured). The thinsolid line depicts the modeled moment using average values obtained from the literature for the muscle parameters. Poor agreement is the result of the muscle parameters not being appropriate for the subject. The dashed line is the ankle moment after the model was tuned. Note how well the model matches the measured moment.

Figure 4

The bold solid line is the ankle moment during stance for 1 subject computed using inverse dynamics (i.e., measured). The thin solid line depicts the model predicted moment using the subject’s EMG and joint kinematics. Despite the variability in muscle activation (see Figure 2), the model was able to predict the ankle moment with good accuracy (R² = 0.95 & normalized RMS error = 5.8%).