A Pathological Condition Affects Motor Modules in a Bipedal Locomotion Model

Abstract

Bipedal locomotion is a basic motor activity that requires simultaneous control of multiple muscles. Physiological experiments suggest that the nervous system controls bipedal locomotion efficiently by using motor modules of synergistic muscle activations. If these modules were merged, abnormal locomotion patterns would be realized as observed in patients with neural impairments such as chronic stroke. However, sub-acute patients have been reported not to show such merged motor modules. Therefore, in this study, we examined what conditions in the nervous system merges motor modules. we built a two-dimensional bipedal locomotion model that included a musculoskeletal model with 7 segments and 18 muscles, a neural system with a hierarchical central pattern generator (CPG), and various feedback inputs from reflex organs. The CPG generated synergistic muscle activations comprising 5 motor modules to produce locomotion phases. Our model succeeded to acquire stable locomotion by using the motor modules and reflexes. Next, we examined how a pathological condition altered motor modules. Specifically, we weakened neural inputs to muscles on one leg to simulate a stroke condition. Immediately after the simulated stroke, the model did not walk. Then, internal parameters were modified to recover stable locomotion. We refitted either (a) reflex parameters or (b) CPG parameters to compensate the locomotion by adapting (a) reflexes or (b) the controller. Stable locomotion was recovered in both conditions. However the motor modules were merged only in (b). These results suggest that light or sub-acute stroke patients, who can compensate stable locomotion by just adapting reflexes, would not show merge of motor modules, whereas severe or chronic patients, who needed to adapt the controller for compensation, would show the merge, as consistent with experimental findings.

Acquisition of bipedal locomotion in a neuromusculoskeletal model with unilateral transtibial amputation

“…Based on the results of previous studies (Ogihara and Yamazaki, 2001;Aoi et al, 2010;Ichimura and Yamazaki, 2019), we constructed a two-dimensional musculoskeletal model including the head, arms, torso (HAT), thighs, shanks, and feet (Figure 1). We determined the segment length and inertia parameters of the model based on past findings (Jo and Massaquoi, 2007;Ichimura and Yamazaki, 2019). Each joint was modeled as a pin joint with a linear viscous element.…”

“…Each joint was modeled as a pin joint with a linear viscous element. The viscosity coefficients of the hip, knee, and ankle joints were 1.09, 3.17, and 0.943 Nms/rad, respectively (Aoi et al, 2010;Ichimura and Yamazaki, 2019). The knee and ankle joints were locked to avoid hyperextension and hyperflexion which are unrealistic joint ranges of motion.…”

“…The heel or toe received ground reaction forces (GRF) when they contacted the ground. The GRF is modeled by a linear spring and damper system which was employed in previous studies (Ogihara and Yamazaki, 2001;Aoi et al, 2010;Ichimura and Yamazaki, 2019) and could mimic the measured GRF. The coefficients of the spring and damper were 5.0 × 10 3 N/m and 1.0 × 10 3 Ns/m in the horizontal direction and 2.5 × 10 4 N/m and 5.0 × 10 2 Ns/m in the vertical direction, respectively (Ichimura and Yamazaki, 2019).…”

“…The forward dynamics simulation could produce physical and neural changes on the computer to enhance the understanding of human locomotion mechanisms. Various neuromusculoskeletal models have revealed the biomechanics and motor control of locomotion, such as control of human bipedal locomotion (Taga et al, 1991;Ogihara and Yamazaki, 2001;Hase and Yamazaki, 2002;Jo and Massaquoi, 2007;Aoi et al, 2010;Song and Geyer, 2015), adaptation to locomotion under pathological conditions (Ichimura and Yamazaki, 2019), and physiological characteristics of animal locomotion (Oku et al, 2021;Kim et al, 2022). Specifically, we examined whether individuals with UTTA acquire prosthetic gait through a symmetric or asymmetric feedback control for the left and right lower limbs.…”

Neurorobotic Models of Neurological Disorders: A Mini Review

“…Computational models have been implemented into virtual robot simulations, thus bypassing the actual construction of the robot but compromising by having an equally simulated environment. These have included more simplified machines such as simulating a 2D robot leg using a neural system with a hierarchical central pattern generator to model leg weakness as a symptom of stroke to explore rehabilitation strategies (Ichimura and Yamazaki, 2019 ). Another study simulated a two-link robot arm using a basal ganglia neuron model to examine the relation of neuron potential strength and gradient with symptoms in PD through varying dopamine (Connolly et al, 2000 ).…”