Xingda Qu

Effects of load carriage and fatigue on gait characteristics.

The objective of this study was to determine the main and interactive effects of load carriage and fatigue on gait characteristics. Twelve young male participants were recruited in this study. Fatiguing protocol involved a running exercise, and fatigue was considered to be induced when the participants first gave an RPE rating at or above 17. Gait data were collected when the participants walked on a medical treadmill at their self-selected comfortable speed, both before and right after the fatiguing exercise. Different back-carrying loads (i.e. 0, 7.5, and 15 kg) were applied separately to the participants during the walking trials. Gait variability measures and kinematic measures were used to quantify gait characteristics. The results showed that gait width variability, hip range of motion, and trunk range of motion increased with fatigue and with the application of the heavy load. These findings suggest that both fatigue and load carriage compromise gait. Findings from this study can help better understand how fatigue and load carriage affect gait, and further aid in developing interventions that are able to minimize fall risks especially with the application of fatigue and/or external load.

Effects of external loads on balance control during upright stance: experimental results and model-based predictions.

The purpose of this study was to identify the effects of external loads on balance control during upright stance, and to examine the ability of a new balance control model to predict these effects. External loads were applied to 12 young, healthy participants, and effects on balance control were characterized by center-of-pressure (COP) based measures. Several loading conditions were studied, involving combinations of load mass (10% and 20% of individual body mass) and height (at or 15% of stature above the whole-body COM). A balance control model based on an optimal control strategy was used to predict COP time series. It was assumed that a given individual would adopt the same neural optimal control mechanisms, identified in a no-load condition, under diverse external loading conditions. With the application of external loads, COP mean velocity in the anterior-posterior direction and RMS distance in the medial-lateral direction increased 8.1% and 10.4%, respectively. Predicted COP mean velocity and RMS distance in the anterior-posterior direction also increased with external loading, by 11.1% and 2.9%, respectively. Both experimental COP data and model-based predictions provided the same general conclusion, that application of larger external loads and loads more superior to the whole body center of mass lead to less effective postural control and perhaps a greater risk of loss of balance or falls. Thus, it can be concluded that the assumption about consistency in control mechanisms was partially supported, and it is the mechanical changes induced by external loads that primarily affect balance control.

Hardware Development and Locomotion Control Strategy for an Over-Ground Gait Trainer: NaTUre-Gaits

Therapist-assisted body weight supported (TABWS) gait rehabilitation was introduced two decades ago. The benefit of TABWS in functional recovery of walking in spinal cord injury and stroke patients has been demonstrated and reported. However, shortage of therapists, labor-intensiveness, and short duration of training are some limitations of this approach. To overcome these deficiencies, robotic-assisted gait rehabilitation systems have been suggested. These systems have gained attentions from researchers and clinical practitioner in recent years. To achieve the same objective, an over-ground gait rehabilitation system, NaTUre-gaits, was developed at the Nanyang Technological University. The design was based on a clinical approach to provide four main features, which are pelvic motion, body weight support, over-ground walking experience, and lower limb assistance. These features can be achieved by three main modules of NaTUre-gaits: 1) pelvic assistance mechanism, mobile platform, and robotic orthosis. Predefined gait patterns are required for a robotic assisted system to follow. In this paper, the gait pattern planning for NaTUre-gaits was accomplished by an individual-specific gait pattern prediction model. The model generates gait patterns that resemble natural gait patterns of the targeted subjects. The features of NaTUre-gaits have been demonstrated by walking trials with several subjects. The trials have been evaluated by therapists and doctors. The results show that 10-m walking trial with a reduction in manpower. The task-specific repetitive training approach and natural walking gait patterns were also successfully achieved.

Evaluation of the roles of passive and active control of balance using a balance control model

At present there is a lack of consensus regarding the relative roles of passive and active control of quiet upright stance. In the current work, this issue was investigated using two simulation models based on contemporary theories. Specifically, the two models, both of which assumed active control torques to be generated from an optimal neural controller, differed with respect to whether or not passive control torques (stiffness and damping) were included. Model parameters were specified using experimental center-of-pressure (COP) time series obtained during upright stance, and comparisons then made between simulated and actual COP-based measures. Including both active and passive joint torques in the control model did not appear to lead to any improvement in the ability to simulate COP compared with only including active joint torque. Further, simulated passive control torques were typically less than 10% of the active control torques, though some exceptions were found. These results, along with existing empirical evidence, suggest that active control torque is dominant in maintaining balance during upright stance.

Model-based assessments of the effects of age and ankle fatigue on the control of upright posture in humans

The aim of this study was to investigate how and why age and localized muscle fatigue affect postural control using model-based simulations. A balance control model, based on an optimal control strategy, was used to simulate trials of quiet upright stance both pre-fatigue and following induced ankle plantarflexor fatigue. Empirical data were obtained from an earlier study that included both younger and older participants. Effects of age and ankle fatigue were determined from center-of-pressure (COP) measures and fitted model parameters. Though some discrepancies existed, the simulated effects of age and ankle fatigue were consistent with experimental findings in terms of trends in COP-based measures with age and ankle fatigue. Changes in both COP-based measures and model parameters were used to infer potential underlying causal mechanisms for the observed effects of age and ankle fatigue. For example, the model-based simulations indicated that sensory delay time increased with age and ankle fatigue by 31.1% and 2.9%, respectively, suggesting a potentially important role for such delay in postural control and fall risks.

An individual-specific gait pattern prediction model based on generalized regression neural networks

Robotics is gaining its popularity in gait rehabilitation. Gait pattern planning is important to ensure that the gait patterns induced by robotic systems are tailored to each individual and varying walking speed. Most research groups planned gait patterns for their robotics systems based on Clinical Gait Analysis (CGA) data. The major problem with the method using the CGA data is that it cannot accommodate inter-subject differences. In addition, CGA data is limited to only one walking speed as per the published data. The objective of this work was to develop an individual-specific gait pattern prediction model for gait pattern planning in the robotic gait rehabilitation systems. The waveforms of lower limb joint angles in the sagittal plane during walking were obtained with a motion capture system. Each waveform was represented and reconstructed by a Fourier coefficient vector which consisted of eleven elements. Generalized regression neural networks (GRNNs) were designed to predict Fourier coefficient vectors from given gait parameters and lower limb anthropometric data. The generated waveforms from the predicted Fourier coefficient vectors were compared to the actual waveforms and CGA waveforms by using the assessment parameters of correlation coefficients, mean absolute deviation (MAD) and threshold absolute deviation (TAD). The results showed that lower limb joint angle waveforms generated by the gait pattern prediction model were closer to the actual waveforms compared to the CGA waveforms.

Uncontrolled manifold analysis of gait variability: effects of load carriage and fatigue.

The uncontrolled manifold (UCM) analysis has been demonstrated to be a powerful tool for understanding motor variability. The purpose of this study was to use the UCM analysis to investigate the effects of load carriage and fatigue on gait variability. Whole-body kinematic data during treadmill walking were collected from 12 healthy male participants when fatigue and load carriage were applied. The task-level variable for the UCM analysis was selected to be the whole-body COM. We chose to analyze the whole-body COM data at two important gait events: right heel contact and right toe off, and the UCM analysis was carried out in the sagittal and frontal planes, separately. The dependent measures were UCM variability measures and UCM ratio. Three-way ANOVA was performed to determine the main and interaction effects of back-carrying load, fatigue, and gait events on the dependent measures. The results showed that frontal UCM ratio significantly changed with the application of back-carrying load and fatigue, indicating that both factors had effects on motor performance in stabilizing the whole-body COM in the frontal plane. These findings can facilitate a better understanding of the nature of motor variability due to load carriage and fatigue.

Simulating Human Lifting Motions Using Fuzzy-Logic Control

Human motion simulation is an ill-posed problem. In order to predict unique lifting motion trajectories, a motion simulation model based on fuzzy-logic control is presented. The human body was represented by a 2-D five-segment model, and the neural controller was specified by fuzzy logic. Fuzzy rules were defined with their antecedent part describing the fuzzy variables of scaled positional error and velocity, and with their consequent part describing scaled angular velocity. These rules were generated according to certain trends in the fuzzy variable trajectories observed from actual lifting motions. An optimization procedure was performed to specify the parameters of the membership functions by minimizing the differences between the simulated and actual final postures. Simulations were obtained for 14 novel lifting motions from seven participants. Overall, results indicated that the presented model simulated lifting motions with an accuracy that was at least comparable to some previous human motion simulation models. The accuracy of the simulations differed between joints and was highest near the beginning and end of the motions. Strengths and limitations of the modeling approach are discussed. The use of fuzzy-logic control appears to be a fruitful basis for future simulations of lifting and other human tasks.

Subject-specific gait parameters prediction for robotic gait rehabilitation via generalized regression neural network

Gait pattern planning is important in robotic gait rehabilitation, whereby patients learned the pattern provided to them Gait pattern is related to gait parameters, such as cadence, stride length, and walking speed. Therefore, the planning of gait parameters for natural walking should be addressed in order to generate gait pattern for specific subjects. The present work utilizes generalized regression neural networks (GRNNs) to predict natural gait parameters for a given subject. The inputs of GRNNs are age, gender, body height, and body weight of the targeted subject. First of all, speed mode (normal/slow) must be chosen by the therapist. When speed mode is specified, the trained “Walking Speed” GRNN (WS-GRNN) outputs a selectable range of walking speed for a given subject. Subsequently, the therapist can select and recommend a walking speed, which will be used as an input to “Stride Length” GRNN (SL-GRNN) for the generation of stride length in the next step. Finally, cadence is calculated from walking speed and stride length. This model is easy to use to obtain gait parameters, since the therapist only needs to predefine the speed mode and select a walking speed from the range that is recommended by WS-GRNN. Results and t-test shows that outputs predicted by the GRNNs are closed to the experimental data. The efficiency and accuracy of the GRNNs are discussed in the conclusion.

Study of body weight shifting on robotic assisted gait rehabilitation with NaTUre-gaits

Therapist assisted body weight supported gait rehabilitation was introduced about 20 years ago. Subsequently, several robotic systems have been introduced for assisted body weight supported gait rehabilitation. However, pelvic assistance is not commonly found in those robotic systems. Lacking of pelvic assistance has several disadvantages. The most obvious disadvantage is the inability to promote body weight shifting during the gait rehabilitation. This work presents a pelvic assistance mechanism design to provide pelvic motion assistance during gait rehabilitation. The mechanism is a module found on NaTUre-gaits, a robotic system that provides gait rehabilitation in the context of over ground walking. The methodology of processing the pelvic motion for playback on the proposed pelvic assistance mechanism is discussed. The pelvic assistance mechanism is tested on human and ground reaction force was recorded for analysis of body weight shifting with/without pelvic motion assistance. It is found that the ground reaction force during robotic assisted walking is significantly affected due to the intervention of the robotic system. Future work is suggested for further study on other potential factors, which could have contributed to the change of the ground reaction force.