Hup Boon Lim

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.

Can the swimming thrust of BCF biomimetics fish be enhanced

In this paper, a robotic fish employing a carangiform swimming mode have been developed. The tail fin mechanism with a spring and a movable pin enables a smooth adjustable motion. Experiments conducted in the laboratory aim to study the variation of robotic fishs thrust and velocity with respect to various parameters, which includes the frequency, amplitude of oscillation, joint link, aspect ratio, free stream velocity and the spring effect. The testing also enables us to find out the relationship between various parameters and how to adjust the parameters in order to generate the maximum thrust. On the other hand, field trials have also been conducted to demonstrate the swimming capability of the robotic fish in real life situation.

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.

Initial analysis of EMG signals of hand functions associated to rehabilitation tasks

The objective of this work is to study the EMG signals based on hand motions for specified tasks, and different gripping conditions so as to identify patterns of EMG signals. This will allow therapists to identify weak muscles of patients with motor weakness, such as spinal cord injury (SCI) and post-stroke and concentrate on rehabilitation activities which can strengthen these specific muscles. At the same time, it is hoped that the analysis is able to provide useful data for objective and quantitative assessment towards control applications on the hand rehabilitation device, which is being developed. The analysis of the EMG signals for various hand muscles during functional motions and prehensile tasks has been carried out. The method to identify individual hand motions from EMG signals is described. The EMG signals associated to physical parameters are also illustrated in this paper. Finally, the preliminary works and future research are concluded.

Objective and quantitative assessment methodology of hand functions for rehabilitation

The objective of this work is the study and analysis of the fundamental issues that constitute the development of a hand rehabilitation system by making use of mechanisms with systematic methodology for objective and quantitative assessment for patients with motor weakness, such as spinal cord injury (SCI) and post-stroke. The goal of the work is to use these assessment/measurement results efficiently, so that the system can predict the intention to perform certain movement and act on the patients hand. The clinicalengineering research aspects are discussed and presented, and the objective and quantitative assessment/evaluation methodology (with sEMG, ROM, grip/pinch force and clinical outcome measures) has been proposed for the task-based hand rehabilitation system to improve their functional ability of hand for functional activities of daily living (ADL) and rehabilitation exercises. The initial results for each category are also illustrated in this paper. Finally, the preliminary works and future research are concluded.

Pelvic control and over-ground walking methodology for impaired gait recovery

In this paper, a methodology for gait rehabilitation, combines over-ground walking, body weight support, pelvic control, and gait assistance are introduced and the integrated platform has been developed. This paper also discusses the gap of state-of-the-art research in gait rehabilitation. Systems like Lokomat and Kineassist were studied and discussed. Initial testing and EMG experiments have been carried out on the developed platform. The results of EMG show that the developed robotic orthosis effectively reduces the effort requirement during assisted gait locomotion. Future testing on the effect study of pelvic control and over-ground walking is being conducted.

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.

Natural gait parameters prediction for gait rehabilitation via artificial neural network

Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural gait parameters for a given subject. The inputs of the MLPNN are age, gender, body height, and body weight of the targeted subject. The MLPNN is trained to output a suitable walking speed and cadence for given subject. Two MLPNNs are trained to study the efficiency and accuracy in predicting the desired outputs, for two different setups. First setup is that the MLPNN is trained specifically for slow speed condition only. In second setup, the MLPNN is trained for both slow and normal speed conditions. The results of the MLPNNs are presented in this paper. The efficiency and accuracy of the MLPNNs are discussed.

Subject tailored gait pattern planning for robotic gait rehabilitation

Robotic is gaining its popularity in gait rehabilitation. Gait pattern planning is an important as to ensure that the gait pattern provided by the robot onto the patient is natural. It is known that cadence, stride length, and walking speed are the key factors of gait pattern. Moreover, a systematical methodology for gait pattern planning in terms of these factors is necessary. The present work proposes a methodology to generate waveforms of lower limb joint angles during walking, given a set of cadence and stride length. The walking motion has been captured with a motion capture system by using passive markers. The waveforms are then decomposed into Fourier coefficients. The decomposition reduces the amount of data for analysis by representing the entire waveform with eleven Fourier coefficients. A function for Fourier coefficients in terms of cadence and stride length are established with a multiple linear regression. The final constructed waveforms with the Fourier coefficients computed with the function are presented together with the original experimental waveforms. The final waveforms closely match the experimental waveforms.