Kay Hiang Hoon

Modeling and simulation for a drop-impact analysis of multi-layered printed circuit boards

Multi-layered printed circuit boards (PCBs) contain a multi-layered structure that is suitable for high-speed and high-frequency applications. Hence, they are used extensively in electronic packaging assemblies for high-density applications. However, numerous composite parts and complex material properties of multi-layer PCBs complicate the reliability simulation of PCB model. This paper deals with a finite element analysis intended to describe numerically the behavior of multi-layered multi-materials PCB model (combination of metallic and composite plies) in the drop-impact performance. Through the comparison of physical drop test results, the fully multi-layered model illustrates higher accuracy if compared with that of the traditional simplified isotropic model and orthotropic model. The effects of material properties for the multi-layer PCB under drop-impact shock have also been investigated.

Initial study on the drop-impact behavior of mini Hi-Fi audio products

Abstract Most electronic devices suffer impact-induced failure in their usage. Drop/impact performance of these products is one of the important concerns in product design. It is often time and cost consuming and thus undesirable to conduct drop tests for every design to physically detect any sign of failure and identify their drop behaviors. Finite element analysis provides a vital and powerful tool to solve the problems. The methodology of finite element modeling, simulation, and basic experimental validation should be developed to effectively study the drop-impact effects. Modeling and transient drop analysis for a mini Hi-Fi audio product is considered in this work. The impact analysis of the top-drop with buffer hitting the concrete floor is the main interest and concern of this work. The model is created with Pro-E, and the analysis is carried out with an existing software package, pam-crash . The analysis is focused on the deformation of the bottom plate that is carrying a transformer. The deflection measured using a 3D-probe measurement is compared with the simulated results. Experimental data have also been obtained for drop simulation correlation for the plate and buffer material properties. The G-force and the permanent deformation of the bottom plate during drop are noted. The effects of the material properties to the plate deflection under drop/impact shock have also been investigated.

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.

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.

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.

An optimized perching mechanism for autonomous perching with a quadrotor.

A two-dimensional perching model is proposed first for perching with quadrotors. Then a perching mechanism by means of grasping is designed based on the model. The kinematic specifications of the perching mechanism are optimized to maximize the force transfer ratio so that sufficient grasping force can be generated for reliable perching. A controller of the gripper based on the control strategy from previous development is designed for autonomous perching with a quadrotor. Experiments on the grasping capability and reliability of the mechanism and its effectiveness with the controller for autonomous perching are conducted. Results show that the perching mechanism can generate sufficient grasping force and achieve autonomous perching to a target pole with a quadrotor both effectively and reliably.

Design and control of robotic exoskeleton with balance stabilizer mechanism

Robotic exoskeletons have drawn much attention recently due to their potential ability to help the stroke and spinal cord injury patients to regain the ability of walking. However, the biggest challenge is the balancing of the exoskeleton and how it can balance is still an open question. Most of the time, patients using such exoskeleton devices require sufficient upper body strength to control upright posture and also manipulate crutches/walking frames to partially support body weight and keep balance. However, high energy cost and the high potential of falling using these devices remains a problem. In this paper, the issues are tackled by virtue of a proposed balance stabilizer mechanism which is able to provide active balance assistance for robotic exoskeletons. The design of a robotic exoskeleton together with balance stabilizer mechanism will be presented and discussed. In addition, a trajectory generation method, which can generate dynamically stable and tunable gait pattern, will also be shown. Finally, clinical trial results with a tetraplegia subject is presented and discussed.