Bufu Huang

Intelligent shoes for abnormal gait detection

In this paper we introduce a shoe-integrated system for human abnormal gait detection. This intelligent system focuses on detecting the following patterns: normal gait, toe in, toe out, oversupination, and heel walking gait abnormalities. An inertial measurement unit (IMU) consisting of three-dimensional gyroscopes and accelerometers is employed to measure angular velocities and accelerations of the foot. Four force sensing resistors (FSRs) and one bend sensor are installed on the insole of each foot for force and flexion information acquisition. The proposed detection method is mainly based on Principal Component Analysis (PCA) for feature generation and Support Vector Machine (SVM) for multi-pattern classification. In the present study, four subjects tested the shoe-integrated device in outdoor environments. Experimental results demonstrate that the proposed approach is robust and efficient in detecting abnormal gait patterns. Our goal is to provide a cost-effective system for detecting gait abnormalities in order to assist persons with abnormal gaits in the developing of a normal walking pattern in their daily life.

Multi-Objective Genetic Algorithm for Hybrid Electric Vehicle Parameter Optimization

As a typical multi-objective optimization problem, parameter optimization of HEV power control strategy must deal with the conflict between objectives, as fuel consumption and emissions. Classical methods define the HEV parameter optimization as a single objective problem to minimize the fuel consumption. In this paper, the multi-objective genetic algorithm (MOGA) is generalized for parameter optimization of power control strategy of series hybrid electric vehicle. Using a single unified formulation, a number of design objectives can be simultaneously optimized through searching in the parameter space. Compared with two main strategies, as Thermostatic and single-objective genetic algorithm (SOGA), the computation procedures of MOGA are discussed. Simulation results based on the model of series hybrid electric vehicle illustrate the optimization validity of MOGA

Gait Event Detection with Intelligent Shoes

For more precise control of the FES-derived gait, the timing of specific gait phases is needed. With the information of when the legs are in each phase of gait, the quality of the gait during each phase can be assessed. The goal of this paper is to investigate potential use of machine learning techniques, in particular Support Vector Machine (SVM), and Force Sensitive Resistor (FSR) to detect discrete stages in the cyclic motion of dynamic human gait. We present intelligent shoes for capturing and analyzing dynamic human gait data. Principal Component Analysis(PCA) will be applied for feature generation and data reduction, and Support Vector Machine (SVM) will be applied for training and classifier generation. The classification performance is assessed quantitatively, by measuring the percentage of time steps in which the correct event is found and qualitatively, by observing the types of errors encountered. The experimental results verify that the proposed method is valid and useful with overall accuracy to 91%.

An Intelligent Shoe-Integrated System for Plantar Pressure Measurement

An intelligent shoe-integrated system has been developed to measure both the pressure distribution under eight special plantar regions and the mean plantar pressure during a subjects normal walking. The system mainly consists of 8 force sensing resistors (FSRs) arranged under bony prominences of each foot, a main board based on microprocessor, and a radio frequency (RF) wireless communication module. The digital sampling frequency is 50 Hz which is adequate for the activity of walking. This system is based on support vector machine (SVM) regression for learning the relationship between eight FSR values and the corresponding mean pressure acquired by Pedar insole system (Novel, Munich). Experimental results show that the system can achieve accurate mean pressure estimation with small mean squared error (MSE). Our goal is to provide a reliable and cost-effective system for predicting the value of mean plantar pressure in order to assist patients with musculoskeletal and neurological disorders in the development of normal gait in their daily life.

Particle Swarm Optimization for Operational Parameters of Series Hybrid Electric Vehicle

Because of the inherent advantages, i. e. increased fuel economy, reduced harmful emissions and better vehicle performance, hybrid electric vehicles (HEV), powered by internal combustion engine (ICE) and energy storage, are being given more and more attention. Since the extent of HEV improvement greatly depends on selection of the control strategy parameters, particle swarm optimization (PSO) algorithm is introduced to optimize the strategy parameters for fuel economy and emissions in this paper. Compared with one of the main strategies, Dividing RECTangles (DIRECT), the computation procedures of particle swarm optimization algorithm are discussed, and simulation study based on the model of series hybrid electric vehicle is given to illustrate the optimization validity of the particle swarm optimization algorithm.

Optimization of Series Hybrid Electric Vehicle Operational Parameters By Simulated Annealing Algorithm

Aimed at solving the more and more serious problems of energy and pollution, hybrid electric vehicle (HEV) is one of the best practical applications for transportation with high fuel economy and low emission. Since the extent of improvement included with HEV greatly depends on selection of the operational parameters, simulated annealing (SA) algorithm is introduced to optimize the operational parameters for fuel economy and emissions in this paper. Compared with one of the widest used strategies, dividing rectangles (DIRECT), the computation procedure of simulated annealing algorithm are discussed, and simulation study based on the model of series hybrid electric vehicle is given to illustrate the optimization feasibility of the simulated annealing algorithm.

Intelligent Shoes for Human Identification

Human gait is a kind of dynamic biometrical feature which is complex and difficult to imitate, it is unique and more secure than static features such as password, fingerprint and face. In this paper, we present intelligent shoes for human identification under the framework of capturing and analyzing dynamic human gait. By utilizing this dynamic property we focus on the research idea of classifying the wearers into authorized ones and unauthorized ones by modeling their individual gait performance. Each intelligent shoe can detect fourteen realtime gait parameters through walking. Principal component analysis(PCA) will be applied for feature generation and data reduction, and support vector machine(SVM) will be applied for training and classifier generation. The experimental results verify that the proposed method is valid and useful with a success human identification rate about 98%.

HUMAN IDENTIFICATION BASED ON GAIT MODELING

Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearers gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.