Bofeng Zhang

The Elderly Fall Risk Assessment and Prediction Based on Gait Analysis

In this paper, a cell phone and a three dimension accelerometer are used to develop a simple fall risk prediction system. Accelerometer offers a practical and low cost method of objectively monitoring human walking. This paper gives the gait stability and gait symmetry definition only under the data conditions of acceleration. According to the gait model, the stability and symmetry of gait are analyzed and calculated, at last not only the individuals gait can be evaluated, but it also constituted a gait assessment model. The proposed gait assessment model can be used to predict the fall risk of walking subject. The experiment results show that this method achieved better performance of prediction and efficiency.

State of the Art in Gait Analysis Using Wearable Sensors for Healthcare Applications

The ability to measure human movement accurately forms an essential part of a clinical assessment, thus allowing the efficacy of therapeutic interventions to be determined. The most commonly used clinical method for assessing human movement is goniometry, motion capture system, Electromagnetic tracking systems and so on. There are some essential limitations in each method. Inertial sensors such as accelerometers, magnetometers and gyroscopes have the potential to be used for assessing human movement in various environments. Numerous studies have reported using systems based on accelerometers or gyroscopes. These two types of sensors may also be combined to study human motions with good accuracy. The present study employed an inertial system which utilized such fusion technology which is reported to provide motion data with better accuracy. This paper reviews walking analysis using wireless inertial sensors currently, analyzes the features of this methods comparing with other methods, and indicates the key problem and the future direction and application domains.

Ontology Based User Profiling in Personalized Information Service Agent

Personalized information service agents have emerged in the recent years to help users to cope with the increasing amount of information available on the Internet. The effectiveness of agents depends mainly on profile completeness and accuracy. In the existing agents, although the performance of these systems improves after learning a user profile, it is difficult to share user profile and adapt user profile to user interests. In order to solve these problems in agents, we present ontology based user profiling methods. The approaches in user profiling, such as representation, acquisition, learning, adaptation and re-ranking, are discussed. Moreover, a personalized information service agent is designed based on these approaches, and the performance of agent is evaluated.

A Personalized Semantic Search Method for Intelligent e-Learning

Leukocyte migration is an important phenomenon in the inflammatory tissue. The migration process includes the rolling velocity decreasing and the leukocytes adhesion. However, the analysis of in vivo microscopy video is a labor-intensive and time consuming task. Several approaches have been proposed for tracking leukocyte movements. However, these approaches can either only track leukocytes that roll along the centerline of the blood vessel, or can only handle leukocytes with fixed morphologies. In addition, the camera/subject movement is a severe problem which occurs frequently while analyzing in vivo microscopy videos. In this paper, we proposed a new method for automatic recognition of non-adherent and adherent leukocytes. The experimental results demonstrate the effectiveness of the proposed method.In intelligent e-Learning, personalized search support becomes even more and more important now. This paper shows how to achieve the personalized search in e-learning environments. We present a semantic-based search method for personalized e- Learning. In the method, learning resources ontology and learner ontology are designed for semantic analysis and algorithm for ontology learning. The method is applied in an e-learning system and evaluated by some users. The result shows that the personalized semantic search method can improve the precision and recall of learning resources retrieval effectively in e-learning system, and can provide more intelligent e-learning services for the learners.

Human Walking Analysis, Evaluation and Classification Based on Motion Capture System

Gait analysis is the systematic study of human walking. It is helpful in the medical management of those diseases which affect the locomotion systems. Recently, the gait motion capture systems are becoming widely used by doctors and physical therapists for kinematics analysis and biomechanics and motion capture research, sports medicine and physical therapy, including human gait analysis and injury rehabilitation. This chapter describes some new progress on human walking analysis that our group made in the past few years based on motion capture system. Generally, ageing causes many changes to neuromuscular system of a human being, for an example, his walking capabilities degenerate by ageing. Because these changes sometimes result in an increase the number of falls during daily walking, especially after the age of 75, it is very important to study the age related changes in the walking gait of elderly subjects. Many researchers studied stability of human walking gait and it was quoted that human walking gait stability decreases with age increasing the risk of falls in elderly people. Many studies have been reported about the change in the kinematics parameters with age (Arif et al., 2004). This paper only focuses on the progress of walking modeling and walking stability. Especially, in order to simplify the method of data acquisition, this paper suggests process of reduction on dynamic stability features through feature selection. That will help us analyze stability in a more clear way.

Walking stability analysis by age based on Dynamic Time Warping

There are more and more researches on walking stability analysis for health management of elderly people. This study presents a definition and an assessment of human walking stability. Its main objectives are to propose a technique to quantify local dynamic stability, and to study the relationship between walking stability and age in developing an understanding of normal gait. Dynamic Time Warping (DTW) is an algorithm for measuring similarity between two sequences, which is employed to estimate the walking stability based on Fourteen-Linkage (FL) walking model. The walking data are from 58 health subjects aged from 21 to 65, walking on a regular surface. The results show that when subject walks on a regular surface, the walking stabilities including subjectpsilas position, velocity and acceleration have some relationships with age.

Opportunistic Scheduling and Performance Analysis on Wireless Network Coding

Network coding has been shown to be able to improve the throughput and power efficiency of wireless networks by mixing packets from different flows into a single packet. However, forcing packets to be coded may induce large delay and packet loss rate which is intolerable to some services such as real-time communications. Considering the diversity of traffic flows and the stochastic nature of the packet arrival process in wireless networks, we propose an opportunistic scheduling called Probabilistic Network Coding with Priority (PNCP) method. The relay node can transmit either network-coded or un-coded packets. Service priority and queue state are the two most important facts that determine a packets transmission with or without network coding. By using a Markov Chain model, the average packet delay and packet loss rate of PNCP are presented. We validate the theoretical results through simulations and demonstrate that it can serve different flows with priority. More importantly, we discuss the impact of different parameters on the performance of PNCP and show that the opportunistic scheduling can significantly reduce the packet loss rate and average delay.

A SAM-Based Evolution Model of Ontological User Model

The goal of user modeling is to provide personalized services based on user’s interests and preferences, thus allowing for more efficient information access. Frequently user’s interests drift with time. The ability to adapt fast to the current users interests is an important feature of user model. In this paper, based on behavioral psychology, a SAM (Spreading Activation Model)-based evolution model of ontological user model is proposed. The method utilizes semantic technology to adapt user model with the support of domain ontology. A spreading activation algorithm is used to maintain the interest weights based on the user’s ongoing behaviors. In the end, the SAM-based evolution model is applied in a computer science paper retrieval system (CSPRS). The result shows that the precision and recall of the retrieval system are improved obviously, and the SAM-based model is effective and efficient in the user model evolution.

Methods of Pattern Extraction and Interval Prediction for Equipment Maintenance

Maintenance interval is one of the most important indexes in equipment maintenance strategy. In the traditional planned maintenance strategy, maintenance interval is often predicted by the way of statistical theory. This method lacks flexibility and can not adjust maintenance intervals according to the actual situation of maintenance. It will easily lead to an under-maintenance or over-maintenance. In order to carry out individual maintenance, in this paper, we use BP Neural Network to predict dynamically maintenance intervals. At first, we extract a lot of maintenance models which exist in the historical maintenance data, and then use these models to train the BP neural network, finally use the trained BP neural network to predict the maintenance interval according to the equipment maintenance model. This method considered the past maintenance factors and made maintenance interval better. The experiment shows that this method achieved 27.1% average relative error of patterns. The dynamic maintenance interval makes the amendment of maintenance interval more scientific for individual strategy.

Geomagnetic Precursor Z Component Diurnal Variation Phase Anomaly Recognition based on the Pattern Distance

Geomagnetic precursor is one of many earthquake precursors, which have better effects on earthquake prediction, while the diurnal variation anomaly of geomagnetic precursor Z component is an important one in short-impending anomalies. Basing on the pattern distance of geomagnetic precursor Z component diurnal variation, this paper proposes an anomaly recognition algorithm which combines the feature of geomagnetic precursor data with time series similarity measure. We validate the effectiveness of the algorithm through the actual data.