Heterogeneous data fusion for three-dimensional gait analysis using wearable MARG sensors

Gait analysis has become a research highlight. In this paper, we propose a computing method using wearable magnetic angular rate and gravity (MARG) sensor arrays with wireless network, which calculates absolute and relative orientation and position information of human foot motion during level walking and stair climbing process. Three-dimensional foot orientation and position were estimated by a Kalman-based sensor fusion algorithm and validated by ground truth provided by Vicon system. The repeatability of the alignment procedure and the measurement errors were evaluated on healthy subjects. Experimental results demonstrate that the proposed method has a good performance at both motion patterns. No significant drifts exist in the overall results presented in the paper. The measured and estimated information can be transmitted to remote server through internet. Moreover, this method could be applied to other cyclical activity monitoring

A hybrid human-machine interface for hands-free control of an intelligent wheelchair

This paper presents a novel hybrid human-machine interface (HMI) designed for hands-free control of electric powered wheelchairs. Both forehead electromyography (EMG) signals and colour face image information are deployed to identify winking and jaw clenching movements of human face. Five winking and jaw clenching movement patterns are selected and classified, mapping into six control commands to drive an electric powered wheelchair in an indoor environment. Six subjects participated in the experiments and the experimental results show that the proposed control scheme have potential applicability to accommodate various individual cases and achieve good performance. © 2011 Inderscience Enterprises Ltd

Fatigue state recognition based on improved ICA-HMM

Many traffic accidents are caused by drivers with fatigue states. It is important to detect fatigue states of drivers from their eye opening degrees, namely normal, dozing and fatigue. The paper proposes a fatigue state recognition algorithm that combines independent component analysis (ICA) and one-dimensional hidden Markov model (HMM) together. The algorithm firstly does binarisation processing for colour images, and ICA algorithm is then used to extract fatigue states. FastICA algorithm is deployed to accelerate the speed of feature extraction, and one-dimensional HMM is finally used to recognise the eye fatigue degree for the fatigue state. The experiment results show that the algorithm can rapidly and effectively recognise the different fatigue states at the eyes area of the driver

A novel information fusion based FTT algorithm for a driver fatigue monitoring system

This paper presents a novel feature triangle tracking (FTT) algorithm to track the eyes, mouth and head gestures of a fatigued driver. The proposed method uses the motion information to localise the face region at an YCrCb colour space to determine the locations of eyes and mouth. According to geometrical relationships of facial components and various head gestures of a driver, the system derives from both the isosceles feature triangle and the right feature triangle. The FTT algorithm effectively removes the confusing triangle from the tracking of the video sequence. The experimental results show that the proposed algorithm precisely tracks the various head gestures of a fatigued driver in real-time video frames

Interactive indoor environment mapping through visual tracking of human skeleton

This paper presents a novel human-robot interaction approach to grid mapping of an indoor environment based on a 3D kinect sensor and the grid-based mapping algorithm. It mainly includes three modules: skeleton tracking, robot control and GMapping. Firstly, the skeleton tracking module builds a human skeleton model, extracts the skeleton joints' position information from 3D visual data and generates digital signals through identifying some simple motions and events. Then according to different digital signals and joints' position information, the robot control module enables the robot to take different actions such as following the person, stop and so on. Finally, the grid map of the environment is built through GMapping algorithm based on odometry and laser data, which is improved by Rao-Blackwellised particle filters. The proposed approach has been implemented successfully in several different buildings and can be applied to service robots. Compared with traditional roaming for mapping, human guiding the robot for mapping is more efficient and takes less time in a complicated environment. Meanwhile, compared with wearable motion sensors attached to the human body, this approach is more convenient and make the user more comfortable. Copyright © 2013 Inderscience Enterprises Ltd

Hybrid lip shape feature extraction and recognition for human-machine interaction

Dumb and deaf people are unable to interact with robots using traditional voice-based human-machine interfaces (HMI). Lip motion is a useful way for these people to communicate with machines, even for normal people in extremely noisy environments. However, the recognition of lip motion is a difficult task since the region of interest (ROI) is non-linear and noisy. This paper proposes a novel lip shape feature extraction method to deal with the difficulty, based on hybrid dual-tree complex wavelet transform (DT-CWT) and discrete cosine transform (DCT). The approximate shift invariance of DT-CWT is utilised to make the same lip shape have the same feature vector when the lips are in different positions in the ROI. Then, DCT is used to extract coefficients from the feature vector generated by DT-CWT, and to choose the larger coefficients to obtain the key information of lip shape and reduce the dimensions of a feature vector. The experimental results show that this method can greatly improve the accuracy of lip shape recognition, and enhance the robustness of the lip shape-based HMI. © 2013 Inderscience Enterprises Ltd

Diverse replenishment frequency model for TOC supply chain replenishment systems with capacity constraints

When the theory of constraints (TOC) supply chain replenishment system (TOC-SCRS) is deployed in a plant or a central warehouse, replenishment frequency (RF) becomes one of the important decision parameters and depends on the sales or replenishment quantity (RQ) in the plant. The higher RF is preferred by TOC for the low inventory and the fast response to different market requirements. However, when sales significantly increase and capacity is not enough, the lower RF is required to save the setup capacity. Some existing RF determination models substantially increased in inventory. This paper proposes a diverse RF model for TOC-SCRS with capacity constraints so that the RF will only moderately increase in inventory. A numeric example is utilised to evaluate the proposed method and a prototype is further provided to demonstrate its feasibility and performance. The proposed model will enable a plant or a central warehouse to successfully implement an effective TOC-SCRS. Copyright © 2013 Inderscience Enterprises Ltd

Application of advanced fault diagnosis technology in electric locomotives

As the continuous development of intelligent mechatronic systems and robots, the fault diagnosis technology is making full advances in many practical applications. In this paper, an advanced fault diagnosis system, which consists of logical control units, microcontrollers, colour display screens and an industry PC, is developed for SS7E locomotives in China. Based on thoroughly analysing the structures and control principles, a full set of digital checkpoints and fault points of SS7E are presented. The method to obtain diagnosis rules from the fault tree is described and the high-efficiency reasoning mechanism is deduced. The intelligent fault diagnosis knowledge base of SS7E is constructed and the data structure is explained. Finally, an online instance of the SS7E locomotive fault diagnosis system interface is shown

3D hand gesture tracking and recognition for controlling an intelligent wheelchair

Hand gesture recognition is a user-friendly and intuitive means for human machine interaction. This paper proposes a novel 3D hand gesture recognition method for controlling an intelligent wheelchair based on both colour and depth information. Image depth information of human palm is obtained by a 3D Kinect vision sensor and then its position is obtained through the hand analysis module in OpenNI. The improved Centroid Distance Function is used to extract 3D hand trajectory features, while hidden Markov model (HMM) is applied to train samples and recognise hand gesture trajectories. Finally, the recognition results are converted into control commands through an ad hoc network and sent to an intelligent wheelchair for its motion control. Experiment results show that the proposed method has good invariance to lighting changes, hand rotation and scaling conditions and is very robust to background interference. Copyright © 2014 Inderscience Enterprises Ltd

A pyramidal deep learning architecture for human action recognition

This paper proposes a pyramidal deep learning architecture for human action recognition based on depth images from a 3D vision sensor. This method consists of three steps: 1) pre-processing depth image; 2) building a hidden deep neural network; 3) pattern recognition. A novel pyramidal stacked de-noising auto-encoder (pSDAE) is proposed to build a deep neural network so that its weights can be learnt layer by layer. A feed-forward neural network based on the deep learned weights is trained to classify each action pattern. Based on the experimental results from the Kinect dataset of human actions sampled in experiments, it is clear that the proposed approach outperforms the existing classical classify method. The robust experiment results on the Weizmann dataset show the good expansibility of the proposed method. Copyright © 2014 Inderscience Enterprises Ltd