Catálogo de publicaciones - libros

Muscular coordination analysis of lower limbs during rising from a squat is one of the important categories in rehabilitation engineering and gymnastic science. This paper describes an efficient biomechanical model of the human lower limb with the aim of simulating the real human rising from a squat with lifting. To understand how intermuscular control coordinates limb muscle excitations the optimal control technique is used to solve the muscle forces sharing problem. The validity of the model is assessed comparing the calculated muscle excitations with the registered surface electromyogramm (EMG) of the muscles. The results show that synergistic muscles are build up by the neural control signals using a minimum fatigue criterion during human squat lifting, with distinct phases that include the acceleration during the initial movement and the posture at the final specified position. Synergistic muscular groups can be used to simplify motor control, and are essential to reduce the number of controlled parameters and amount of information needing to be analyzed in the performance of any motor act.

A three-dimension, neuromusculoskeletal model of the human upper limb, consisting of 30 muscle–tendon systems, was combined with dynamic optimization theory to simulate reaching-grasping movements. The model was verified using experimental kinematics, muscle forces, and electromyographic(EMG) data from volunteer subjects performing reaching-grasping movements. Despite joint redundancy, the topological invariance was observed in the trajectories of different task performance, and the linear relationships between joints covariation were exhibited. Quantitative comparisons of the model predictions and muscle activations obtained from experiment show that the minimum torque-change criterion is a valid measure of reaching-grasping performance.

We present a simple and low cost 3D shape acquisition method that can measure the objects with interreflection and high light. The capture system employs a projector to project line shifting shadow on the object, and a digital camera to record videos (image sequence) of the object and the distorted shadow. A novel spacetime edge finding method is introduced to get the shadow edge accurately and overcome the interreflection and high light. The 3D data of the object are got by using the spacetime information. A post-processing 3D filter is proposed to filter the bad data. The 3D filter makes full use of the neighborhoods’ geometric constrains and the view point constrain.

This paper presents an automatic method to extract joints accurately from scanned human body shape. Firstly, model is divided into slices by a group of horizontal parallel planes and primary landmarks are determinated according to the slices. Additionally, we propose a model intersection method based on direction of limbs. Model is separated into five segments including torso and four limbs and approximate direction of each segment can be calculated according to the primary landmarks. Five groups of parallel planes perpendicular to corresponding segments are employed to divide each segment into slices, and contours are calculated by intersection detection between planes and triangles. Finally, we propose a circularity function differentiation technique to extract key contours at joints from segments and the joints can be calculated according to centroids of the key contours. The experimental results demonstrate that the method has more advantages than the conventional ones, especially in algorithm’s accuracy and robustness.

Electroencephalogram (EEG) is a useful bioelectrical signal in pattern recognition of hand activities because of its following characteristics: (1) patterns of EEG are different when doing diverse movements and mental tasks; (2) EEG can be real-timely or instantly extracted; (3) the measurement of EEG can reach an enough precision. A new approach for the pattern recognition of four complicated hand activities based on EEG is presented in this paper, in which each piece of raw data sequence for EEG signal is decomposed by wavelet transform (WT) to form a matrix, and the singular value of the matrix is extracted by singular value decomposition (SVD). And then the singular value, as the feature vector, is input to the artificial neural network (ANN) to discriminate the four hand activities including grasping a football, a small bar, a cylinder and a hard paper. Finally the research results show the correct classification rate of 89% was achieved by the approach mentioned above.

In this paper, we present an Orthogonal Locality Preserving Projection based (OLPP) approach to capture three-dimensional human motion from monocular images. From the motion capture data residing in high dimension space of human activities, we extract the motion base space in which human pose can be described essentially and concisely by more controllable way. This is actually a dimensionality reduction process completed in the framework of OLPP. And then, the structure of this space corresponding to special activity such as walking motion is explored with data clustering. Pose recovering is performed in the generative framework. For the single image, Gaussian mixture model is used to generate candidates of the 3D pose. The shape context is the common descriptor of image silhouette feature and synthetical feature of human model. We get the shortlist of 3D poses by measuring the shape contexts matching cost between image features and the synthetical features. In tracking situation, an AR model trained by the example sequence produces almost accurate pose predictions. Experiments demonstrate that the proposed approach works well.

Acquisition of large scale good quality training samples is becoming a major issue in machine learning based human motion analysis. This paper presents a method to simulate continuous gross human body motion with the intention to establish a human motion corpus for learning and recognition. The simulation is achieved by a temporal-spatialtemporal decomposition of human motion into actions, joint actions and actionlets based on the human kinematic model. The actionlet models the primitive moving phase of a joint and represents the muscle movement governed by kinesiological principles. Joint actions and body actions are constructed from actionlets through constrained concatenation and synchronization. Methods for concatenation and synchronization are proposed in this paper. An action corpus with small number of action vocabularies is created to verify the feasibility of the proposed method.

Although the term ’user experience’ has become ubiquitous, variations in its conceptualization can make design objectives unclear. This paper proposes a simple framework for conceptualizing the components of user experience in order to communicate with UX stakeholders and advance goal setting and measurement in applied settings. A deeper understanding of the components of experience provide a greater ability to set strategic direction for the user experience, guide design goals, and assess user experience outcomes. In educating stakeholders on a more complete view of user experience, UCD practitioners have the opportunity to play a key role in planning the level of user experience quality for the product user experience and influencing where user experience studies will have the most impact on products.

Computational cognitive models are used to validate psychological theories of cognition, to formally describe how complex tasks are performed, and to predict human performance on novel tasks. Most cognitive models have very limited models of how the body interacts with the environment. The present research examines a simple human-machine interaction task and a simple object manipulation task. A cognitive model is integrated with a human avatar within a virtual environment in order to model both tasks.

Recently, more and more devices everywhere are getting “smarter” with a multi-modal hierarchical menu and form interface. One of the main points of the menu or interface design is to provide users with ease-to-use operation environment. This make them not only learn efficiently but also feel fun (interested) in the process of learning. However, there is no one concept of design suit everyone because the needs and purposes of users are much different from individuals. To satisfy them, the varied design concepts have been suggested to fit for their distinct perceptions and experiences. Consequently, new assessment, called usability, is also required to estimate whether the design concepts are good or not. Therefore, this study attempted to investigate into the usability of 3D interface design. For that, 3 types of main menu of the mobile phone’s interface metaphor design were developed as stimuli with different degree of abstract in this study. Then, a four-phase experiment was conducted to explore the usability evaluation of 3 types of metaphorical interface design with different degree of abstract, including: (1) to investigate users’ opinions on a mobile phone’s interface design; (2) to verify whether the simulated graphics and interactions corresponding to the metaphors intended (pilot study); (3) to measure the usability of 3 types of metaphorical interface design simulated in this study; (4) to compare the preference for any one of the 3 types of metaphorical interface design. The experimental procedures and the results of the analysis would be interpreted respectively according to different phases. Additionally, the degree of abstract in the metaphorical interface design was defined by the average ratings in phase 1: metaphor 3 were regarded as abstract interface design and metaphor 1 and metaphor 2 were regarded as concrete interface designs, but the degree of concrete in metaphor 1 was stronger than in metaphor 2.