Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health care, and navigation. Because of the agility, upper limb motion estimation is the most difficult problem in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and then estimate their movements separately; therefore, the estimated motion are always with serious distortion. In this paper, we propose a novel ubiquitous upper limb motion estimation method using wearable microsensors, which concentrates on modeling the relationship of the movements between upper arm and forearm. Exploration of the skeleton structure as a link structure with 5 degrees of freedom is firstly proposed to model human upper limb motion. After that, parameters are defined according to Denavit-Hartenberg convention, forward kinematic equations of upper limb are derived, and an unscented Kalman filter is invoked to estimate the defined parameters. The experimental results have shown the feasibility and effectiveness of the proposed upper limb motion capture and analysis algorithm.
The study of three-dimensional human kinematics has significant impacts on medical and healthcare technology innovations. As a non-invasive technology, optoelectronic stereophotogrammetry is widely used for in-vivo locomotor evaluations. However, relatively high testing difficulties, poor testing accuracies, and high analysis complexities prohibit its further employment. The objective of this study is to explore an improved modeling technique for quantitative measurement and analysis of human locomotion. Firstly, a 3D whole body model of 17 rigid segments was developed to describe human locomotion. Subsequently, a novel infrared reflective marker cluster for 17 body segments was constructed to calibrate and record the 3D segmental position and orientation of each functional body region simultaneously with high spatial accuracy. In addition, the novel calibration procedure and the conception of kinematic coupling of human locomotion were proposed to investigate the segmental functional characteristics of human motion. Eight healthy male subjects were evaluated with walking and running experiments using the Qualisys motion capture system. The experimental results demonstrated the followings: (i) The kinematic coupling of the upper limbs and the lower limbs both showed the significant characteristics of joint motion, while the torso motion of human possessed remarkable features of segmental motion; (ii) flexion/extension was the main motion feature in sagittal plane, while the lateral bending in coronal plane and the axial rotation in transverse plane were subsidiary motions during an entire walking cycle regarding to all the segments of the human body; (iii) compared with conventional methods, the improved techniques have a competitive advantage in the convenient measurement and accurate analysis of the segmental dynamic functional characteristics during human locomotion. The modeling technique proposed in this paper has great potentials in rehabilitation engineering as well as ergonomics and biomimetic engineering
Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities.Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring,however,relatively little attention is paid to accuracy and reliability.In previous study,a wearable respiration biofeedback system was designed.In this work,three kinds of signals were mixed to extract respiratory rate,i.e.,respiration inductive plethysmography(RIP),3D-acceleration and ECG.In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate.It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37%of a reference gold standard.Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17%of a reference gold standard.
