Motion capture based on wearable inertial sensors is a promising technique for swimmers’ training. To apply motion capture techniques properly, a swimming motion evaluation method based on inertial motion capture technology is proposed. Our proposed method uses a multisensor data fusion algorithm for swimmers’ attitude estimation, and the swimming posture is reconstructed in combination with a human biomechanical model. Furthermore, a comparative experiment between our proposed motion capture system and the NDI motion tracking system shows that our system performs reliably and accurately, and the estimation errors are well controlled. In addition, the accuracy of the orientation estimation algorithm ranges from $\text{1.65}^{\circ }$ to $\text{3.66}^{\circ }$ . The system can capture swimmers’ lumbar spine in four competitive swimming styles. A kinematic analysis of lumbar spine movement indicates that the patterns of swimmers’ lumbar spine movement can be used to evaluate training performance and provide quantitative data for swimmers.

A correct riding posture is essential for a good performance in equestrian. This paper proposes a method of equestrian analysis based on body sensor networks. First of all, raw sensor data corresponding to professional (PRO) riders and beginners (BEGs) under different horse gaits were collected by inertial sensor nodes. Then, after an initial sensor alignment with the reduced error, a kind of motion reconstruction method based on the gradient descent method was used to fuse the sensor data to constantly update the rider's attitude information. In addition, to evaluate the tracking accuracy of joint angle, an Northern Digital optical system was deployed to verify the accuracy of our method. Using the attitude information, the motions of riders in equestrian sports were reconstructed by combining the method of human kinematics. Finally, in view of the two aspects of exercise intensity and joint angle, a quantitative analysis of key features between PRO and BEG in walking and rising trot was carried out by combining with the kinematic analysis method. Experiments conducted in this paper demonstrate that the estimation errors are well controlled, and our method can better reflect the similarities and differences between PRO and BEG in equestrian sports and provide quantitative data for equestrian coaches or riders, which can be used to improve the riding skills of riders.

Wearable motion capture technique is widely used in kinematic analysis, which contributes to understanding motion patterns and provides quantitative data on human postures. Swimming stroke phase plays an important role in spatialtemporal swimming parameters. As a sporting pattern that involves all limbs, the swimming phase is more complicated than gait phase and makes the swimming phase segmentation a new issue of pattern recognition. The paper focuses on the swimming phase segmentation as pattern classification. By analyzing the human posture data given by motion capture system, swimming phase could be described qualitatively and used to obtain posture features. The swimming phase of the four competitive swimming styles is studied in the paper and classified accurately. In the 10-fold cross validation, the mean values of accuracy, sensitivity, and specificity could reach 98.22%, 95.65%, 98.67%, respectively, under the 2.5 ms timing tolerance. In terms of leave-one-subjectout cross validation, performance metrics perform best under a relatively small timing tolerance. The results of the experiment show that the study could well address the issue of swimming phase segmentation and provide spatial-temporal parameters for further swimming motion analysis.

Human gait reflects health condition and is widely adopted as a diagnostic basis in clinical practice. This research adopts compact inertial sensor nodes to monitor the function of human lower limbs, which implies the most fundamental locomotion ability. The proposed wearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy. It can output the kinematic parameters of joint flexion and extension, as well as the displacement data of human limbs. The experimental results provide strong support for quick access to accurate human gait data. This paper aims to provide a clue for how to learn more about gait posture and how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database, it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injury risks, and chronic pain, and provides guidance for arranging personalized rehabilitation programs for patients. The proposed framework may eventually become a useful tool for continually monitoring spatio-temporal gait parameters and decision-making in an ambulatory environment.

Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist’s motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist’s attitude information after sensor calibration, and then the motions of canoeist’s actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learning algorithm was used as the classification method to divide the stroke cycle into different phases, including propulsion-phase and recovery-phase, a quantitative kinematic analysis was carried out. Experiments conducted in this paper demonstrated that our method possesses the capacity to reveal the similarities and differences between novice and coach, the whole process of canoeist’s motions can be analyzed with satisfactory accuracy validated by videography method. It can provide quantitative data for coaches or athletes, which can be used to improve the skills of rowers.