Zero velocity updates (ZUPT) is an effective way for the foot-mounted inertial pedestrian navigation systems. For the ZUPT technique to work properly, it is necessary to correctly detect the stance phase of each gait cycle. An adaptive stance-phase detection method is proposed based solely on an inertial sensor, which deals with the measurement fluctuations in swing and stance phases differently, and applies a clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait phases. The roles of the detection parameters and the relationship between them are analyzed to offer some suggestions for parameter tuning. Detection performance is evaluated with multisubject experimental data collected at varying walking speeds. The evaluation results show that the proposed detection method performs well in the presence of measurement fluctuations, which can make the detection of stance phases more robust and the choice of detection parameters more flexible.

Foot-mounted inertial navigation is an important issue in areas such as pedestrian localization, gait analysis, and sport training. However, low-cost inertial sensors suffer from several errors that make the navigation results less convincing. In this paper, a multi-sensor approach with one sensor on each foot is presented to reduce the system heading drift. Through dual-gait analysis, gait parameters between two feet are employed to make the non-collocated and uncoupled subsystems be related to each other. A step length estimator based on an inverted pendulum model is developed to derive a relative position vector between the two foot-mounted sensors rather than a distance scalar. A Kalman-type filter with one time update and two measurement updates is developed to fuse the velocity and position observations at foot and person levels, respectively. Experiments were conducted by four healthy subjects, and experimental results show that the proposed sensor fusion method can effectively reduce the heading drift of inertial navigation and make the captured dual-foot motion closer to its actual process.

Wearable inertial motion capture, a new type of motion capture technology, mainly estimates the human posture in 3-D space through multisensor data fusion. The available method for sensor fusion is usually aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of a complex magnetic field environment. In this article, an extended Kalman filter (EKF) data fusion method is proposed to fuse the 9-axis sensor data. Meanwhile, the heuristic drift reduction (HDR) method is used to calibrate the accumulated error of a heading angle. In addition, the position in 3-D space is estimated by the foot-mounted zero-velocity-update (ZUPT) technique. Combining 3-D attitude and position, a biomechanical model of the human body is established to track the motion of a real human body. The EKF algorithm and position estimation methods are benchmarked against the golden standard, optical motion capture system, for various indoor experiments. In addition, various outdoor experiments are also conducted to verify the reliability of the proposed method. The results show that the proposed algorithm outperforms the available attitude estimation model in motion tracking and is feasible for 3-D human motion capture.

Motion capture technology has been widely used in the sport analysis to improve their performance and reduce the injury risk. Kayak, a popular outdoor sport, employs the coordination of multiple muscles and skeletons, especially those of upper limbs that must be investigated carefully. The fine-time phase segmentation of rowing cycle plays an important role in analyzing kayaker’s technique. Aiming at the problem of laborious manual phase labeling in the traditional video analysis method, an automatic phase segmentation method for kayak rowing is proposed combined with a machine learning algorithm. In this article, inertial sensors and a data fusion algorithm are used to calculate the joint angles between arm and trunk, left elbow and right elbow when the athlete is rowing. According to the permutation and combination principle, the angle sequence is combined in nine different ways, and four machine learning algorithms (decision tree, support vector machine, $k$ -nearest neighbor, bagging ensemble learning) are used to study the effects of different combinations on rowing phase division. Among them, the precision of phase segmentation becomes higher with the increase of motion information. The combination of arm to trunk joint angle only needs three data collection nodes; thus, the computational cost is smaller; moreover, all the four algorithms show good classification accuracy (up to 98.1%). The results indicating that the combination of arm to trunk joint angle and support vector machine algorithm could better complete the task of the phase segmentation for kayak rowing.