Displacement estimation in micro-sensor motion capture

The objective of integrated navigation systems is to estimate the navigation states reliability, which includes position and velocity of the aircraft, from the inertial measurements and the external aids despite the possible occurrence of devices faults. In this study three different integrated systems have been studied without taking into consideration any faulty case: integrated baro-inertial (IBI), integrated inertial-GPS (IIG) and integrated baro-inertial-GPS (IBIG). IBI and IIG navigation systems have been designed using a complementary Kalman filter (CKF), which estimates the error model parameters. Simulation results show that fluctuations for IIG altimeter errors are the largest ones for the three altimeters. The second largest errors are acquired by IBI altimeter and finally, the best results are obtained by the fused IBIG altimeter.

Personal Dead Reckoning based on foot-mounted Inertial Measurement Units is a research hotspot in the field of positioning and navigation in recent years. This paper conducts a targeted research on the application of current mainstream attitude and heading reference system (AHRS) algorithm in the foot inertial navigation positioning. Through open datasets, the positioning accuracy and directional accuracy of 9-state complementary Kalman filter (CKF) are compared and analyzed among the conventional algorithm, Mahony algorithm, and Madgwick algorithm, in which the Madgwick algorithm can achieve the best positioning results. And on this basis, for the Madgwick algorithm, it is verified that it can help improve the positioning accuracy of 15-state CKF under the assistive technologies of zero angular rate update (ZARU) and heuristic heading reduction (HDR). The adaptive zerospeed detection algorithm is designed, and the threshold value of zero-speed detection is set dynamically through tracking the variable of speed in CKF, which can detect the time period of zero-speed state more accurately, thus further improving the correction of directional errors. Finally, the effectiveness of the proposed algorithm is further proved by actual data.

In human gait motion analysis, which is one useful method for efficient physical rehabilitation to define various quantitative evaluation indices, joint angles and joint loads are measured during gait. Recently, it is thought that trans-femoral amputees with the prosthetic limb must regain motion pattern by refined rehabilitation program using joint moments as a representative joint loads and understanding them is indispensable for biomechanical consideration. On the other hand, human keeps a high correlation among the motion of all joints during gait. Joint motion analysis of the correlation extracts a few simultaneously activating segmental coordination patterns, and the structure of the intersegmental coordination is attracting attention to an expected relationship with a control strategy. However, when the evaluation method using singular value decomposition has been applied to only joint angles of the lower limb, joint moments related to the rotational motion of the joints have not yet been considered. In this paper, a novel quantitative analysis method concerning rotational motion of lower limb joints by using joint moments as well as joint angles aims to be proposed. Each parameter of the healthy subject and trans-femoral amputee is measured by using mobile force plate and attitude sensor, split-belt treadmill simultaneously and their motion patterns are analyzed by the proposed method. As a result of the experiments, patterns of each evaluation index are obtained as the remarkably different feature quantities between healthy subject and trans-femoral amputee especially pertaining to joint moments. Finally, the effectiveness of the quantitative analysis concerning human motion is validated.

Human motion is a complex activity of the central nervous system (CNS) and muscles. Performance of a human motion can be decomposed into three components: estimation of trajectory; calculation of required signal for muscles; and performance of movement. The CNS conducts the first two tasks and the muscles perform the third task. This paper presents the development of a mathematical model and a Matlab Simulink plant for human gait movement. An internal model predictive control (MPC) is setup and plays as the human CNS to estimate the trajectory and to calculate the required signal for muscles to perform the movement. MPC calculates the required torques for each joint and generate optimal trajectories subject to human physical constraints for muscles. Results of simulation are analyzed and compared to the real human gait motions captured by a real motion capture system (Vicon). Finally, conclusions and recommendations from this research are withdrawn.

Study on the impacts of human walking on indoor particles dispersion using momentum theory method

In recent years, researches and developments of robots working in human living space are increasing with development of robot technologies. Environmental information acquisition and human existence detection and estimation are necessary to control the robots moving in this kind human living space. In this study, we propose human motion map based on a concept of representing extracted human behavior in the human living space as a map, by using human existence estimation function and mapping function of SLAM. For building the human motion map, human walking area interpolating algorithm and map update algorithm are proposed. Finally, we implemented human motion map generating system with above-mentioned two algorithms on a mobile robot. The human motion map could be used as information for both motion planning of robots and further estimation of human motion in dynamic environment. Additionally, human motion map can express the behavior of more human motion by performing further accumulation of human walking motion.

Onboard attitude estimation for a ground vehicle is persuaded by its application in active anti-roll bar design. Conventionally, the attitude estimation problem for a ground vehicle is a complex one, and computationally, its solution is very intensive. Lateral load transfer is an important parameter which should be taken in account for all roll stability control systems. This parameter is directly related to vehicle roll angle, which can be measured using devices such as dual antenna global positioning system (GPS) which is a costly technique, and this led to the current work in which we developed a simple and robust attitude estimation technique that is tested on a ground vehicle for roll mitigation. In the first phase Luenberger and Sliding mode observer is implemented using simplest roll dynamics model to measure the roll angle of a vehicle and the validation of results is carried using commercial software, CarSim® (CarSim, Ann Arbor, MI, USA). In the second phase of research, complementary and Kalman filters have been designed for attitude estimation. In the third phase, a low-cost inertial measurement unit (IMU) is mounted on a vehicle, and both the complementary filter (CF) and Kalman filter (KF) are applied independently to measure the data for both smooth and uneven terrains at four different frequencies. We compared the simulated and real-time results of roll and pitch angles obtained using the complementary and Kalman filters. Using the proposed method, the achieved root mean square error (RMSE) is less than 0.73 degree for pitch and 0.68 degree for roll, with a sample time of 2 ms. Thus, a warning signal can be generated to mitigate roll over. Hence, we claim that our proposed method can provide a low-cost solution to the roll-over problem for a road vehicle.

This manuscript presents a method for motion capture–based manufacturing time and motion studies. The proposed human motion analytics system uses motion capture technology to collect, transform, store, and analyze data from repetitive physical motions performed by manufacturing workers. The system supports the isolation of basic simple motions for analysis using statistical process control and data analytics techniques. The proposed method has resulted in the ability to identify patterns of repetitive motions and statistically significant deviations from those patterns.

This study uses theory and experiments to investigate the relationship between the passive stiffness of series elastic actuators and torque tracking performance in lower-limb exoskeletons during human walking. Through theoretical analysis with our simplified system model, we found that the optimal passive stiffness matches the slope of the desired torque-angle relationship. We also conjectured that a bandwidth limit resulted in a maximum rate of change in torque error that can be commanded through control input, which is fixed across desired and passive stiffness conditions. This led to hypotheses about the interactions among optimal control gains, passive stiffness and desired quasi-stiffness. Walking experiments were conducted with multiple angle-based desired torque curves. The observed lowest torque tracking errors identified for each combination of desired and passive stiffnesses were shown to be linearly proportional to the magnitude of the difference between the two stiffnesses. The proportional gains corresponding to the lowest observed errors were seen inversely proportional to passive stiffness values and to desired stiffness. These findings supported our hypotheses, and provide guidance to application-specific hardware customization as well as controller design for torque-controlled robotic legged locomotion.

This paper proposes a fusion algorithm based on explicit complementary filter (ECF) and Kalman filter using open source autopilot controller to realize flight of indoor quadrotor UAV in the environment of motion capture system (i.e. Optitrack). The fusion algorithm establishes the state equation model of the system based on the attitude angle differential equation. The attitude angle after ECF is used as the observation of the system, and then the extended Kalman filter (EKF) algorithm is used to fuse the measured data of gyroscope, Optitrack, accelerometer and magnetometer. To verify the effectiveness of the algorithm, static and dynamic flight experiments are conducted indoors with the Optitrack environment using a UAV equipped with an IMU. The experimental results show that the fusion algorithm can restrain the attitude angle drift and filter out the noise well when placed in static state, and can track the change of the attitude of the UAV quickly in flight, which improves the attitude angle accuracy of estimation.

Human motion capture is an important technology in the three-dimensional character animation production.Since the motion capture process is very expensive and complex,in this paper we introduce a new method that captures the human motion data from the inertial motion sensors attached to the user's limbs.The user wears multiple wireless inertial motion sensors with accelerometer and magnetometer,and the sensor data are sent to the computer using the wireless sensor network.Levenberg-Marquardt based optimization algorithm is applied to estimate the three-dimensional orientation of the inertial sensor and then bind the obtained orientation quaternion to the skeleton of the animation character.The root position of the animation character is modified inversely from the relative status of the supporting leg and the ground in order to make the walking motion more realistic.The experimental results show that our method can capture the user's motion effectively and produce accurate and realistic motion capture data.

This study describes a surgical technique and evaluates the clinical outcomes in small breed dogs with cranial cruciate ligament rupture (CCLR) treated with tibial plateau leveling osteotomy (TPLO). Seven skeletally mature dogs weighing less than 15 kg underwent unilateral TPLO to stabilize the stifle joint with CCLR. Clinical evaluation was performed via visual lameness score, range of motion (ROM), and thigh girth circumference (TC). Postoperative complications were recorded. All patients reached a grade 1 score at 1 week and grade 0 at 8 weeks postoperatively. The mean operated limb extension angle was 98.11%, 99.07%, and 98.73% of the mean extension angle of the contralateral limb at 4 weeks, 8 weeks, and 12 weeks postoperatively, respectively. The mean operated limb flexion angle was 98.07%, 95.88%, and 96.35% of the mean flexion angle of the contralateral limb at 4 weeks, 8 weeks, and 12 weeks postoperatively, respectively. The mean TC of the operated limb was 92.95%, 93.68%, and 95.44% of the mean TC of the normal limb at 4 weeks, 8 weeks, and 12 weeks postoperatively, respectively. Our outcomes for TPLO in small breed dogs are not worse than those previously reported for large breed dogs. Based on the result in the present study, CCLR in small dogs can be successfully managed with TPLO, as done in large breed dogs.

Indoor ventilation and human movement are important factors affecting the spread of droplets. In this paper, the effects of indoor wind speed and human movement on the propagation and diffusion of cough ejected droplets are investigated using computational fluid dynamics. The effects of cough non-isothermal jet flow, indoor wind velocity, and human movement on the transmission of exhaled droplets were studied. The droplet size ranges in dynamic indoor environments were redefined based on the risk of droplet spread dispersal. The buoyant flux, gravity, inertial forces, and drag forces that affect the droplet motion were evaluated. The results show that droplet spread above 40 μm is limited by indoor airflow or human-induced wake and settles rapidly under the influence of gravity. The indoor air velocity makes the droplets of less than 40 μm to spread within 4 m in front of the cougher within 18 s. The induced wake allowed the lateral movement of droplets between the coughing person and the moving person, but the diffusion effect was much less than the effect of indoor air velocity. The combined effect of indoor air velocity and human movement makes the indoor flow field very complex. It greatly delays the deposition time of 10–40 μm droplets (2.2 times) and makes droplets below 10 μm to spread rapidly to distant locations by the induced wake, increasing the risk of infection. Further research is required to quantify the influence of parameters, such as the human movement and evaporation of droplets among others in indoor dynamic environment.

Understanding the underlying concepts of human locomotion is important for many fields of research. Based on the assumption that human motions are optimal, we propose an inverse optimal control (IOC) based approach to identify the optimality criteria in human walking. To this end, human walking is modeled as a non-linear optimal control problem with a linear combination of elementary optimality functions as objective and a hybrid dynamics multi-body system as constraints. The developed IOC-framework is set up in a modular way and exploits the natural bi-level structure of the problem. It allows for a great flexibility in the choice of outer optimization techniques and inner dynamic models. In the present work, we use the developed IOC approach to identify weights of seven elementary criteria for seven walking motions captured from six different subjects. The considered optimality criteria address the minimization of joint torques for four sets of joints, head stabilization, the step length, and the step frequency. For all trials the algorithm performs successfully. Even though the identified weights differ observably between subjects, which explains the different walking styles, the correlation matrix gives rise to the hypothesis that there exists a significant correlation of optimality across subjects. The identification of optimality criteria in human walking is a very important issue for all disciplines, where a prediction of human behavior is needed. For example in medical applications to improve therapies or to develop new mobility devices, in sport science to improve training plans or in humanoid robotics to develop new walking strategies.

In this paper, a nonlinear optimization solver is proposed to establish the human upper body motion capture system, including the orientation of each bone joint and the global position of the hip bone. 5 IMUs and one Kinect are used, the rotation data and acceleration data of IMUs, Kinect skeleton position data and human pose prior are fused in a non-linear optimization way. The method proposed in this paper combines the characteristics of Kinect data with high accuracy and IMU data with stability, which can make up for the accuracy of human pose capture in dynamic, magnetically disturbed and occlusion conditions. In order to verify the practicability of the human motion capture system proposed in this paper, the system is firstly applied to real-time human motion capture, then a simple human-robot collaboration (HRC) case is implemented, which is very common in modern collaborative medical field. The experimental results show that the proposed human motion capture system can achieve stable human motion measurement, which can meet the needs of HRC scenarios.