Respiratory Inductance Plethysmography (RIP) is commonly used to detect the respiratory motions of chest and abdomen during sleep. However, this method is not suitable for daily use due to its strong constraints. Many unconstrained methods have been developed but they have a common problem in distinguishing the chest and abdominal motions, which are required for understanding the type of sleep apnea. In this study, we proposed an unconstrained method to distinguish chest and abdominal motions by measuring the pressure fluctuations from the chest and abdomen acting on a mattress. We then investigated the feasibility of detecting respiratory motions by using a flexible tactile sensor array, which was laid on a bed like a bed sheet. Ten healthy subjects (five males and five females) were tested and the results were compared with those measured with RIP band sensors. It was confirmed that the sensor array could measure the motions of the chest and abdomen and obtain the respiratory rate at a high precision. However, the phase of the pressure fluctuations measured with the sensor array might disagree with the outputs of the RIP band sensors, depending on the gender and lying position of the subject. The reason was discussed with a dynamic model, which showed that the disagreement might have been related to the coupling of the chest and abdomen. In addition, the influence of the stiffness of the mattress on the pressure fluctuations was investigated. The results showed that mattress stiffness had little effect on the measurement.

Aiming at the problems of small good workspace, many singular configurations, and limited carrying capacity of non-redundant parallel mechanisms, a full-redundant drive parallel mechanism is designed and developed, and its performance evaluation, good workspace identification, and scale optimization design are studied. First, the kinematics analysis of the planar 6R parallel mechanism is completed. Then, the motion/force transmission performance evaluation index of the mechanism is established, and the singularity analysis of the mechanism is completed. Based on this, the fully redundant driving mode of the mechanism is determined, and the good transmission workspace of the mechanism in this mode is identified. Then, the mapping relationship between the performance and scale of the mechanism is established by using the space model theory, and the scale optimization of the mechanism is completed. Finally, the robot prototype is made according to the optimal scale, and the performance verification is carried out based on the research of dynamics and control strategy. The results show that the fully redundant actuation parallel mechanism obtained by design optimization has high precision and large bearing capacity. The position repeatability and position accuracy are 0.053 mm and 0.635 mm, respectively, and the load weight ratio can reach 15.83%. The research results of this paper complement and improve the performance evaluation and scale optimization system of redundantly actuated parallel mechanisms.

Due to the errors in manufacturing and assembly, there are differences between the actual model and the theoretical model of the robot, which affects the positioning accuracy of the robot end-effector. In order to improve the accuracy of robot end-effector position, a robot error compensation strategy based on error sensitivity is proposed.Firstly, the robot kinematic model is established by Denavit–Hartenberg method, and the sensitivity of end-effector position error is analyzed. According to the influence degree of different kinematic parameters on the robot end-effector position accuracy in the whole workspace, different weights are given to different kinematic parameters. Secondly, the kinematic error model is established, and the redundancy of the error parameter matrix is analyzed to obtain an independent error model. Thirdly, based on the error sensitivity analysis, a weighted Levenberg–Marquard algorithm with adaptive penalty factor is proposed, and the kinematic parameters are iteratively identified. Finally, an error compensation experiment is carried out by using a universal serial six-degree-of-freedom robot. The experimental results show that the maximum error, mean absolute error and root mean square error of the position error on the test set are reduced by 90.75%, 89.86% and 95.64% respectively. The research in this paper provides a theoretical basis for robot end error compensation.

An accurate and timely prediction of falls in a complex environment is vital for population groups such as workers, the elderly, and power-assisted exoskeleton wearers. Enhancing the universality of fall warning methods has been regarded as one of the primary challenges in the field of precise anomaly detection and fall prediction. To address this issue, a gait abnormality detection and fall warning method is proposed in this paper. First, a wearable data acquisition system integrated with inertial measurement units and capacitive plantar pressure sensors is used to obtain real data on feet. Second, a human musculoskeletal model is built in AnyBody software to obtain simulation data on feet. By comparison, the effectiveness of the simulation model is verified and the characteristics of abnormal gait are determined. Third, a backpropagation network is cleverly combined with the hidden Markov model. The cooperation of neural network and probabilistic model is employed to detect the abnormal gait sequence before falling and make a first-level fall warning. Then, a mapping model between the real and simulation plantar pressures is constructed using a multiple linear regression algorithm to weaken the difference of stability thresholds of different people and conduct second-level fall warning. Finally, two common fall patterns, tripping and slipping, are used to test the proposed fall waring method. The average sensitivity, specificity, and accuracy of the gait anomaly detection and stability judgment are used as evaluation metrics. The results indicate that the proposed method achieves average sensitivity, specificity, and accuracy of 100%, 97%, and 98.5%, and of 100%, 96%, and 98%, on tripping and slipping patterns, respectively. Moreover, the proposed method could assess pedestrian stability and provide fall warnings of more than 300 ms before a fall occurs.