This paper addresses a problem of event-triggered state estimation for a linear time-varying Gaussian system over the Internet of Underwater Things (IoUT), where the IoUT is a hybrid topology including underwater acoustic wireless sensors and surface wireless network. Distributed state estimation aims to reconstruct the system state using noisy measurements and local neighbor information, both of which are transmitted via the IoUT. However, IoUT is subject to limited energy and communication bandwidth. Each node, especially applied in underwater case, selectively transmits necessary data to maintain a minimum communication load, and thus to improve energy efficiency and prolong network lifetime. To achieve this goal, we design two event-triggered strategies for typical types of wireless communication channels, of which one channel is used to transmit the local posterior information pair of filter and the other is used to transmit measurement of sensor. Then, based on the covariance intersection fusion rule and the event-triggered strategies, we develop a novel consensus-based distributed state estimator with dual event-triggered communication in a recursive form. Further, after guaranteeing the network connectivity and system collective observability constraints, we derive the uniformly mean-square upper bound of the estimation error of each node. Finally, we provide an example about underwater target tracking to illustrate the effectiveness of the proposed approach.

This paper addresses the output synchronization problem in heterogeneous leader-follower multi-agent systems with unknown follower dynamics. A data-driven control strategy is proposed, wherein a tracking controller is designed based on finite-horizon input-output data. The data is further employed to solve the output regulator equation, thereby facilitating synchronization of the followers’ outputs with that of the leader. To account for bounded noise interference, the matrix S-lemma is employed to reformulate the synchronization conditions into a tractable convex optimization problem. Furthermore, it is analytically shown that the synchronization error remains bounded and is explicitly constrained by the solution of the optimization problem. The proposed methodology is substantiated through comprehensive numerical simulations and experimental validations, demonstrating its effectiveness and robustness.

The human foot arch plays a significant role in bearing weight, keeping balance, and walking efficiently. In this study, we present a pneumatic arch support insole (PASI) and a foot arch stiffness-based dynamic plantar support control to reduce the metabolic cost of walking. We first obtain the foot arch quasi-stiffness estimation over the gait phase by conducting multiple subject experiments. The design and modeling of the PASI and foot-insole interactions are then discussed. A model predictive control scheme is presented to dynamically regulate the foot arch stiffness over the gait phase by using the active PASI. We validate the system experimentally and conduct multi-subject walking tests. The results show that dynamic plantar support control of the foot arch stiffness reduces the metabolic cost by 7.99% compared to regular walking. In contrast, passive support without dynamic regulation increases the metabolic cost by 6.60%. The new pneumatic insoles and dynamic support method demonstrate promising potential for everyday and medical applications.

Abstract This paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%.

Load-carrying capability is an essential criterion in legged robots' practical application. This paper proposes an unpowered hydraulic auxiliary system to improve the legged robot's loading capability and energy efficiency. For humans, it has been widely hypothesized that intra-abdominal pressure can reduce potential injurious compressive force imposed on spinal discs when a person lifts heavy objects. Inspired by this human biomechanical phenomenon, we design a novel loading-carrying strategy using hydraulic cylinders, valves and accumulators. Different from ordinary powered hydraulic systems, this design provides continuous support force to share the load applied on knee joint actuator without consuming extra energy. The bent-leg theoretical model is constructed to validate the design and analysis. A bipedal hydraulic-assisted electric leg prototype (HyELeg) is fabricated and tested for squatting and walking gaits. The results show that with hydraulic assistance, the prototype can save energy by 45.9% for squatting with a load of 125% of its own body weight, and the walking performance is enhanced by 16.9% in energy efficiency with carrying a load of 67.5% of its own body weight.

Estimation of the motion of an agent and its environment concurrently is done by simultaneous localization and mapping (SLAM). In the recent past, SLAM has made rapid and exciting progress and is used in different fields such as unmanned aerial vehicle (UAV), medical surgeries, and endoscopic procedures. The aim of this article is to devise a more accurate physiotherapy exercise monitoring device on the basis of analysis from eight different SLAM algorithms with criteria including power and memory consumption, CPU heat, and CPU utilization. This article provides a comprehensive evaluation on an embedded platform and is first of its kind, especially providing that SLAM systems ego-motion estimation has never been done so explicitly before. Based on the results of the prior analysis, we proposed a stereo visual-inertial tracking (S-VIT) for lower limb tracking in physiotherapy applications. Our proposed algorithm has significantly improved results compared with the state-of-the-art algorithms. Data sets of various physiotherapy rehabilitation exercises for leg are also collected for detailed validations where the ground truth is acquired with a state-of-the-art motion tracking system, Vicon.Note to Practitioners—Accurate real-time tracking in an unknown environment is a challenging task, especially if high accuracy is needed. In physiotherapy, analyzing the daily recorded data (data acquired from a patient’s body movement) will be beneficial in the process of rehabilitation. However, keeping the daily record of patient body motion during exercise is a difficult task in most of the circumstances, which is due to the nonavailability of precise portable devices for accurate motion tracking. In this article, a solution for tracking the patient’s motion (during physiotherapy) using a small hand-held device is presented. For this purpose, simultaneous localization and mapping (SLAM) is used, which, according to the best of our knowledge, is not used in the field of physiotherapy before. In the first part of this article, we present an extensive analysis of a few SLAM algorithms based on power, memory consumption, CPU heat, and CPU utilization. Based on these results, we select the best algorithm EMoVI-SLAM, and then, we extend the work of this article by modifying EMoVI-SLAM. We propose a new SLAM algorithm called stereo visual-inertial tracking (S-VIT). The proposed algorithm is compared with the EMoVI-SLAM on our data set. We collect the data set of various movements of the lower limb. The result shows that S-VIT outperforms EMoVI-SLAM.