Deformations in Cement Pastes during Capillary Imbibition and Their Relation to Water and Isopropanol as Imbibing Liquids

The traditional approach for evaluating capillary imbibition, which describes the phenomena as a linear relationship between mass gain and the square root of time, considers a rigid pore structure. The common deviation from the linearity when using the square-root law (manifested in a downward curvature, i.e., slower water ingress) can be explained by considering a changing pore structure during the process caused by the swelling of calcium silicate hydrate (C-S-H) during water ingress. Analysing how the combination of deforming phase (C-S-H), non-deforming phase, and porosity affects the capillary water ingress rate is relevant for a deeper understanding of concrete durability. In this research, the C-S-H content was quantified by means of XRD diffraction coupled with Rietveld + PONKCS, dynamic water sorption (DVS), and SEM/BSE images coupled with phase mapping using PhAse Recognition and Characterization (PARC) software. The porosity was assessed by mercury intrusion porosimetry, water absorption under vacuum, and DVS. Furthermore, to assess deformations occurring with water and a non-aqueous imbibant, capillary imbibition tests with water and isopropanol as invading liquids were performed along with simultaneous deformation measurements. The relation between the relative C-S-H content and porosity has a great impact on the transport process. Samples exposed to isopropanol presented a much larger liquid uptake but significantly fewer deformations in comparison to imbibition with water. The effects of the changing pore structure were also evaluated with the Thomas and Jennings model, from which calculations indicated that pore shrink during imbibition. A comprehensive description of the relation between deformations and capillary imbibition in cement pastes reveals that liquid ingress is highly influenced by deformations.

Gait Phase Recognition Using Fuzzy Logic Regulation with Multisensor Data Fusion

The health challenges brought by aging population and chronic noncommunicable diseases are increasingly severe. Scientific physical exercise is of great significance to prevent the occurrence of chronic diseases and subhealth intervention and promote health. However, improper or excessive exercise can cause injury. Research shows that the sports injury rate of people who often exercise is as high as 85%. Aiming at the problem of low accuracy of single sensor gait analysis, a real-time gait detection algorithm based on piezoelectric film and motion sensor is proposed. On this basis, a gait phase recognition method based on fuzzy logic is proposed, which enhances the ability of gait space-time measurement. Experimental results show that the proposed gait modeling method based on ground reaction force (GRF) signal can effectively recognize and quantify various gait patterns. At the same time, the introduction of heterogeneous sensor data fusion technology can effectively make up for the accuracy defects of single sensor measurement and improve the estimation accuracy of gait space-time measurement.

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.