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An oleyl alcohol-based extended surfactant, sodium oleyl polyethylene oxide-polypropylene oxide sulfate (OE3P3S), was synthesized and identified using FT-IR and 1H NMR. The adsorption and aggregation behaviors of OE3P3S and its mixture with cationic surfactant alkyltrimethylammoniumbromide (ATAB) were investigated under different molar ratios. The static surface tension analysis indicated that the critical micellization concentration (cmc) and the critical surface tension (γcmc) of OE3P3S were 0.72 mmol/L, and 36.16 mN/m, respectively. The cmc and γcmc values of the binary system were much lower than that of the individual component. And the cmc values of OE3P3S/ATAB = 6:4 mixtures decreased with an increase in the chain length of the cationic surfactant in the binary system. It was found from the dynamic surface tension that there was a slower diffusion rate in the binary system compared to the pure surfactant, and the adsorption processes for OE3P3S/ATAB = 6:4 were mixed diffusion-kinetic adsorption mechanisms. With a combination of DLS data and TEM measurements, formations of vesicles in OE3P3S/ATAB = 6:4 solutions appeared to occur at a concentration of 0.05 mmol/L. By studying the formation of liquid crystal structures in an emulsion prepared with OE3P3S as the surfactant, it was found that the oil-in-water emulsion is birefringent with a Maltese cross texture, and the rheological properties revealed its predominant viscoelastic behavior and shear thinning properties.

Reasoning is a cognitive activity that leverages knowledge to generate solutions to problems. Knowledge representations in the brain require both symbolic and graphical information since visual information is figurative and conveys a large amount of information. Consequently, graphical knowledge representation is often employed in reasoning. Distance-type fuzzy inference utilizes the distance information between the antecedent and the set of facts as the basis for inference. Compared to Mamdani inference, the distance-type fuzzy inference method not only satisfies the convexity and asymptotic properties of the inference results but also adheres to the separation rule (modus ponens), a fundamental principle in inference. This paper discusses extensions of distance-type fuzzy inference methods to handle spatial figures. In this paper, we first explain the distance-type fuzzy inference method. Then, we discuss the concept representation in the feature space and independent parameters that can completely express the characteristics of a figure in space, which are defined as “characteristic parameters”. Furthermore, we describe the correspondence between figures and vectors in the feature space, propose a new distance-type fuzzy inference method based on characteristic parameters and describe its characteristics. Finally, an example is used to demonstrate the inference results of this new distance-type fuzzy inference method.

Abstract The authors propose a multifunctional intelligent bed (MIB) that integrates multiple modes of interaction to improve the welfare of mobility‐impaired users and reduce the workload of medical personnel. The MIB features independent autonomous omnidirectional movement, position adjustment, multi‐degree‐of‐freedom (DOF) movement regulation and posture memory functions to facilitate comfortable and convenient interaction for mobility‐impaired users. In particular, an integrated “MIB‐state perception‐interaction interfaces” system is established, and a bed fall risk detection algorithm and assisted get‐up‐transfer algorithm is proposed. By recognising and sharing human body state characteristics, nursing collaboration can be achieved with caregivers or other nursing robots. Comprehensive experiments demonstrate that the MIB is a novel MIB that is highly adaptable to the environment, convenient to interact with and safe. By integrating the proposed algorithms, daily safety monitoring, assisted get‐up and defecation tasks can be effectively accomplished. This technology demonstrates excellent applicability and promising prospects for implementation in hospitals, nursing centres and homes catering to elderly and disabled individuals with mobility impairments.

At present, autonomous mobile robots are widely used in industrial and commercial fields. However, although the global path searched by existing mobile robot path planning methods has no collision with obstacles, there is a problem in that the path is close to obstacles and is not smooth, and there is a collision safety risk when the robot is actually moving. To solve the above problems, this paper proposes a global path planning method based on minimizing the risk index. Firstly, the distance calculation method of the heuristic function of the traditional graph search algorithm is improved to reduce the number of nodes in the search space. Additionally, by selecting the appropriate search neighborhood, the search efficiency and path smoothness of the algorithm are improved. Thirdly, to increase the distance between the original search path and obstacles, the risk index path search strategy is proposed. Finally, the minimized snap trajectory smoothing method with a safe corridor is used to smooth the original waypoint. Both simulation and real robot experimental results show that the minimum distance between waypoints and obstacles is increased by 43.72% on average, and the number of trajectory inflection points are reduced by 75.12% on average after optimization. As such, the proposed method can fully guarantee safety and generate smooth mobile robot paths in global trajectory planning tasks.

The rehabilitation evaluation of Parkinson’s disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson’s disease patients, thereby adjusting the actuators of the human–machine system and making rehabilitation robots better adapt to the recovery process of patients. The rehabilitation evaluation of Parkinson’s disease has always been the research focus of rehabilitation robots. It is a research hotspot to be able to objectively and accurately evaluate the recovery of Parkinson’s disease patients, thereby adjusting the driving module of the human–machine collaboration system in real time, so that rehabilitation robots can better adapt to the recovery process of Parkinson’s disease. The gait task in the Unified Parkinson’s Disease Rating Scale (UPDRS) is a widely accepted standard for assessing the gait impairments of patients with Parkinson’s disease (PD). However, the assessments conducted by neurologists are always subjective and inaccurate, and the results are determined by the neurologists’ observation and clinical experience. Thus, in this study, we proposed a novel machine learning-based method of automatically assessing the gait task in UPDRS with wearable sensors as a more convenient and objective alternative means for PD gait assessment. In the design, twelve gait features, including three spatial–temporal features and nine kinematic features, were extracted and calculated from two shank-mounted IMUs. A novel nonlinear model is developed for calculating the score of gait task from the gait features. Twenty-five PD patients and twenty-eight healthy subjects were recruited for validating the proposed method. For comparison purpose, three traditional models, which have been used in previous studies, were also tested by the same dataset. In terms of percentages of participants, 84.9%, 73.6%, 73.6%, and 66.0% of the participants were accurately assigned into the true level with the proposed nonlinear model, the support vector machine model, the naive Bayes model, and the linear regression model, respectively, which indicates that the proposed method has a good performance on calculating the score of the UPDRS gait task and conformance with the rating done by neurologists.

In this study we propose a “hand gesture + face expression” human machine interaction technique, and apply this technique to bedridden rehabilitation robot. “Hand gesture + Facial expression” interactive technology combines the input mode of gesture and facial expression perception. It involves seven basic facial expressions that can be used to determine a target selecting task, while hand gestures are used to control a cursor’s location. A controlled experiment was designed and conducted to evaluate the effectiveness of the proposed hybrid technology. A series of target selecting tasks with different target widths and layouts were designed to examine the recognition accuracy of hybrid control gestures. An interactive experiment applied to a rehabilitation robot is designed to verify the feasibility of this interactive technology applied to rehabilitation robots. The experimental results show that the “hand + facial expression” interactive gesture has strong robustness, which can provide a novel guideline for designing applications in VR interfaces, and it can be applied to the rehabilitation robots.

The excretion behavior in daily life for the elderly and the disabled is a high frequency, high physical load, and risky behavior. Therefore, we proposed an auxiliary lavatory robot (ALR) with autonomous movement and self-cleaning capability. When the nursing staff assists a user in transferring from a standing or lying state to sitting on the ALR, the ALR can follow the user according to their position and posture. Over the whole transfer process, the ALR always provides the user with the best transfer position and posture, which is an effective approach to reduce workload and physical load. However, confusion and occlusion of the lower limbs between the nursing staff and the user would affect the user’s posture recognition. First, in this paper, a method combined with object segmentation and shape constraint was proposed to extract the contour of the lower limbs of the user and the nursing staff. Then, depending on the position constraint and dynamic characteristics of the legs contour and back contour of the user, a dynamic posture recognition approach based on a two-level joint probabilistic data association algorithm (JPDA) was proposed. Finally, the leg target recognition experiment, path-tracking experiment, and auxiliary excretion transfer experiment were implemented to verify the effectiveness and robustness of our proposed algorithm. The experimental results showed that our proposed method improved the safety and convenience of the user, and it also reduced the workload and physical load of the nursing staff. The ALR, integrated with the proposed method, has a good universal property for the elderly and disabled with weak motion capability in hospitals, pension centers, and families.

This work develops intelligent walking assist robot and gait rehabilitation robot for people with walking disabilities, where distinguishing of abnormal gait is crucial during safe human-robot interaction. Although vision sensors and wearable inertial devices are widely used in fall detection system, shielding of robot mechanical structure brings instability for visual detection method, and utilizing a large number of wearable sensors brings discomfort and complexity for users. Meanwhile, the frequent drag-to-drop gait has never been considered, which is a kind of intermittent state between normal state and fall. It's a major challenge to achieve an abnormal gait recognition system with high stability and good comfort. In this paper, a novel non-contact gait recognition method is proposed, which can identify fall and drag-to-drop gait in a stable and comfortable way. Leg movement data is obtained by a built-in-robot camera, which accurately describes gait law through contrast test and error analysis. New-type ESMF algorithm is proposed to evaluate knee angle precisely and resolve the issue of marker point loss effectively. The comprehensive experiments have shown that this innovative approach calculates accurately knee angle in the walking process so as to recognize the phenomenon of drag-to-drop gait and fall. Additionally, this method having a sound theoretical basis and higher stability solves the issue of marker point loss, which could be applied to a walker with a similar structure for elderly and disabled in a comfortable way.

In this paper, in order to reduce the burden of caring for individuals who are bedridden, we present a method of reasoning about the objects and actions that could satisfy their physiological needs. First, we introduce a method of representing knowledge about everyday objects in terms of their properties and functions. Based on this representation, we then propose a desire-driven reasoning approach that bridges the gap between physiological desires and robot actions. This can also deal with issues caused by uncontrolled domains, including incomplete knowledge and dynamic environments. Finally, we evaluate the proposed method by applying our newly developed KUT-PCR personal care robot to real household scenarios.

It is a considerable challenge to realize the accurate, continuous detection of handgrip strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation method oriented toward the multi-wrist angle based on the development of a flexible deformation sensor is proposed. The flexible deformation sensor consists of a foaming sponge, a Hall sensor, an LED, and photoresistors (PRs), which can measure the deformation of muscles with grip strength. When the external deformation squeezes the foaming sponge, its density and light intensity change, which is detected by a light-sensitive resistor. The light-sensitive resistor extended to the internal foaming sponge with illuminance complies with the extrusion of muscle deformation to enable relative muscle deformation measurement. Furthermore, to achieve the speed, accuracy, and continuous detection of grip strength with different wrist angles, a new grip strength-arm muscle model is adopted and a one-dimensional convolutional neural network based on the dynamic window is proposed to recognize wrist joints. Finally, all the experimental results demonstrate that our proposed flexible deformation sensor can accurately detect the muscle deformation of the arm, and the designed muscle model and convolutional neural network can continuously predict hand grip at different wrist angles in real-time.

In this study, we designed a dynamic output feedback control for a walking assistance training robot. The proposed system addresses the problems of shifts in the center of gravity and vibration associated with time-varying arm of force in omniwheel to improve the accuracy of the trajectory tracking of the walking assistance training robot. The dynamic output feedback controller was developed by constructing a velocity observer on a stochastic dynamic model of the walking assistance training robot via integration with a Lyapunov function. An analysis of the time-varying arm of force in omniwheel revealed that it increased the accuracy of the walking assistance training robot dynamic model. Thus, an adaptive law was designed such that the vibration caused by the time-varying arm of force in omniwheel was eliminated. The mean absolute practical stability in the position and velocity tracking errors was verified based on Young’s inequality and stochastic stability theory. The simulation results show that the walking assistance training robot with the shifts in the center of gravity was able to track a designed trajectory and that the application of the adaptive law effectively eliminates the vibrations caused by the time-varying arm of force in omniwheel.

With population aging, increasing numbers of people who are elderly are losing the ability to walk and therefore are experiencing difficulties caring for themselves. This prompted the designing of a sitting-type walking-assistance robot named the human support robot (HSR), which can move accurately in narrow indoor environments and act as a person's legs to assist her/him with activities of daily living. With the construction of a dynamic model of the HSR, a saturated control algorithm guarantees the safety of the omniwheels and the HSR supporting process. Analysis of the omniwheel touchdown characteristics improves the accuracy of the dynamic HSR model. Under trajectory tracking control with adaptive technology, the HSR tracking performance is improved, and the vibration in the orientation angle caused by the omniwheel touchdown characteristics is eliminated. The exponential practical stability of the tracking error system is derived, and the effectiveness of the proposed methods is confirmed in simulations.

This work develops an intelligent walk assist robot for people with walking disabilities, where the precise tracking of the user’s movement is crucial. The time-varying friction and load changes impose significant challenges on the tracking. Although the digital acceleration controller is effective to handle them, it is difficult and time consuming to manually tune the control parameters for optimal performance. This is why the automatic parameter optimization techniques are popular in the literature. Despite this, the existing parameter tuning algorithms suffer from sub-optimal performance and low computational efficiency. In this paper, an improved genetic algorithm (IGA) is proposed, which can quickly identify the optimal parameters. Our algorithm explores a few advanced genetic mechanisms including nonlinear ranking selection, arithmetic crossover operation method with competition and selection mechanisms among several crossover offspring, and adaptive change of mutation scaling. The simulation and experimental results have shown that this novel IGA algorithm can effectively reduce the tracking error from 18mm to 0.08mm and reduces the computational complexity.

This work develops an indoor carrier robot for people with disabilities, where the precise tracking of designated route is crucial. The parameter uncertainties and disturbances of the robot impose significant challenges for tracking. The present paper first investigates the dynamic of mechanical structure and modeling of actuator motors and constructs a new dynamic model by considering all main parameter uncertainties and disturbances. A novel robust feedback tracking controller considering both the optimization of path tracking and the minimization of the power consumption energy is proposed. It is proved that the tracking errors e and e ˙ satisfy a H∞ performance indicator while the energy consumption is minimum. A simulation example was performed and the results show that this novel algorithm can effectively reduce the tracking error from 0.2 m to 0.006 m while guaranteeing the minimum energy consumption. Furthermore, the effectiveness of the proposed method was validated by experiment compared with the non-robust one.