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Care robots have been developed to address the shortage of human caregivers in aging societies facing declining birth rates. However, currently care robots only passively perform care tasks based on commands, they are unable to provide care services to bedridden patients with communication disorder in individual homes. This paper first proposes the concept of “proactive care”, which expects robots to act as a human caregiver even in a complex and changing environment according to the real-time state of the care recipient, such as proactively determine and complete the optimal care program without any commands. Then, a proactive care architecture (PCA) is proposed, which in turn gives a proactive care model (PCM) that can proactively generate the desires of care recipients. Finally, the feasibility of PCA and the validity of PCM were verified through simulation. PCA opens up an avenue for robots to care for bedridden patients with communication disorder, which will contribute to alleviating the social problem of human caregivers shortage.

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Self‐assembled monolayers (SAMs) employed in inverted perovskite solar cells (PSCs) have achieved groundbreaking progress in device efficiency and stability for both single‐junction and tandem configurations, owing to their distinctive and versatile ability to manipulate chemical and physical interface properties. In this regard, we present a comprehensive review of recent research advancements concerning SAMs in inverted perovskite single‐junction and tandem solar cells, where the prevailing challenges and future development prospects in the applications of SAMs are emphasized. We thoroughly examine the mechanistic roles of diverse SAMs in energy‐level regulation, interface modification, defect passivation, and charge transportation. This is achieved by understanding how interfacial molecular interactions can be finely tuned to mitigate charge recombination losses in inverted PSCs. Through this comprehensive review, we aim to provide valuable insights and references for further investigation and utilization of SAMs in inverted perovskite single‐junction and tandem solar cells. The self‐assembled monolayer plays a pivotal role in inverted single‐junction and tandem perovskite solar cells due to its distinctive and versatile ability to manipulate chemical and physical interface properties, serving as a key factor in charge transport, interface modification, energy‐level modulation, and defect passivation.

The excretion care robot’s (ECR) accurate recognition of transfer-assisted actions is crucial during its usage. However, transfer action recognition is a challenging task, especially since the differentiation of actions seriously affects its recognition speed, robustness, and generalization ability. We propose a novel approach for transfer action recognition assisted by a bidirectional long- and short-term memory (Bi-LSTM) network combined with a multi-head attention mechanism. Firstly, we utilize posture sensors to detect human movements and establish a lightweight three-dimensional (3D) model of the lower limbs. In particular, we adopt a discrete extended Kalman filter (DEKF) to improve the accuracy and foresight of pose solving. Then, we construct an action prediction model that incorporates a fused Bi-LSTM with Multi-head attention (MHA Bi-LSTM). The MHA extracts key information related to differentiated movements from different dimensions and assigns varying weights. Utilizing the Bi-LSTM network effectively combines past and future information to enhance the prediction results of differentiated actions. Finally, comparisons were made by three subjects in the proposed method and with two other time series based neural network models. The reliability of the MHA Bi-LSTM method was verified. These experimental results show that the introduced MHA Bi-LSTM model has a higher accuracy in predicting posture sensor-based excretory care actions. Our method provides a promising approach for handling transfer-assisted action individual differentiation in excretion care tasks.

The nursing robot, equipped with a 6-degree-of-freedom (6-DOF) humanoid manipulator, has been applied in elderly and disabled care to execute complex and random nursing tasks with its advantages in automation and intelligence. Especially, when the nursing robot performs daily care tasks such as serving tea and pouring water, the good trajectory tracking performance of its manipulator is a crucial capability. However, nonlinear coupling, model uncertainty, joint friction, unknown external disturbances, and particularly the fact that manipulator does not satisfy Pieper criterion-are the main challenges, which degrade control performance. Few existing studies have simultaneously addressed all these issues to improve the control accuracy of the manipulator. Therefore, to achieve the good tracking performance for manipulator, a robust control method combining sliding mode control (SMC), radial basis function neural network (RBFNN), and nonlinear disturbance observer (NDO) is proposed. An improved Jacobian-based gradient descent method solves inverse kinematics, with the improved gradient descent driven inverse kinematics (IGDIK) module ensuring accuracy; RBFNN compensates for model uncertainty; NDO handles disturbances and friction. Simulations and experiments demonstrate enhanced trajectory tracking accuracy and stability, validating its effectiveness for the target manipulator.

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.

Physiotherapy robots offer a feasible and promising solution for achieving safe and efficient treatment. Among these, acupoint recognition is the core component that ensures the precision of physiotherapy robots. Although the research on the acupoint recognition such as hand and ear has been extensive, the accurate location of acupoints on the back of the human body still faces great challenges due to the lack of significant external features. This paper designs a two-stage acupoint recognition method, which is achieved through the cooperation of two detection networks. First, a lightweight RTMDet network is used to extract the effective back range from the image, and then the acupoint coordinates are inferred from the extracted back range, reducing the inference consumption caused by invalid information. In addition, the RTMPose network based on the SimCC framework converts the acupoint coordinate regression problem into a classification problem of sub-pixel block subregions on the X and Y axes by performing sub-pixel-level segmentation of images, significantly improving detection speed and accuracy. Meanwhile, the multi-layer feature fusion of CSPNeXt enhances feature extraction capabilities. Then, we designed a physiotherapy interaction interface. Through the three-dimensional coordinates of the acupoints, we independently planned the physiotherapy task path of the physiotherapy robot. We conducted performance tests on the acupoint recognition system and physiotherapy task planning in the physiotherapy robot system. The experiments have proven our effectiveness, achieving a recall of 90.17% on human datasets, with a detection error of around 5.78 mm. At the same time, it can accurately identify different back postures and achieve an inference speed of 30 FPS on a 4070Ti GPU. Finally, we conducted continuous physiotherapy tasks on multiple acupoints for the user. The experimental results demonstrate the significant advantages and broad application potential of this method in improving the accuracy and reliability of autonomous acupoint recognition by physiotherapy robots.

Seasonal flu is currently a major public health issue the world is facing. Although the World Health Organization (WHO) suggests social distancing is one of the best ways to stop the spread of the flu disease, the lack of controllability in keeping a social distance is widespread. Spurred by this concern, this paper developed a fast social distancing monitoring solution, which combines a lightweight PyTorch-based monocular vision detection model with inverse perspective mapping (IPM) technology, enabling the nursing robot to recover 3D indoor information from a monocular image and detect the distance between pedestrians, then conducts a live and dynamic infection risk assessment by statistically analyzing the distance between the people within a scene and ranking public places into different risk levels, called Fast DeepSOCIAL (FDS). First, the FDS model generates the probability of an object’s category and location directly using a lightweight PyTorch-based one-stage detector, which enables a nursing robot to obtain significant real-time performance gains while reducing memory consumption. Additionally, the FDS model utilizes an improved spatial pyramid pooling strategy, which introduces more branches and parallel pooling with different kernel sizes, which will be beneficial in capturing the contextual information at multiple scales and thus improving detection accuracy. Finally, the nursing robot introduces a gap-seeking strategy based on obstacles-weighted control (GSOWC) to adapt to dangerous indoor disinfection tasks while quickly avoiding obstacles in an unknown and cluttered environment. The performance of the FDS on the nursing robot platform is verified through extensive evaluation, demonstrating its superior performance compared to seven state-of-the-art methods and revealing that the FDS model can better detect social distance. Overall, a nursing robot employing the Fast DeepSOCIAL model (FDS) will be an innovative approach that effectively contributes to dealing with this seasonal flu disaster due to its fast, contactless, and inexpensive features.

Lead‐free chalcogenide SnTe has been demonstrated to be an efficient medium temperature thermoelectric (TE) material. However, high intrinsic Sn vacancies as well as high thermal conductivity devalue its performance. Here, β‐Zn4Sb3 is incorporated into the SnTe matrix to regulate the thermoelectric performance of SnTe. Sequential in situ reactions take place between the β‐Zn4Sb3 additive and SnTe matrix, and an interesting “core–shell” microstructure (Sb@ZnTe) is obtained; the composition of SnTe matrix is also tuned and thus Sn vacancies are compensated effectively. Benefitting from the synergistic effect of the in situ reactions, an ultralow κlat ≈0.48 W m−1 K−1 at 873 K is obtained and the carrier concentrations and electrical properties are also improved successfully. Finally, a maximum ZT ≈1.32, which increases by ≈220% over the pristine SnTe, is achieved in the SnTe‐1.5% β‐Zn4Sb3 sample at 873 K. This work provides a new strategy to regulate the TE performance of SnTe and also offers a new insight to other related thermoelectric materials. In situ chemical reactions occur between the β‐Zn4Sb3 additive and SnTe matrix, and the resultant interesting “core–shell” structure is obtained in this work. The composition, microstructure, and transport properties of SnTe thermoelectric materials are synergistically tuned, so an ultralow lattice thermal conductivity (≈0.48 Wm−1 K−1 at 873 K) and relatively high ZT (≈1.32 at 873 K) are achieved, which present an effective method to enhance the thermoelectric performance of SnTe, and are also of referential values for other thermoelectric materials.

In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO2, SO2, NOx, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO2, SO2, NOx, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.