3D gaussian splatting has advanced simultaneous localization and mapping (SLAM) technology by enabling realtime positioning and the construction of high-fidelity maps. However, the uncertainty in gaussian position and initialization parameters introduces challenges, often requiring extensive iterative convergence and resulting in redundant or insufficient gaussian representations. To address this, we introduce a novel adaptive densification method based on Fourier frequency domain analysis to establish gaussian priors for rapid convergence. Additionally, we propose constructing independent and unified sparse and dense maps, where a sparse map supports efficient tracking via Generalized Iterative Closest Point (GICP) and a dense map creates high-fidelity visual representations. This is the first SLAM system leveraging frequency domain analysis to achieve high-quality gaussian mapping in realtime. Experimental results demonstrate an average frame rate of 36 FPS on Replica and TUM RGB-D datasets, achieving competitive accuracy in both localization and mapping. The source code is publicly available at https://github.com/3DV-Coder/FGS-SLAM.

For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71% and knee angle prediction error being 6.78%. These findings demonstrate the method’s ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.

Wearable devices are currently being used to reduce metabolism and assist people with disabilities for daily walking. For the elderly and disabled people, improving the walking stability of wearable devices is a crucial and unsolved research. In particular, the center of mass (CoM) trajectory can reflect the walking state as well as the stability of a person. For this reason, realizing the prediction of CoM trajectory under daily walking is promising to improve the wearable devices. In this article, a method for estimation and prediction of CoM during daily walking was proposed. A visual-inertial-odometry algorithm was used to obtain history CoM trajectories during walking, and the depth information from camera data was extracted by sequential distance embedding method and encode the information into terrain vectors. The trajectory vectors were patched with the corresponding terrain vectors and then realized the fusion of multi-modal data, which were then fed into a well-trained temporal convolution network to output the prediction results. With data from two outdoor datasets, the method in this paper was verified to be able to be used for CoM trajectory prediction for a variety of walking tasks and consume low computational cost. This method has the potential to be extensible in improving the stability of wearable devices, such as exoskeleton, powered prosthetic, and so on.

Existing research suggests that lower limb exoskeleton robots, when used for rehabilitation training based on the pre-stroke gait trajectories of stroke patients, may be more beneficial for gait rehabilitation. However, it’s challenging to obtain such personalized trajectories for specific patients. Therefore, this hypothesis is difficult to be verified. This paper introduces an Individualized Gait Trajectory Generation (IGTG) method based on Fast Fourier Transform (FFT) to approximate and regress pre-stroke gaits, along with conducting clinical rehabilitation validation trials. Initially, human gait trajectories are described using Fourier coefficients to construct gait features. Subsequently, a probabilistic mapping between these gait features and physical body parameters is established. Then, personalized gait trajectories are obtained by applying the inverse Fourier transform to the predicted gait features. The application of fast Fourier transform can reduce the number of the regression data points needed, decrease dependency on large datasets, and enhance the systematic robustness. This algorithm is trained using body parameters and gait trajectories collected from 128 healthy subjects. The algorithm is further applied to generate specific personalized trajectories for the 9 stroke patients. Clinical trial results indicate that rehabilitation training using these individualized gait trajectories reduces blood oxygen saturation (SpO2) and heart rate (HR) by up to 66.67% and 69.23% respectively compared to training with fixed trajectories. Note to Practitioners—The main purpose of this paper is to solve gait trajectories mismatch problem when different stroke patients use lower limb exoskeleton robot for rehabilitation training. Variations in body factors among individuals lead to different gait trajectories including walking speed, gender, age, and other anthropometric parameters. Therefore, this paper introduces a novel Individualized Gait Trajectory Generation (IGTG) method to generate suitable gait trajectories for stroke patients with different body characteristic parameters when taking gait rehabilitation training with a lower limb exoskeleton robot. The detailed methodology introduction and a full analysis of experimental results are also given. Finally, clinical experiments involving stroke patients were conducted to demonstrate the feasibility and effectiveness of the presented method.

Environment awareness is crucial for enhancing walking safety and stability of amputee wearing powered prosthesis when crossing uneven terrains such as stairs and obstacles. However, existing environmental perception systems for prosthesis only provide terrain types and corresponding parameters, which fail to prevent potential collisions when crossing uneven terrains and may lead to falls and other severe consequences. In this paper, a visual-inertial motion estimation approach is proposed for prosthesis to perceive its movement and the changes of spatial relationship between the prosthesis and uneven terrain when traversing them. To achieve this, we estimate the knee motion by utilizing a depth camera to perceive the environment and align feature points extracted from uneven terrains. Subsequently, an error-state Kalman filter is incorporated to fuse the inertial data into visual estimations to obtain a more robust and accurate estimation, which is then utilized to derive the motion of the whole prosthesis for our prosthetic control scheme. Experiments conducted on our collected dataset and stair walking trials with powered prosthesis show that the proposed method can accurately track the motion of human leg and the prosthesis with the average root-mean-square error of toe trajectory less than 5 cm. The proposed method is expected to enable the environmental adaptive control for prosthesis, thereby enhancing amputee’s safety and mobility in uneven terrains.

Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N$\pm$15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than$\pm$1 mm and 30 mm$\pm$10%, respectively.Note to Practitioners—The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.

Suspended backpacks have been acknowledged for their advantages in load carriage, leading to the development of various designs aimed at enhancing their performance. However, current suspended backpacks typically possess fixed stiffness or limited adjustability, thereby limiting their adaptability to different load carriage tasks, such as varying walking speeds and load masses. This article introduced a suspended backpack design capable of modulating its stiffness over a wide range while maintaining a lightweight profile. The variable stiffness suspension (VSS) was integrated into the load frame of the suspended backpack and utilized a motor to adjust the stiffness by generating spring-like force based on the relative displacement between the load and the body. Experimental validation was conducted to assess the stiffness modulation of the suspended backpack. The VSS enabled the stiffness modulation of the suspended backpack ranging from 424 to 2182 N/m, which corresponded to the desired stiffness range for a 10–25 kg load at walking speeds for 3.5–6 km/h. Moreover, the mechanics of the carriers were analyzed to evaluate the impact of the suspended backpack on the individuals. Results showed that the designed VSS suspended backpack could reduce peak push-off force by 20.71% under the high working condition and energetic cost by 30.39% under the midworking condition. However, a tradeoff exists between minimizing the peak accelerative load force and energetic cost. The proposed design holds the potential for enhancing performance across various load carriage tasks, including human-in-the-loop energetic optimization.

The quality of breast ultrasound imaging is greatly affected by the contact force of the probe, which largely requires experienced sonographers to complete the clinical examination. We propose a flexible and fully autonomous ultrasound scanning system for breast ultrasound imaging. It consists of an ultrasound machine, a dual robotic arms system, a multi-structured light system, a human–computer interaction system, and a flexible ultrasound probe clamping device (FUPCD). First, the dynamics model of the FUPCD was analyzed, and a closed-loop force control strategy was established. We then implemented an automatic scanning system. The hysteresis characteristics of the FUPCD and transient response of the force controller were experimentally verified. The system could keep the steady-state error less than ± 5% within 0.5 s. Second, the performance of the control system to maintain constant contact force at different scanning speeds (Note to Practitioners—The motivation of this study is to solve the problem of poor image reproducibility in breast ultrasound scanning, but the proposed system can also be applied to ultrasound scanning of other parts of the body. The position, direction, and contact force of the ultrasound probe affects the image quality and repeatability of the ultrasound, thereby affecting the diagnostic ability. Therefore, we propose and develop a flexible and fully autonomous breast ultrasound scanning system. We aimed to improve the autonomy and stability of the scanning process by designing end-to-end automated scanning strategies, flexible clamping devices, and closed-loop force control strategies. Among them, the fully automatic three-dimensional perception and trajectory planning can provide global fitting capabilities to different forms of breast surfaces and control the probe to maintain the best contact posture. At the same time, the fully automatic workflow reduces additional interference and reduces the complexity of the workflow. The flexible ultrasound probe clamping device improves the passive applicability to flexible tissues and reduces the resistance in the scanning process. The closed-loop force control strategy can adjust the contact force in real time. Experiments verified that the repeatability could be evaluated by contact force. A series of human–machine comparison experiments verified the advancement and effectiveness of the system. In future research, we will further solve the problem of abnormalities and repeatability of ultrasound images acquired during the scanning process by combining the multi-modal feedback of images and forces.

The visual analysis of the mouth cavity plays a significant role in the pathogen specimen sampling and disease diagnosis of the mouth cavity. Aiming at performance defects of general detectors based on deep learning in detecting mouth cavity components, this article proposes a mouth cavity analysis network (MCNet), which is an instance segmentation method with spatial features, and a mouth cavity dataset (MCData), which is the first available dataset for mouth cavity detecting and segmentation. First, given the lack of a mouth cavity image dataset, the MCData for detecting and segmenting key parts in the mouth cavity was developed for model training and testing. Second, the MCNet was designed based on the mask region-based convolutional neural network. To improve the performance of feature extraction, a parallel multiattention module was designed. Besides, to solve low detection accuracy of small-sized objects, a multiscale region proposal network structure was designed. Then, the mouth cavity spatial structure features were introduced, and the detection confidence could be refined to increase the detection accuracy. The MCNet achieved 81.5% detection accuracy and 78.1% segmentation accuracy (intersection over union = 0.50:0.95) on the MCData. Comparative experiments with the MCData showed that the proposed MCNet outperformed state-of-the-art approaches with the task of mouth cavity instance segmentation. In addition, the MCNet has been used in an oropharyngeal swab robot for COVID-19 oropharyngeal sampling.

Walking with load is a common task in daily life and disaster rescue. Long-term load carriage may cause irreversible damage to the human body. Although remarkable progress has been made in the field of wearable robots, it is still far from avoiding interference to human legs, which will lead to energy consumption. In this paper, a novel wearable robot, Centaur, for assisting load carriage has been proposed. The Centaur system consists of two rigid robotic legs of two degrees-of-freedom (DOFs) to transfer load weight to the ground. Different from exoskeletons, the robotic legs of the Centaur are placed behind the human rather than attached to human limbs, which can provide a larger support polygon and avoid additional interference to the wearer. Additionally, the Centaur can attain the locomotion stability of the quadruped while maintaining the motion agility of the biped itself. This paper also presents an interactive motion control strategy based on the human-robot interaction force. This control strategy incorporates legged robotics walking controller and real-time walking trajectory planning to realize the cooperative walking with human beings. Finally, experiments of human walking with load carriage have been conducted on flat terrain to verify the concept of the Centaur system. The result demonstrates that the Centaur system can effectively reduce 70.03% of load weight during the single stance phase, which indicates that the Centaur system provides a new solution for assisting human walking with load-carriage.

The mechanical work performed by the individual legs affects the metabolic cost of locomotion. The effects of an elastically suspended backpack (ESB) on the mechanical work performed by the individual legs have not yet been quantified. This article explores the impact of variables, such as the stiffness and damper of an ESB, walking speed, and load mass, on the leg mechanical work (LMW). A model integrating an improved bipedal walking submodel and a spring−mass−damper submodel is proposed to estimate the mechanical work performed by the individual legs (LMW model). Experimental data were collected to estimate the accuracy of the proposed model. Seven subjects walked with a loaded ESB prototype at speeds ranging from 3.6 to 6.0 km/h with the suspension engaged and with the suspension locked out. The measured mechanical work performed by the individual legs was compared to the LMW model estimates. The proposed model estimates corresponded well with the empirical results (averageR2= 0.909; estimated average error 3.6%). The LMW model was then used to simulate the effects of variables. The ESB produces positive or negative effects under different variables. With increasing ESB stiffness, the ESB first produces positive effects, then negative effects, and finally approaches the rigid backpack effect. The ESB damper also affects the magnitude of the effect. The smaller the damping, the larger the effect. These results could assist engineers trying to design ESB to minimize the mechanical work performed by the legs which may also minimize the metabolic energy cost.

An improved unit module of a novel mobile self-assembling modular robotic system is presented in this paper. Two continuous rotational DoFs are used in each module to implement both valuable self-locomotion and flexible reconfiguration. To achieve efficient exploration, unit module can implement two-dimensional locomotion independently and freely in a range of surface conditions in real world, even in environments with certain terrain challenges. With the help of three active connection mechanisms (ACMs), the module has great potential in assembling and reconfiguration to form complex three-dimensional structures. Preliminary locomotion tests in different environments demonstrate its effective mobility and potential applications for exploration. Several useful and easy realized configurations are explained with simulations at last.