Impedance-based control represents a prevalent strategy in the powered transfemoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for specific tasks, while creating a task-adaptive controller for variable-task walking continues to be a significant challenge. This article proposes a task-adaptive quasi-stiffness control framework for powered prostheses that generalizes across various walking tasks, enhancing the gait symmetry between the prosthesis and intact leg. A Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint’s angle and torque in a new task. Subsequently, a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of the new task from multiple human reference trajectories and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speeds and inclines walking tasks. Notably, the proposed framework not only aligns with but frequently surpasses the performance of a benchmark finite state machine impedance controller (FSMIC) without necessitating manual impedance tuning and has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.

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.

Accurate foot orientation and trajectory estimation are pivotal for advanced gait analysis, yet achieving this with inertial measurement units (IMUs) remains challenging due to their susceptibility to external acceleration, magnetic disturbances, and unbounded position errors. To address these limitations, we propose a multi-kernel correntropy smoother, which effectively mitigates unknown disturbances and enhances orientation accuracy. Furthermore, while the conventional zero-velocity update (ZUPT) method has been widely adopted in IMUs, the impact of incorporating position constraints has been largely overlooked. This paper demonstrates that by incorporating a single loop closure, the maximum positioning error can be reduced by up to 75%, with further reductions achievable through multiple position constraints. Comprehensive theoretical analysis and extensive experimental validation confirm the superior performance of the proposed methods.

This article focuses on the trajectory tracking problem of mobile manipulators (MMs). Firstly, we construct a position and orientation model predictive tracking control (POMPTC) scheme for mobile manipulators. The proposed POMPTC scheme can simultaneously minimize the tracking error, joint velocity, and joint acceleration. Moreover, it can achieve synchronous control for the position and orientation of the end-effector. Secondly, a finite-time convergent neural dynamics (FTCND) model is constructed to find the optimal solution of the POMPTC scheme. Then, based on the proposed POMPTC scheme, a non-singular fast terminal sliding model (NFTSM) control method is presented, which considers the disturbances caused by the floating-base on the manipulator at the dynamic level. It can achieve finite-time tracking performance and improve the anti-disturbances ability. Finally, simulation and experiments show that the proposed control method has the advantages of strong robustness, fast convergence, and high control accuracy.

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.

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this article proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

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.

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 objective of this paper is to develop a theory for designing the keyboard area of smartphones, and endowing the users with comfortable texting ability via one thumb. In light of the fact that most users tend to hold the smartphone in one hand and manipulate with one thumb in daily life, this paper extracts the thumb kinematic chain from the thumb anatomy of the human hand, and defines the manipulation comfort ellipsoid of the thumb. Then some indexes such as the morphology and volume of the comfort ellipsoid, and the condition number of the thumb Jacobian are presented to evaluate the manipulation comfort of the thumb. Finally, the design problem of the smartphone’s keyboard area is transformed into an optimization problem that determines the most comfortable manipulation space for the human thumb. The proposed quantitative study method of the thumb manipulation comfort is verified by 12 subjects who hold a smartphone with one hand and manipulate the screen with one thumb, and the most comfortable keyboard layout of smartphone is determined for the 12 subjects. The proposed method for the manipulation comfort of the human thumb bridges the gap between the layout planning of soft keyboard of smartphones and the comfort of single thumb manipulation.

Estimation of center of mass (CoM) and center of pressure (CoP) is critical for lower limb exoskeletons, prostheses, and legged robots. To meet the demand in these fields, this study presents a novel CoM and CoP estimation method for human walking through a wearable visual odometry (VO) device. This method is named VO-based estimation of CoM and CoP (VOECC). The methodology of VOECC is that the VO provides CoM trajectory estimation and the inherent walking dynamics model is exploited as prior knowledge for CoP trajectory estimation during human walking. Gait cycle is estimated based on the frequency analysis of the CoM trajectory, which is cropped into segments. Each segment mainly includes a half gait cycle. The segments are designed to be sliding to mitigate the disturbance of double-stance phase. For each segment, a quadratic programming (QP) problem is formulated to fit the CoM measurement with the theoretical walking dynamics model. The solution to this QP problem is an optimal gait parameters estimation, including CoP. Based on this solution, the human walking model with the CoM trajectory and CoP excursion is reconstructed. VOECC is evaluated experimentally where human walks on level ground and upstairs with VO device attached in front of the chest. The ground truth of CoM and CoP position is directly measured by the motion capture system and fully instrumented treadmill, respectively, and compared with the VOECC results. The proposed method is demonstrated to be effective in terms of wearable and extensible functionalities compared with the existing methods. Root-mean-squared errors between the CoP measured by fully instrumented treadmill and the CoP estimated by VOECC are evaluated and compared. This method has the potential to be extensible in lower limb rehabilitation, prosthetic, and legged locomotion fields.Note to Practitioners—This article addresses the problem of estimating center of mass (CoM) and center of pressure (CoP) trajectories using a minimum number of wearable sensors and reliable algorithms during human daily walking. Estimation of CoM and CoP trajectories is critical for lower limb exoskeletons, prostheses, and legged robots. In this study, a novel method named VO-based estimation of CoM and CoP (VOECC) is presented that utilizes a walking model as prior knowledge and integrates it with visual odometry data, which estimates the trajectory of the wearable visual device. Compared with the inertia measurement unit (IMU)-based method, VOECC only uses one wearable visual device and thus significantly reduces the cost and system complexity. In addition, the VOECC outperforms the motion capture system and force plate since it is not limited to space constraints and has the potential to be applicable for daily life locomotion tasks. VOECC is wearable and untethered and therefore can be directly amounted on lower limb exoskeletons, prostheses, and legged robots. In the experiments, motion capture system and force plates are used to measure CoM and CoP as ground truth to demonstrate the effectiveness of the proposed VOECC. Practical limitations include failure from fast turning and synchronization of multichannel sensors. These limitations will be addressed as our future research directions.

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.

Visual information is indispensable to human locomotion in complex environments. Although amputees can perceive the environmental information by eyes, they cannot transmit the neural signals to prostheses directly. To augment human-prosthesis interaction, this article introduces a subvision system that can perceive environments actively, assist to control the powered prosthesis predictively, and accordingly reconstruct a complete vision-locomotion loop for transfemoral amputees. By using deep learning, the subvision system can classify common static terrains (e.g., level ground, stairs, and ramps) and estimate corresponding motion intents of amputees with high accuracy (98%). After applying the subvision system to the locomotion control system, the powered prosthesis can help amputees to achieve nonrhythmic locomotion naturally, including switching between different locomotion modes and crossing the obstacle. The subvision system can also recognize dynamic objects, such as an unexpected obstacle approaching the amputee, and assist in generating an agile obstacle-avoidance reflex movement. The experimental results demonstrate that the subvision system can cooperate with the powered prosthesis to reconstruct a complete vision-locomotion loop, which enhances the environmental adaptability of the amputees.

Compliance is important for humanoid robots, especially a position-controlled one, to perform tasks in complicated environments where unexpected or sudden contacts will result in large impacts which may cause instability or destroy the hardware of robots. This paper presents a compliance control method based on viscoelastic model for humanoid robots to survive on these conditions. The viscoelastic model is used to obtain the relationship between the differential of contact force/torque and linear/angular position. Thus a state equation of this model can be established and a state feedback controller adjusting the position to adapt to the contact force/torque can be designed to realize the compliant movement. The proposed compliance control method based on viscoelastic model has been employed in ankle compliance for stable walking on indefinite uneven terrain and arm compliance for falling protection on BHR-6P, a position-controlled humanoid robot, which validates its effectiveness.

Biped walking is sensitive to large perturbation because of the limited foothold and unstable nature. This paper proposed a strategy of dynamic updating the footstep for humanoid robots to recover their balance from an unexpected perturbation with one step. The proposed strategy consists of a desired footstep calculator and a swing leg controller. The desired footstep calculator consists of three stages: perturbation detection, rapid adaption, and capturabililty inspection. In the perturbation detection stage, the acceleration and the jerk of the pelvis are used to detect whether the perturbation occurs. In the rapid adaption stage, the robot modifies the desired step location quickly according to the integration of the changed acceleration during the perturbation. In the capturability inspection stage, capture region is calculated to inspect whether the desired foot step is inside the capture region. The swing leg controller is to generate the joint trajectories and torques of the swing leg based on the desired footstep online. Simulations of walking of a 12-DOF humanoid robot show that the proposed method is effective in recovering its balance even when it is perturbed from different directions by an impulse of 20 [Ns].

This paper presents a novel reflex control system for passive biped walking on unknown slope varying terrains by extension of previous work in the fields of CPG. The algorithm takes advantage of the passive dynamics of walking, assisting only when necessary with an intermittent oscillator driving the hip joint. We analyze inherent reasons of falling for a biped system based on dynamic principles and assume that human walking relies more on instinct actions within the spinal cord rather than brain. Corresponding falling tendency function is proposed, based on which a reflex controller adjusting output of oscillator is designed. An auxiliary span angle controller is put forward to provide secondary actuations for compensation to improve landing performance. The proposed reflex system requires no prior knowledge of the terrain or tens of hundreds of experiments, which are necessary for machine learning methods. Results of simulations indicate that our reflex controllers are capable of ensuring stable passive blind walking on slope varying terrains without ankle torque.

This paper addresses parametric walking patterns and optimum design issues for an underactuated biped robot. The walking pattern is curved by impact postures and middle postures. Impact postures are regulated by two parameters and middle postures are selected to adapt the swing foot to negotiate obstacles. To evaluate constraint conditions, stability margins, and walking performances, some indices are defined, and the correlations between these indices and the two parameters are illustrated by the corresponding atlases. The optimum design method which considers multi-criteria is carried out by using these atlases. This method provides not only one optimum result, but also an optimum region which contains all feasible optimum results. Using these atlases presented in this paper one can obtain the optimum result with respect to any object(s)