In the context of exoskeleton rehabilitation for patients in the later stages of recovery, reducing unnecessary interactive torque and enhancing exoskeleton transparency is important, where patients are encouraged to engage in voluntary movement. This study introduces a novel method known as Periodic Kernelized Movement Primitives (PerKMP) to address this issue. PerKMP is an advanced iteration of the original Kernelized Movement Primitives (KMP) that integrates periodic kernel functions for heightened adaptability. Two distinct PerKMP modules have been developed: the desired trajectory modulation PerKMP, which dynamically plans the desired trajectory according to the walking state, and the joint torque estimation PerKMP, which accurately estimates total joint torque in real-time and adjusts controller stiffness accordingly. A series of experiments with different walking speeds and different subjects were conducted to verify the validity of the method. Through the combined implementation of two PerKMP modules, the average absolute value of the interactive torque and energy per unit distance (EPUD) were reduced effectively. This research broadens the application of imitation learning methods in exoskeletons, and enables real-time control adjustments, thereby presenting a pioneering approach to enhancing the adaptability of exoskeleton rehabilitation systems.

With the rapid development of rehabilitation robotics, there is a pressing need for efficient and accurate gait prediction methods. However, due to the complexity and variability of individual gait characteristics and external disturbances, accurately predicting gait in real time remains a significant challenge. This paper proposes an innovative Bayesian-inference-based method for real-time gait prediction while a subject walks with a lower-limb exoskeleton. Periodic gait information is represented using von Mises basis functions, and the weight parameters serve as real-time updated state variables. The error-subspace transform Kalman filter (ESTKF) is applied for gait trajectory prediction. A fully connected neural network (FCNN) is used to estimate the walking speeds in real time based on predicted trajectories. Comparative experiments based on an open-source database prove the advantages of ESKTF compared with other Bayesian filters. Walking experiments are conducted to estimate phase and speed in real time, and to predict the joint angle, total joint torque, and lower-limb muscle surface electromyography (sEMG) values. Experimental results validate the method's prediction performance across different speeds and demonstrate its resilience to external interference.

Legged robots have demonstrated high efficiency and effectiveness in unstructured and dynamic environments. However, it is still challenging for legged robots to achieve rapid and efficient locomotion on deformable, yielding substrates, such as granular terrains. We present an enhanced resistive force model for bipedal walkers on soft granular terrains by introducing effective intrusion depth correction. The enhanced force model captures fundamental kinetic results considering the robot foot shape, walking gait speed variation, and energy expense. The model is validated by extensive foot intrusion experiments with a bipedal robot. The results confirm the model accuracy on the given type of granular terrains. The model can be further integrated with the motion control of bipedal robotic walkers.

The human foot arch plays a significant role in bearing weight, keeping balance, and walking efficiently. In this study, we present a pneumatic arch support insole (PASI) and a foot arch stiffness-based dynamic plantar support control to reduce the metabolic cost of walking. We first obtain the foot arch quasi-stiffness estimation over the gait phase by conducting multiple subject experiments. The design and modeling of the PASI and foot-insole interactions are then discussed. A model predictive control scheme is presented to dynamically regulate the foot arch stiffness over the gait phase by using the active PASI. We validate the system experimentally and conduct multi-subject walking tests. The results show that dynamic plantar support control of the foot arch stiffness reduces the metabolic cost by 7.99% compared to regular walking. In contrast, passive support without dynamic regulation increases the metabolic cost by 6.60%. The new pneumatic insoles and dynamic support method demonstrate promising potential for everyday and medical applications.

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are 3.3 ± 0.3° and 3.5 ± 0.7°, respectively. For the patients, the estimated error compared with Xsens-measured angle is 4.8 ± 1.7°. Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. Note to Practitioners—Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

Ankle moment plays an important role in human gait analysis, patients' rehabilitation process monitoring, and the human-machine interaction control of exoskeleton robots. However, current ankle moment estimation methods mainly rely on inverse dynamics (ID) based on optical motion capture system (OMC) and force plate. These methods rely on fixed instruments in the laboratory, which are difficult to be applied to the control of exoskeleton robots. To solve this problem, this paper developed a new distributed plantar pressure system and proposed an ankle plantar flexion moment estimation method using the plantar pressure system. We integrated eight pressure sensors in each insole to collect the pressure data of the key area of the foot and then used the plantar pressure data to train four neural networks to obtain the ankle moment. The performance of the models was evaluated using normalized root mean square error (NRMSE) and cross-correlation coefficient (ρ). During experiments, eight subjects were recruited for the overground walking tests, and OMC and force plate were used as the gold standard. The results indicate that the Genetic algorithm - Gated recurrent unit estimation algorithm (GA-GRU) was the best estimation model which achieved the highest accuracy in generalized ankle moment estimation (NRMSE = 7.23%, ρ = 0.85) compared with the other models. The designed novel distributed plantar pressure system and the proposed method could serve as a joint moment estimation approach in wearable robot control and human motion state monitoring.

Real-time lower limbs motion or gait measurement is an important part in human-robotic interaction for the control of robotic walkers and rehabilitation devices. Laser range finder or infrared sensor that is mounted on the device has been widely used in applications. Although these sensors can provide accurate horizontal motion information of lower limbs during human walking, it is still difficult to measure the angular motion of lower limbs due to their functional principles. Using inertial measurement units (IMU) can measure the angular motion of lower limbs, but it requires a large amount of IMU units for measurements of all lower limb segments. In this study, a novel method is developed for real-time monitoring lower limbs (shanks and thighs) motion in human walking using just two shank-mounted IMUs. A pose prediction model based on multiple linear regression and Kalman filter is proposed. The root-mean-square error (RMSE) of the thigh orientation and the knee joint angle estimation in sagittal plane are 6.1 ± 1.3 and 6.8 ± 1.4 degs, respectively. The RMSE of the ankle, knee, and hip position estimation are 4.2 ± 1.3, 4.2 ± 1.1 and 3.5 ± 0.9 cm, respectively.

Load-carrying capability is an essential criterion in legged robots' practical application. This paper proposes an unpowered hydraulic auxiliary system to improve the legged robot's loading capability and energy efficiency. For humans, it has been widely hypothesized that intra-abdominal pressure can reduce potential injurious compressive force imposed on spinal discs when a person lifts heavy objects. Inspired by this human biomechanical phenomenon, we design a novel loading-carrying strategy using hydraulic cylinders, valves and accumulators. Different from ordinary powered hydraulic systems, this design provides continuous support force to share the load applied on knee joint actuator without consuming extra energy. The bent-leg theoretical model is constructed to validate the design and analysis. A bipedal hydraulic-assisted electric leg prototype (HyELeg) is fabricated and tested for squatting and walking gaits. The results show that with hydraulic assistance, the prototype can save energy by 45.9% for squatting with a load of 125% of its own body weight, and the walking performance is enhanced by 16.9% in energy efficiency with carrying a load of 67.5% of its own body weight.

It is inevitable for human to bear the gravitational and inertial force when carrying loads. The impact force exerted on human body is originated from the inertial force which can increase the energy expenditure and cause injury to human body. This paper proposes a semi-active hover backpack with controllable air damper to minimize the inertial force. The skyhook damper model of hover backpack is established which is the dynamic target of the practical backpack. Sliding mode control is designed to eliminate the tracking error and the effectiveness of the control method is analyzed. Simulation and experiment are conducted and comparative results are stated. The results demonstrate that the semi-active hover backpack with sliding mode control can reduce the inertial force.

Inertial measurement units (IMU) have been used for gait analysis in many clinical studies, as a more convenient, low cost and less restricted alternative to the laboratory-based motion capture systems or instrumented walkways. Spatial-temporal gait parameters such as gait cycle duration and stride length calculated from the IMUs were often used in these studies for evaluating the impaired gait. However, the spatial-temporal information provided by IMUs is limited, and sometime suffers incomplete and less effective evaluation. In this study, we develop a novel IMU-based method for clinical gait evaluation. Nine gait variables including three spatial-temporal parameters and six kinematic parameters are extracted from two shank-mounted IMUs for quantifying patient's gait deviations. Based on those parameters, an IMU-based gait normalcy index (INI) is derived to evaluate the overall gait performance. Eight inpatient subjects with gait impairments caused by n-hexane neuropathy and ten healthy subjects were recruited. The proposed gait variables and INI were examined on the inpatients at three to five time instants during the rehabilitation process until being discharged. A comparison with healthy subjects and statistical analysis for the changes of gait variables and INI demonstrated that the proposed new set of gait variables and INI can provide adequate and effective information for quantifying gait abnormalities, and help understanding the progress of gait and effectiveness of therapy during rehabilitation process.

This paper presents the mechanism design and coordination control strategy of a lower limb rehabilitation robot in the bed, which may be helpful for improving the rehabilitation quality of patients with limb injuries when staying on the bad. It not only has a variety of rehabilitation training mode, but also has an adjustable structure and simple operation. It is more adaptable and friendly to patients in different circumstances. We conducted in-depth research on the configuration design, human kinematics analysis and man-machine coordination control of the horizontal lower limb rehabilitation robot. A prototype experimental platform developed to validate the effectiveness of the proposed control method. The result shows that the rehabilitation robot can meet the training needs of patients with different posture and injury degree in various stage of rehabilitation.

We present stability and control analysis of a rider- bicycle system under human steering and body movements. The dynamic model of rider-bicycle interactions is first constructed to integrate the rider's body movement with the moving bicycle platform. We then present human balance control strategies based on human riding experiments. The closed-loop system stability is analyzed and discussed. Quantitative influences of the bicycle physical parameters, the human control gains, and the time delays are also analyzed and discussed. Extensive experiments are conducted to validate the human control models and demonstrate human balance performance using the bikebot, an instrumented bicycle platform. The presented modeling and analysis results can be potentially used for further development of bicycle-assisted rehabilitation for postural balance patients.

Slip is one of the leading causes of fall-related injuries among occupational and elderly population. Existing research supports proactive slip and fall prevention approaches, while active strategies remain underdeveloped. Development of active slip-induced fall prevention systems requires fast, effective slip detection. This paper aims to develop a novel, real-time slip detection and estimation algorithm during human walking. The slip estimation is built on a slip dynamic model for biped walkers with the integration of the human locomotion constraints. The slip detection uses a set of wearable inertial sensors attached on the lower limbs. A slip indicator is introduced to detect the slip shortly after the heel-strike event. We present an extended Kalman filter-based slip estimation to characterize the slipping distance. One attractive property of the algorithm is its fast, accurate slip onset detection and slipping distance estimation with low-cost, nonintrusive sensing features. Experiments are conducted to validate and demonstrate the performance of the proposed slip detection and estimation scheme.

Passive acoustic localization is an important technique in a wide variety of monitoring applications, ranging from health care over biological survey to structural health monitoring of buildings. However, the method obtains the location of an unknown sound source with low-cost and simple still is lacking. In this paper, we implement a passive sound source location system based on commercial off-the-shelf (COTS) mobile devices, typically a smartphone, are organized as Wireless Sensor Networks (WSNs). We use the Precise Time Protocol (PTP) in WLAN to achieve the time synchronization of mobile devices with a coarse grain, and then uses moving variance and linear interpolation to get the Time of Arrival (TOA) of the sound source signal. We also design and implement a robust Sequence-Based localization algorithm based on Linear Programming, i.e. LPSBL, which transforms the TOA information of the sound source signal arriving at these devices to a nodes sequence and estimate the location of the sound source by the nodes sequence. After plenty of experiments were carried out, it is verified that our system can provide sufficiently reasonable positioning accuracy and good robustness in an indoor environment.

Vertical Ground Reaction Forces (VGRF) reference profiles of human gait are important in medicine, for the recognition of gait disorders and in the assessment of rehabilitation treatments. A walking human is a dynamic varying system and in spite of the VGRF reference dependence on several patient's variables, doctors traditionally use the same static reference for all patients. The purpose of this study is to find out if an Extreme Learning Machine (ELM) is adequate to generate the dynamic VGRF reference profiles of healthy people depending on the subject's age, weight, height and stride duration. The ELM is compared with two other baseline Computational Intelligence (CI) methods, the Backpropagation Neural Network (BNN) and Multioutput Support Vector Regression (MSVR). Data from 28 healthy males walking at five different stride durations, collected using instrumented shoes, were used to train and test the CI models. The results showed that ELM is a well suited method to generate the dynamic VGRF reference profile for both dominant and non-dominant limbs, showing the lowest root mean square errors for the test set, 0.0201 and 0.0243 (fraction of body weight) for the dominant and non-dominant limbs respectively. This study reveals a promising methodology that can be implemented in real time gait analysis, allowing doctors to find a specific reference gait pattern for the gait analysis of an unhealthy person by specifying the age, weight, height and stride duration.

Traditional exoskeletons have made considerable contributions to people in terms of providing wearable assistance and rehabilitation. However, exoskeletons still have some disadvantages, such as being heavy, bulky, stiff, noisy, and having a fixed center of rotation that can be a burden on elders and patients with weakened muscles. Conversely, artificial muscles based on soft, smart materials possess the attributes of being lightweight, compact, highly flexible, and have mute actuation, for which they are considered to be the most similar to natural muscles. Among these materials, dielectric elastomer (DE) and polyvinyl chloride (PVC) gel exhibit considerable actuation strain, high actuation stress, high response speed, and long life span, which give them great potential for application in wearable assistance and rehabilitation. Unfortunately, there is very little research on the application of these two materials in these fields. In this review, we first introduce the working principles of the DE and PVC gel separately. Next, we summarize the DE materials and the preparation of PVC gel. Then, we review the electrodes and self-sensing systems of the two materials. Lastly, we present the initial applications of these two materials for wearable assistance and rehabilitation.

Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader-follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer-controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.

WiFi-based Human activity recognition has attracted attention in the human-computer interaction, smart homes, and security monitoring fields. We first construct a WiFi-based activity dataset, namely WiAR, to provide a benchmark for existing works. Then, we leverage the moving variance of CSI to detect the start and end of activity. Moreover, we present K-means-based subcarrier selection mechanism according to subcarrier's sensitivity on human activity to enhance the robustness of human activity recognition. Finally, we leverage several classification algorithms to evaluate the performance of WiAR. Our results show that WiAR satisfies primary demand and achieves an average accuracy of greater than 93% using SVM, 80% using kNN, Random forest, and Decision tree.

We present trajectory tracking and balance control of an autonomous bikebot. The bikebot is a single-track autonomous mobile robot that is designed to study unstable physical human-robot interactions. The controller is built on the property of the external-internal convertible (EIC) structure for the bikebot dynamics. We present two types of the designs and analyses of the control systems and also demonstrate their performance through extensive experiments. The comparison results with the human riding experiments show that the rider's motor skills generate the similar strategies as the proposed EIC-based balance control.

Slip is the major cause of falls in human locomotion. We present a new bipedal modeling approach to capture and predict human walking locomotion with slips. Compared with the existing bipedal models, the proposed slip walking model includes the human foot rolling effects, the existence of the double-stance gait and active ankle joints. One of the major developments is the relaxation of the nonslip assumption that is used in the existing bipedal models. We conduct extensive experiments to optimize the gait profile parameters and to validate the proposed walking model with slips. The experimental results demonstrate that the model successfully predicts the human recovery gaits with slips.

We present the development of the gyroscopic-balanced control of an autonomous bikebot. The bikebot is an actively controlled bicycle-based robotic platform with a gyro-balancer developed to study human dynamic postural balance motor skills through unstable physical human-robot interactions. We also present a dynamic model and analysis for stationary bikebot. A nonlinear balancing controller is designed to stabilize the underactuated stationary bikebot on an orbital trajectory around the unstable equilibrium point that is coupled with another orbit of the actuated gyro-balancer. We then demonstrate the analysis and control design with experimental validations. Finally, we present a set of human riding experiments to show how the bikebot can be used to perturb and excite human sensorimotor feedback loop for dynamic postural balance motor skills.

Although many wearable sensors have been developed or commercialized for gait analysis, ambulatory force sensors and motion sensors were rarely integrated to implement complete human dynamics analysis. Three dimensional (3D) lower limb kinematic and kinetic analysis based on ambulatory measurements is introduced in this paper. We developed a wireless sensor system composed of a mobile force plate system, 3D motion analysis units and a wireless data logger. 3D motions of body segment and triaxial ground reaction force (GRF) could be simultaneously measured using the system, and the data obtained from sensor units on thighs, shanks and feet could be transferred to a personal computer by wireless local area network (LAN). A stick-chain model was built to visually analyze lower limb postures and joint trajectories, and an inverse dynamics method was adopted to calculate triaxial joint moments. We conducted application experiments on volunteers to visualize and analyze 3D segment orientations and joint moments during successive gait for level ground walking, treadmill walking, and stair climbing. Experiment results showed that 3D segment orientations and joint trajectories could be shown and analyzed using the stick chain model, and that joint loads for different walking conditions could be compared in each gait cycle.

Numerous rehabilitation robots have been developed to deliver therapy for hemiplegic patients with a unilateral-disabled limb or for aged persons with motor-function degenerated limbs. Recently, much attention has been paid to the development of robots that support bilateral arm training in various patterns. It has been proved that the coordination of two limbs can reduce hypertonia and abnormal synergies compared to unilateral limb training. However, traditional robots realized different modes with the robot providing a corresponding force for the impaired limb. This paper proposes a novel bimanual training system. A subject controls master and slave terminal handles with his/her two limbs. The less impaired limb provides a corresponding force for the more impaired limb to achieve different training patterns. No matter in which mode, the two limbs accomplish symmetric movement. An experimental prototype was built. Frequency response test and training tests on 9 healthy subjects in active-resisted and active-assisted modes were performed. Experimental results confirm that the system can correctly respond a subject's commands if velocity was changed within the frequency range of 30 Hz. For each training mode, the average position error, and RMS (root mean square) values of position errors and velocity errors that between the reference values and actual values of a motion tracking task were reduced after practice. This proves the effectiveness of the system for self-assisted rehabilitation training.

To achieve accurate and efficient interaction with humans, robot training is indispensable to make robot cooperate with different host. We are focusing on development of a human motion analysis system to real-time measure body segment orientations. Imitation control was applied on a biped robot based on measurements of the developed wearable sensor system. Experimental study was implemented to verify the synchronous imitation control method proposed for the biped robot, and verification results proved the feasibility of the proposed control method. Through comparing results obtained from direct imitation control method and improved method based on training algorithm considering the personal motion pattern, we found that the imitation control accuracy was markedly improved, and the three-axial average errors of x- y- and z- moving displacements related to leg length were 12%, 8% and 4% respectively.

A wearable sensory system for human lower extremity motion analysis is proposed, and an intelligent computation method for this sensory system is presented. The standard method for human motion analysis is the optical motion analysis using high-speed cameras to record human 3D motion, but this method is only limited in the laboratory research, because it requires expensive devices, large space and time-exhausted calibration experiments. In this study, two low-cost human motion analysis systems are constructed, deferent from the conventional 3D motion analysis system based on high-speed camera. These wearable systems incorporate gyroscopes (ENC-05EB) to measure angular velocities of body segments, and two-axis accelerometers (ADXL202) are used to measure the accelerations for the purpose of leg (foot, shank and thigh) motion analysis in every human motion cycle. The first wearable sensor system is designed for only foot motion analysis and the second system can be used for a leg (foot, shank and thigh) motion analysis. Then based on the two sensor systems, a fuzzy inference system (FIS) is developed for the calculation of the gait phases fed by sensors outputs. A digital filter is also designed to eliminate noises from of the output of the fuzzy inference system, which enhances robustness of the system. Finally, experimental study is conducted to validate the wearable sensor systems using an optical motion analysis system (Hi-DCam)