Assistive Control System for Upper Limb Rehabilitation Robot

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises

In conventional and robotic rehabilitation, the patient's active participation in exercises is essential for the maximum functional output to be received from therapy. In rehabilitation exercises performed with robotic devices, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient's therapy performance to improve active participation. However, the existing therapy performance evaluation methods are based on either some specific device designs or certain therapy tasks, which limits their widespread use. In this paper, the effectiveness of a participation assessment system, which can evaluate patients' therapy performance, tiredness, and slacking independent of any device design and therapy exercise, was clinically tested on ten patients diagnosed with frozen shoulder syndrome. The patients performed exercises using the system once a week throughout their 4-week treatment period. Multiple clinical measurements and scales were employed during the clinical study to assess patients' progress and status, such as tiredness throughout the therapy process. The clinical data, along with the patient findings obtained from the participation assessment system, were statistically analyzed and compared. The findings revealed that the patients' improvements and progress during the therapy process clinically coincide with the variations in the performance evaluation results of the system, and the implemented method successfully assesses the patients' participation during the rehabilitation exercises.

A Self-Aligning Upper-Limb Exoskeleton Preserving Natural Shoulder Movements: Kinematic Compatibility Analysis

NESM- γ is an upper-limb exoskeleton to train motor functions of post-stroke patients. Based on the kinesiology of the upper limb, the NESM- γ includes a four degrees-of-freedom (DOF) active kinematic chain for the shoulder and elbow, along with a passive chain for self-aligning robotic joint axes with the glenohumeral (GH) joint's center of rotation. The passive chain accounts for scapulohumeral rhythm and trunk rotations. To assess self-aligning performance, we analyzed the kinematic and electromyographic data of the shoulder in eight healthy subjects performing reaching tasks under three experimental conditions: moving without the exoskeleton (baseline), moving while wearing the exoskeleton with the passive DOFs properly functioning, i.e., unlocked (human-in-the-loop(HIL)-unlocked), and with the passive DOFs locked (HIL-locked). Comparison of baseline and HIL-unlocked conditions showed nearly unchanged anatomical movement patterns, with a root-mean-square error of shoulder angle lower than 5 deg and median deviations of the GH center of rotation below 20 mm. Peak muscle activations showed no significant differences. In contrast, the HIL-locked condition deviated significantly from the baseline, as observed by the trunk and GH trajectory deviations up to 50 mm, accompanied by increased peak muscle activations in the Deltoid and Upper Trapezius muscles. These findings highlight the need for kinematic solutions in shoulder exoskeletons that can accommodate the movements of the entire shoulder complex and trunk to achieve kinematic compatibility.

Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis

BACKGROUND

Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis.

OBJECTIVE

Using bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years.

METHODS

The Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace.

RESULTS

We obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots.

CONCLUSIONS

Research on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research.

Kinematic analysis and development of cable-driven rehabilitation robot for cerebral palsy patients

This study aimed to analyze the kinematic development of a rehabilitation cable robot for patients with cerebral palsy problems. For this purpose, the walking pattern of a healthy person was analyzed in the robot by extracting his kinematic model. Therefore, a seven-link model was considered, and changes in the mass center of the links and then movements during the gait cycle were obtained with the angles related to joint changes. Next, the person’s integration with the rehabilitation cable robot was investigated with the resolution of the direct kinematic problem. In addition, the change-related outputs of the cables were obtained by the person’s movement and the attached belt. The robot was further proposed because the specific change diagram of the cables facilitates understanding how much motor torque is needed to change the length of the cable. It is noteworthy that the static person balance is provided in the existing rehabilitation robots. However, in this structure, the balance is done by the six degrees of freedom robot so that the robot can return the person to the original path when he loses his balance. Cable systems for the lower limbs (thighs and shanks) are also simulated to rehabilitate the patient. The obtained results from the simulation and the obtained output from kinematic equations for lower limb movements were also compared, and the highest deference was 2.2, 1.8, 1.8, and 1.5% for shank-back, shank-front, thigh-back, and thigh-front of the leg in the corresponding points in the outputs of both software, respectively.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Design and Assist-as-Needed Control of Flexible Elbow Exoskeleton Actuated by Nonlinear Series Elastic Cable Driven Mechanism

Exoskeletons can assist the daily life activities of the elderly with weakened muscle strength, but traditional rigid exoskeletons bring parasitic torque to the human joints and easily disturbs the natural movement of the wearer’s upper limbs. Flexible exoskeletons have more natural human-machine interaction, lower weight and cost, and have great application potential. Applying assist force according to the patient’s needs can give full play to the wearer’s remaining muscle strength, which is more conducive to muscle strength training and motor function recovery. In this paper, a design scheme of an elbow exoskeleton driven by flexible antagonistic cable actuators is proposed. The cable actuator is driven by a nonlinear series elastic mechanism, in which the elastic elements simulate the passive elastic properties of human skeletal muscle. Based on an improved elbow musculoskeletal model, the assist torque of exoskeleton is predicted. An assist-as-needed (AAN) control algorithm is proposed for the exoskeleton and experiments are carried out. The experimental results on the experimental platform show that the root mean square error between the predicted assist torque and the actual assist torque is 0.00226 Nm. The wearing experimental results also show that the AAN control method designed in this paper can reduce the activation of biceps brachii effectively when the exoskeleton assist level increases.

Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements

Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on upper-limb motions is lost in the process of feature extraction for sEMG signals, and there exists the fact that only a small variety of rehabilitation movements can be distinguished, and the classification accuracy is easily affected. To solve these dilemmas, first, a multiscale time-frequency information fusion representation method (MTFIFR) is proposed to obtain the time-frequency features of multichannel sEMG signals. Then, this paper designs the multiple feature fusion network (MFFN), which aims at strengthening the ability of feature extraction. Finally, a deep belief network (DBN) was introduced as the classification model of the MFFN to boost the generalization performance for more types of upper-limb movements. In the experiments, 12 kinds of upper-limb rehabilitation actions were recognized utilizing four sEMG sensors. The maximum identification accuracy was 86.10% and the average classification accuracy of the proposed MFFN was 73.49%, indicating that the time-frequency representation approach combined with the MFFN is superior to the traditional machine learning and convolutional neural network.

The classification of movement intention through machine learning models: the identification of significant time-domain EMG features

Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.

MECHANICAL CHARACTERISTICS ANALYSIS OF A BIONIC MUSCLE CABLE-DRIVEN LOWER LIMB REHABILITATION ROBOT

Cable-driven parallel robots (CDPR) have been well used in the rehabilitation field. However, the cables can provide the tension in a single direction, there is a pseudo-drag phenomenon of the cables in the CDPR, which will have a great impact on the safety of patients. Therefore, the novelty of this work is that a bionic muscle cable is used to replace the ordinary cable in the CDPR, which can solve the pseudo-drag phenomenon of the cables in the CDPR and improve the safety performance of the rehabilitation robot. The cable-driven lower limb rehabilitation robot with bionic muscle cables is called as the bionic muscle cable-driven lower limb rehabilitation robot (BMCDLR). The motion planning of the rigid branch chain of the BMCDLR is studied, and the dynamics and system stiffness of the BMCDLR are analyzed based on the man–machine model in this paper. The influence of the parameters of the elastic elements in the bionic muscle cables on the mechanical characteristics of the BMCDLR system was analyzed by using simulation experiments. The research results can provide a reference basis for research on the safety evaluation and control methods of the BMCDLR system.

Safety Evaluation and Experimental Study of a New Bionic Muscle Cable-Driven Lower Limb Rehabilitation Robot

Safety is a significant evaluation index of rehabilitation medical devices and a significant precondition for practical application. However, the safety evaluation of cable-driven rehabilitation robots has not been reported, so this work aims to study the safety evaluation methods and evaluation index of cable-driven rehabilitation robots. A bionic muscle cable (BM cable) is proposed to construct a bionic muscle cable-driven lower limb rehabilitation robot (BM-CDLR). The working principle of the BM-CDLR is introduced. The safety performance factors are defined based on the mechanical analysis of the BM-CDLR. The structural safety evaluation index and the use safety evaluation index of the BM-CDLR are given by comprehensively considering the safety performance factors and a proposed speed influence function. The effect of the structural parameters of the elastic elements in the BM cable on the safety performance factors and safety of the BM-CDLR is analyzed and verified by numerical simulations and experimental studies. The results provide the basis for further study of the compliance control strategy and experiments of the human-machine interaction of the BM-CDLR.

Leg-amplitude differentiation guided by haptic and visual feedback to detect alterations in motor flexibility due to Total Knee Replacement

Following total knee replacement (TKR), patients often persist in maladaptive motor behavior which they developed before surgery to cope with symptoms of osteoarthritis. An important challenge in physical therapy is to detect, recognize and change such undesired movement behavior. The goal of this study was to measure the differences in clinical status of patients pre-TKR and post-TKR and to investigate if differences in clinical status were accompanied by differences in the patients'' motor flexibility. Eleven TKR participants were measured twice: pre-TKR and post-TKR (twenty weeks after TKR). In order to infer maladaptation, the pre-TKR and post-TKR measurements of the patient group were separately compared to one measurement in a control group of fourteen healthy individuals. Clinical status was measured with the Visual Analogue Scale (VAS) for pain and knee stiffness and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Furthermore, Lower-limb motor flexibility was assessed by means of a treadmill walking task and a leg-amplitude differentiation task (LAD-task) supported by haptic or visual feedback. Motor flexibility was measured by coordination variability (standard deviation (SD) of relative phase between the legs) and temporal variability (sample entropy) of both leg movements. In the TKR-group, the VAS-pain and VAS- stiffness and the subscales of the KOOS significantly decreased after TKR. In treadmill walking, lower-limb motor flexibility did not significantly change after TKR. Between-leg coordination variability was significantly lower post-TKR compared to controls. In the LAD-task, a significant decrease of between-leg coordination variability between pre-TKR and post-TKR was accompanied by a significant increase in temporal variability. Post-TKR-values of lower-limb flexibility approached the values of the control group. The results demonstrate that a clinically relevant change in clinical status, twenty weeks after TKR, is not accompanied by alterations in lower-limb motor flexibility during treadmill walking but is accompanied by changes in motor flexibility towards the level of healthy controls during a LAD-task with visual and haptic feedback. Challenging patients with non-preferred movements such as amplitude differentiation may be a promising tool in clinical assessment of motor flexibility following TKR.

Design and verification of a human-robot interaction system for upper limb exoskeleton rehabilitation

This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human-robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed for the control system to mitigate the noise and time delay of the collected signal. The clipping filtering method was used to filter the accidental error and avoid the safety problem caused by a mistrigger. A modified adaptive Kalman filter was used to account for the sudden change of the motion state during rehabilitation training. The results show that the intent recognition system designed herein can accurately recognize the human-robot interaction information, and estimate the intent of human motion in time. Therefore, it can be concluded that the designed system effectively follows the predicted motion intent with the proposed method, which is a significant improvement for human-robot interaction control of upper limb extremity rehabilitation robots.

Multi-connection load compensation and load information calculation for an upper-limb exoskeleton based on a six-axis force/torque sensor

In this article, a method of multi-connection load compensation and load information calculation for an upper-limb exoskeleton is proposed based on a six-axis force/torque sensor installed between the exoskeleton and the end effector. The proposed load compensation method uses a mounted sensor to measure the force and torque between the exoskeleton and load of different connections and adds a compensator to the controller to compensate the component caused by the load in the human–robot interaction force, so that the human–robot interaction force is only used to operate the exoskeleton. Therefore, the operator can manipulate the exoskeleton with the same interaction force to lift loads of different weights with a passive or fixed connection, and the human–robot interaction force is minimized. Moreover, the proposed load information calculation method can calculate the weight of the load and the position of its center of gravity relative to the exoskeleton and end effector accurately, which is necessary for acquiring the upper-limb exoskeleton center of gravity and stability control of whole-body exoskeleton. In order to verify the effectiveness of the proposed method, we performed load handling and operational stability experiments. The experimental results showed that the proposed method realized the expected function.

A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information

Power assistive devices have been developed in recent years. To detect the wearer’s motion, conventional devices require users to wear sensors. However, wearing many sensors increases the wearing time, and usability of the device will become worse. We developed a soft gait assistive suit actuated by pneumatic artificial rubber muscles (PARMs) and proposed its control method. The proposed suit is easy to wear because the attachment unit does not have any electrical sensors that need to be attached to the trainee’s body. A target application is forward walking exercise on a treadmill. The control unit detects the pre-swing phase in the gait cycle using the pressure information in the calf back PARMs. After the detection, the suit assists the trainee’s leg motion. The assist force is generated by the controlled PARM pressure, and the pressure input time is changed appropriately considering the gait cycle time. We conducted walking experiments; (1) verifies the proposed control method works correctly, and (2) verifies whether the gait assistive suit is effective for decreasing muscular activity. Finally, we confirmed that the accurate phase detection can be achieved by using the proposed control method, and the suit can reduce muscular activity of the trainee’s leg.

Series elastic actuation of an elbow rehabilitation exoskeleton with axis misalignment adaptation

Powered exoskeletons can facilitate rehabilitation of patients with upper limb disabilities. Designs using rotary motors usually result in bulky exoskeletons to reduce the problem of moving inertia. This paper presents a new linearly actuated elbow exoskeleton that consists of a slider crank mechanism and a linear motor. The linear motor is placed beside the upper arm and closer to shoulder joint. Thus better inertia properties can be achieved while lightweight and compactness are maintained. A passive joint is introduced to compensate for the exoskeleton-elbow misalignment and intersubject size variation. A linear series elastic actuator (SEA) is proposed to obtain accurate force and impedance control at the exoskeleton-elbow interface. Bidirectional actuation between exoskeleton and forearm is verified, which is required for various rehabilitation processes. We expect this exoskeleton can provide a means of robot-aided elbow rehabilitation.