736 publications from this institution
The application of robotics in the field of home rehabilitation training has revolutionized the way patients receive treatment. However, current rehabilitation robotic systems often lack diverse training methods and sufficient feedback channels, resulting in challenges for patients to sustain long-term engagement in rehabilitation training. This paper introduces a novel hand rehabilitation robot system that leverages virtual reality technology, multi-channel feedback technology, and mirror therapy to facilitate autonomous hand rehabilitation treatment for patients in a home setting. The system comprises a soft glove, a computer equipped with virtual interaction scenes, Leapmotion sensors, and a soft glove control box. To evaluate the usability and patient acceptance of the system, a clinical trial involving five patients was conducted. The trial results demonstrated noteworthy improvements in finger grip strength, with an increase from 8.74 ± 14.2 N to 17.82 ± 13.63 N when utilizing the soft glove. Furthermore, patients' upper extremity function assessment scale (ARAT) scores exhibited improvement from 12.4 ± 23.44 to 24.44 ± 25.89, and their functional ability for daily living (ADL) showed improvement from 43.8 ± 47.22 to 50.6 ± 43.24. These measurements indicated significant enhancements compared to the baseline, signifying that the proposed system did not compromise finger functionality. Additionally, the results of a user acceptance questionnaire, consisting of seven surveys administered to the patients, demonstrated a positive score of 4.18 ± 0.61 and a negative score of 0.95 ± 0.57. These outcomes reflect a high level of acceptance among patients, affirming the system's safety and effectiveness in providing a comfortable and reliable platform for rehabilitation training.
With the advent of the holographic communication era, the amount and types of information continue to increase. People urgently need to explore new methods of information interaction. Tactile interaction technology can produce tactile feedback, greatly enhancing multimedia interactivity and user immersion, and it is an indispensable key technology in future multisensory interactive communication. This paper will introduce the latest research progress of tactile feedback devices in the past 5 years. According to different installation methods, it will be divided into four types and will summarize the characteristics of the most representative tactile feedback devices in the past 5 years in a table. Subsequently, the paper will summarize the existing problems of each type of tactile feedback device and analyze the future development trends of this field. The application of haptic feedback technology is prospected in the end.
The utility to decode hand movement parameters is significant to the control of artificial limb in the BCI fields. Most previous studies have adopted amplitude features of the low-frequency EEG signals to decode hand movement parameters. In this study, we have investigated the instantaneous phase of the low-frequency EEG signals attained by Hilbert transform for such a task for the first time, and compared its decoding accuracy with that of the amplitude features. An experiment was carried out that 5 subjects executed a center-out reaching task in two sessions. Then the Multiple Linear Regression (MLR) model is used to decode hand movement parameters based on the amplitude feature and the phase feature, respectively. The performance of the proposed approach is evaluated by calculating the correlation coefficients between the recorded parameters and the reconstructed parameters. The experiments results show that compared to the decoder with the amplitude feature, the correlation coefficients obtained by the decoder with the phase feature have increased 27.8% (X-position), 24.1% (Y-position), 27.9% (X-velocity), 20.9% (Y-velocity).
Location information is critical to monitoring activities in wireless sensor networks. This paper proposed an adaptive localisation algorithm of mobile node in wireless sensor network for solving this problem. The structure decomposition, electronic control and kinematics model of the proposed mobile node have been investigated, respectively. Improvements have been demonstrated through eliminates the positioning error caused by situations such as node self–energy, data packet loss, low measured value and singular values. A testbed has finally been created for validating the adaptive localisation capability of mobile nodes in wireless sensor network. Experimental results show that the proposed adaptive localisation algorithm can be applied in real use, and precision and stability are upper, and promote the speed and practicability of mobile node in wireless sensor networks using in undesirable environments.
In this study, dynamic characteristics of a robot six-axis wrist force/torque (F/T) sensor with crossbeam elastomer are analyzed by two methods of model identification, a method for simultaneous identification of order and parameters of the model (SIM) and a method based on the differential evolution (DE) algorithm. Firstly, by establishing the simplified mechanical model and finite element (FE) model, respectively, natural frequency of the six-axis wrist F/T sensor is calculated. Secondly, dynamic calibration experiment is conducted. Lastly, two dynamic models of the sensor are identified by SIM and DE methods and the dynamic characteristics of the sensor, such as natural frequency and working band, are further analyzed. Comparing experimental values with the theoretical values, the results show that this sensor has a wide dynamic range with the first natural frequency at more than 1600 Hz, working bands (±5%) are more than 400 Hz, and the step response oscillation is intense. This study can provide a reference for the application of the six-axis F/T sensor in the field of dynamic measurement.
Brain-machine interface (BMI) can be used to control robotic arm to assist paralysis people improving their quality of life. However process control of objects grasping is still a complex task for BMI users. High efficiency and accuracy is hard to achieve in objects grasping process even after extensive training. An important reason is lack of sufficient feedback information for performing the closed-loop control. In this study, we describe a method of augmented reality (AR) guiding assistance to provide extra feedback information to the user for closed-loop control. A hybrid BMI based system with AR feedback is proposed to evaluate the performance of our method in objects grasping task using robotic arm. Reaching and releasing tasks are completed by the robotic arm automatically. For the grasping task controlled by the user, AR is used to enrich the normal visual information during the grasping process to provide the BMI user augmented feedback information about the gripper status in real time. The feasibility of the proposed system both in open-loop (visual inspection) and closed-loop (AR feedback) are compared. According to our experimental results obtained from 5 subjects, the time used for controlling the robotic arm to grasp objects with AR feedback reduces more than 5s and the error rate of the gripper aperture decreases approximately 20% compared to those of grasping with normal visual inspection only. The results reveal that the BMI user can benefit from the information provided by AR interface in the grasping task.
Most target grabbing problems have been dealt with by computer vision system, however, computer vision method is not always enough when it comes to the precision contact grabbing problems during the teleoperation process, and need to be combined with the stiffness display to provide more effective information to the operator on the remote side. Therefore, in this paper a more portable stiffness display device with a small volume and extended function is developed based on our previous work. A new static load calibration of the improved stiffness display device is performed to detect its accuracy, and the relationship between the stiffness and the position is given. An effective target grabbing strategy is presented to help operator on the remote side to judge and control and the target is classified by multi-class SVM (supporter vector machine). The teleoperation system is established to test and verify the feasibility. A special experiment is designed and the results demonstrate that the improved stiffness display device could greatly help operator on the remote side control the telerobot to grab target and the target grabbing strategy is effective.
A new softness display system has been developed which is based on the principle of deformable length of elastic element control (DLEEC). In this system, operator can feel the softness of the virtual objects with the softness display device. Compared with other haptic device, the device is passive and exert the react force only when the operator "actively touch" the virtual objects. The stability of the softness display system is analyzed, and some experimental results are presented to show the validity of the proposed approach
The classification of motor imagery Electroencephalogram (EEG) of the same limb is important for natural control of neuroprosthesis. Due to the close spatial representations on the motor cortex area of the brain, the discrimination of the different motor imagery tasks is challenging. In this paper, phase synchronization information was proposed to classify motor imagery EEG within the same limb. In addition, non-portable was compared with portable EEG acquisition equipment for the purpose of making the brain computer interface (BCI) system more practical. In the non-portable case, the average accuracy of the binary classification and 3-class classification was 60.6% and 42.7%. In the portable case, the average EEG decoding accuracy of 58.5% and 39.9% was achieved for the two and three tasks. Furthermore, in both two cases, different sets of electrode pairs got the similar results. Moreover, we found that the proposed phase information based method was less sensitive to the number of EEG channels and had less performance degradation in portable EEG equipment. These results show it is possible to use phase synchronization information to discriminate different motor imagery tasks within the same limb. Eventually, this will potentially make the control of neuroprosthesis or other rehabilitation device more natural and intuitive.
Force feedback helps the operator a lot to perform tasks in teleoperation. But the time delay in communication makes teleoperation system with direct force feedback unstable. In this paper, a novel approach is proposed to create and verify the graphic and dynamic model of the remote physical environment. This model can provide the operator with real-time force feedback which does not depend on the existing time delay in the system. The dynamic parameters of the model are corrected and updated on-line based on information from position and force sensors to keep equal to the remote environment. Then the VR (virtual reality)-based teleoperation system developed in our laboratory is described. Experiments have been done to demonstrate the effectiveness of the proposed method.
