Novel Design and Adaptive Fuzzy Control of a Lower-Limb Elderly Rehabilitation

Research trends and frontiers in rehabilitation after total knee arthroplasty: based on bibliometric and visualization analysis

BACKGROUND

Total knee arthroplasty (TKA) is an effective treatment for end-stage knee osteoarthritis, and postoperative rehabilitation is crucial. However, a comprehensive bibliometric analysis of this area has yet to emerge. This study aims to visualize the research trends in postoperative rehabilitation after TKA through bibliometric analysis and explore current research frontiers and hotspots.

METHODS

Publications related to postoperative rehabilitation following TKA were identified and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace and VOSviewer were used for bibliometric and visualization analysis.

RESULTS

From January 1, 2000, to December 31, 2022, a total of 1,422 articles on TKA postoperative rehabilitation were identified from the database. The number of publications and citations showed steady growth during this period. The United States was the major contributor in this field, with the University of Colorado being the most active institution domestically. J Arthroplasty ranked first in both publication volume and total citations among all journals. Stevens-Lapsley, JE, and Mizner, RL were the two most influential authors. Reference and keyword analyses suggest that remote or home-based rehabilitation, the development of novel prehabilitation techniques, and pain management hold significant research potential, constituting current research hotspots.

CONCLUSION

This study quantitatively identified and assessed the current research status and trends in perioperative rehabilitation management for TKA through bibliometric and visualization analyses. It provides essential information for scholars in the field of TKA postoperative rehabilitation research, highlighting key research frontiers and trends.

Active Assistive Design and Multiaxis Self-Tuning Control of a Novel Lower Limb Rehabilitation Exoskeleton

This paper presented the mechanical design and control of a lower limb rehabilitation exoskeleton named “the second lower limb rehabilitation exoskeleton (LLRE-II)”. The exoskeleton with a lightweight mechanism comprises a 16-cm stepless adjustable thigh and calf rod. The LLRE-II weighs less than 16 kg and has four degrees of freedom on each leg, including the waist, hip, knee, and ankle, which ensures fitted wear and comfort. Motors and harmonic drives were installed on the joints of the hip and knee to operate the exoskeleton. Meanwhile, master and slave motor controllers were programmed using a Texas Instruments microcontroller (TMS320F28069) for the walking gait commands and evaluation boards (TMS320F28069/DRV8301) of the joints. A self-tuning multiaxis control system was developed, and the performance of the controller was investigated through experiments. The experimental results showed that the mechanical design and control system exhibit adequate performance. Trajectory tracking errors were eliminated, and the root mean square errors reduced from 6.45 to 1.22 and from 4.15 to 3.09 for the hip and knee, respectively.

Psychological health analysis based on fuzzy assisted neural network model for sports person

Stress is indeed a life aspect that influences everyone, even though athletes seem to suffer from it one step ahead of others because of the extent they are expected to balance between coursework, workouts, and competitions, along with everyday life and family stress. Therefore, an efficient psychological health analysis for sportspersons is crucial in sports training. This paper introduces a Fuzzy-assisted Neural Network model for Psychological Health Analysis (FNN-PHA) to assess mental stress by monitoring the Electro Cardio Gram signal (ECG), Electroencephalogram (EEG), and Pulse rate. This paper integrates the fuzzy assisted Petri nets, fuzzy assisted k-complex detector, and fuzzy assisted transient time analyzer to ensure the psychological health analysis neural network model’s adaptive performance. The strength of the proposed fuzzy model demonstrates interpretability against the accuracy of different criteria. The simulation analysis shows that the FNN-PHA model enhances the prediction ratio of 98.7%, emotional stability of 96.7%, personal growth of 95.7%, physical fitness level of 97.8%, and depression ratio of 12.5% when compared to other existing models.

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in ± 0 . 5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human–machine hybrid control model.