Intelligent ankle-foot prosthesis based on human structure and motion bionics.

The ankle-foot prosthesis aims to compensate for the missing motor functions by fitting the motion characteristics of the human ankle, which contributes to enabling the lower-limb amputees to take care of themselves and improve mobility in daily life. To address the problems of poor bionic motion of the ankle-foot prosthesis and the lack of natural interaction among the patient, prosthesis, and the environment, we developed a complex reverse-rolling conjugate joint based on the human ankle-foot structure and motion characteristics, the rolling joint was used to simulate the rolling-sliding characteristics of the knee joint. Meanwhile, we established a segmental dynamics model of the prosthesis in the stance phase, and the prosthetic structure parameters were obtained with the optimal prosthetic structure dimensions and driving force. In addition, a carbon fiber energy-storage foot was designed based on the human foot profile, and the dynamic response of its elastic strain energy at different thicknesses was simulated and analyzed. Finally, we integrated a bionic ankle-foot prosthesis and experiments were conducted to verify the bionic nature of the prosthetic joint motion and the energy-storage characteristics of the carbon fiber prosthetic foot. The proposed ankle-foot prosthesis provides ambulation support to assist amputees in returning to social life normally and has the potential to help improve clinical viability to reduce medical rehabilitation costs.

Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Successful Treatment of Refractory Synovitis, Acne, Pustulosis, Hyperostosis, and Osteitis (SAPHO) Syndrome with Baricitinib, a Janus Kinase Inhibitor.

Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome, a rare immune-mediated inflammatory disease, poses diagnostic and therapeutic challenges owing to its multi-system involvement, high heterogeneity, and lack of specific laboratory tests. Additionally, lacking evidence-based treatment recommendations, with the primary approach focusing on symptomatic relief. Herein, we report the case of a 32-year-old Chinese woman who presented with recurrent, generalized multiple osteoarticular pain lasting over one year and skin erythema pustulosis for 11 months. Traditional treatments, including non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, disease-modifying antirheumatic drugs (DMARDs), and other traditional approaches, yielded no significant effects. Despite the prior use of adalimumab and acitretin capsules, the treatment remained unsatisfying, especially regarding the skin lesions. Considering the complex pathogenesis of SAPHO syndrome, the patient was orally administered baricitinib (2 mg), a Janus kinase (JAK) inhibitor, twice daily. A notable improvement in both skin lesions and osteoarticular pain was observed within two weeks of treatment initiation. Subsequently, the dosage of baricitinib was halved and continued for an additional three months, during which regular follow-ups revealed neither disease recurrence nor adverse effects. Collectively, the successful treatment of refractory SAPHO syndrome with baricitinib presents a promising implication for addressing the therapeutic challenges of this rare autoimmune condition, offering a potential breakthrough in managing its complex manifestations.

Bionic intelligent ankle-foot prosthesis based on the conjugate curved surface.

Given the poor biomimetic motion of traditional ankle-foot prostheses, it is of great significance to develop an intelligent prosthesis that can realize the biomimetic mechanism of human feet and ankles. To this end, we presented a bionic intelligent ankle-foot prosthesis based on the complex conjugate curved surface. The proposed prosthesis is mainly composed of the rolling conjugated joints with a bionic design and the carbon fiber energy-storage foot. We investigated the flexibility of the prosthetic ankle joint movement, and the ability of the prosthetic foot to absorb ground impact during the gait cycle. Experimental results showed the matching of the ankle/toe position relationship of the human foot during simulated walking, which is helpful to realize the biomimetic motion of the human foot and ankle. It can also help therapists and clinicians provide better rehabilitation for lower-limb amputees.

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study.

Objective.The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control.Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure.Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R2= 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R2= 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability.Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series.

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.

Evaluation of Synergy-Based Hand Gesture Recognition Method Against Force Variation for Robust Myoelectric Control.

The non-stationary characteristics of surface electromyography (sEMG) and possible adverse variations in real-world conditions make it still an open challenge to realize robust myoelectric control (MEC) for multifunctional prostheses. Variable muscle contraction level is one of the handicaps that may degrade the performance of MEC. In this study, we proposed a force-invariant intent recognition method based on muscle synergy analysis (MSA) in the setting of three self-defined force levels (low, medium, and high). Specifically, a fast matrix factorization algorithm based on alternating non-negativity constrained least squares (NMF/ANLS) was chosen to extract task-specific synergies associated with each of six hand gestures in the training stage; while for the testing samples, we used the non-negative least square (NNLS) method to estimate neural commands for movement classification. The performance of proposed method was compared with conventional pattern recognition (PR) method consisting of LDA (linear discrimination analysis) classifier and representative features in three offline evaluation scenarios. Statistical tests on ten able-bodied subjects revealed no significant difference in intra-force-level (p = 0.353) and multi-force-level (p = 0.695) accuracy; But the synergy-based method performed significantly better than conventional PR-based method under inter-force-level conditions (p < 0.05). Similar results were observed for nine amputee subjects though there was a drop in the classification accuracy. This study was the first to concurrently demonstrate the robustness and predictive power of task-specific synergies under variant force levels and explore their potential for reliable intent recognition against force variation. Although the online performance is yet to be demonstrated, the proposed method is characterized by simple training procedure and acceptable computational efficiency, which would potentially provide an alternative approach for the development of clinically viable prostheses and rehabilitation robots driven by sEMG.

A Novel Underactuated Robotic Finger with Variable Stiffness Joints.

Existing robotic hands mostly consist of rigid finger mechanism with constant joint stiffness, leading to poor handling performance and even unexpected safety issues. This paper proposed a novel underactuated robotic finger with variable stiffness joints based on human finger anatomy and electrostatic adhesion(ESA) principle. The proposed finger is unique in the 3D printable one-piece body structure consisting of three similar joints, actuated by only one linear actuator to mimic the flexion/extension movement of the human finger. It is characterized by simple actuation, light weight, low cost and compliant grasp. We constructed a portable finger prototype to investigate the variable stiffness performance. It turns out that the joint stiffness shows a growing trend as the applied voltage increases, which verifies the effectiveness of this design. The proposed novel finger indicates potential applications in service robots and prosthetic hands.