Perception of defecation intent: applied methods and technology trends.

The aging population has led to a widening gap between the supply and demand for defecation care. To address this issue, the development of defecation care devices is the most direct and effective solution. Pre-defecation care devices offer a more personalized and comfortable alternative to the conventional post-defecation care devices currently available on the market. Furthermore, they facilitate greater patient involvement in the care process. Real-time monitoring and accurate identification of defecation intention are key technologies in the development of pre-defecation nursing devices. Automatic and accurate online monitoring of defecation intention can provide accurate early warning information for differentiated defecation assistance and cleansing care, effectively reducing nursing workload and improving patients' quality of life. However, there are relatively few studies on real-time monitoring and accurate identification of defecation intention. This review summarizes the existing defecation intention sensing technologies and their monitoring principles and research status, and explores the potential development direction of defecation intention sensing systems by comparing the characteristics and application conditions of various sensing technologies, which provides a direction for perception strategies for future defecation intention monitoring and early warning research.

State of the art of brain function detection technologies in robot-assisted lower limb rehabilitation.

BACKGROUND: With an aging population, the prevalence of neurological disorders is increasing, leading to a rise in lower limb movement disorders and, in turn, a growing need for rehabilitation training. Previous neuroimaging studies have shown a growing scientific interest in the study of brain mechanisms in robot-assisted lower limb rehabilitation (RALLR). OBJECTIVE: This review aimed to determine differences in neural activity patterns during different RALLR tasks and the impact on neurofunctional plasticity. METHODS: Sixty-five articles in the field of RALLR were selected and tested using three brain function detection technologies (BFDT). RESULTS: Most studies have focused on changes in activity in various regions of the cerebral cortex during different lower limb rehabilitation tasks, but have also increasingly focused on functional changes in other cortical and deep subcortical structures. Our analysis also revealed a neglect of certain task types. CONCLUSION: We identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different RALLR tasks.

Control strategies for trunk exoskeletons based on motion intent recognition: A review.

BACKGROUND: Wearable trunk exoskeletons hold immense potential in fields such as healthcare and industry. Previous research has indicated that intention recognition control plays a crucial role in users' daily use of exoskeletons. OBJECTIVE: This review aims to discuss the characteristics of intention recognition control schemes for intelligent trunk exoskeletons under different control objectives over the past decade. METHODS: Considering the relatively late development of active trunk exoskeletons, we selected papers published in the last decade (2013 to 2023) from the Web of Science, PubMed, and IEEE Xplore databases. In total, 50 articles were selected and examined based on four control objectives. RESULTS: In general, we found that researchers focus on trunk exoskeleton devices designed for assistance and motor augmentation, which rely more on body movement signals as a source for intention recognition. CONCLUSION: Based on these results, we identify and discuss several promising research directions that may help to attain a widely accepted control methods, thereby advancing further development of trunk exoskeleton technology.

Research on multi-dimensional intelligent quantitative assessment of upper limb function based on kinematic parameters.

BACKGROUND: Rehabilitation assessment is a critical component of rehabilitation treatment. OBJECTIVE: This study focuses on a comprehensive analysis of patients' movement performance using the upper limb rehabilitation robot. It quantitatively assessed patients' motor control ability and constructed an intelligent grading model of functional impairments. These findings contribute to a deeper understanding of patients' motor ability and provide valuable insights for personalized rehabilitation interventions. METHODS: Patients at different Brunnstrom stages underwent rehabilitation training using the upper limb rehabilitation robot, and data on the distal movement positions of the patients' upper limbs were collected. A total of 22 assessment metrics related to movement efficiency, smoothness, and accuracy were extracted. The performance of these assessment metrics was measured using the Mann-Whitney U test and Pearson correlation analysis. Due to the issue of imbalanced sample categories, data augmentation was performed using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on weighted sampling, and an intelligent grading model of functional impairment based on the Extreme Gradient Boosting Tree (XGBoost) algorithm was constructed. RESULTS: Sixteen assessment metrics were screened. These metrics were effectively normalized to their maximum values, enabling the derivation of quantitative assessment scores for motor control ability across the three dimensions through a weighted fusion approach. Notably, when applied to the data-enhanced dataset, the intelligent grading model exhibited remarkable improvement, achieving an accuracy rate exceeding 0.98. Moreover, significant enhancements were observed in terms of precision, recall, and f1-score. CONCLUSION: The research findings demonstrate that this study enables the quantitative assessment of patients' motor control ability and intelligent grading of functional impairments, thereby contributing to the efficiency enhancement of clinical rehabilitation assessment. Moreover, this method resolves the issues associated with the subjectivity and prolonged periods of traditional rehabilitation assessment methods.

A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient's training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient's condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

Robot-Assisted Training to Improve Proprioception of Wrist.

In recent years, robot-assisted training has been shown to significantly improve motor function and proprioception in people with functional disabilities, but the efficiency of proprioceptive acuity was unclear. To characterize the efficiency of joint proprioceptive acuity improvement in space, we designed a robot-assisted ipsilateral joint position matching experiment using the wrist as the study object. We conducted 2-way repeated measures ANOVA on error data before and after training in 12 healthy subjects and mapped the distribution of wrist proprioceptive learning ability in different workspaces. The results showed significant differences in the proprioceptive acuity of the wrist joint in different workspaces and movement directions before and after training in 12 subjects ( [Formula: see text]), and the proprioceptive acuity of the wrist after training was significantly higher than before training. In addition, the learning ability of wrist proprioceptive acuity showed significant differences in different workspaces and movement directions (Flexion and Extension in habit workspace (HW) ( P=0.037 ); Flexion and Extension in maximum workspace (MW) ( P=0.016 ); Flexion in HW and MW ( P=0.043 )). Robot-assisted training is beneficial for improving the proprioceptive acuity of the wrist. The learning ability of proprioceptive acuity of joints in different movement directions is independently distributed and influenced by usage habits, which accelerate the improvement of proprioceptive acuity. This research hopes to clinically guide the development of highly effective rehabilitation programs to achieve better recovery and help build patient confidence.

Design and optimization of a new integrated hip and knee prosthesis structure.

BACKGROUND: Conventional hip disarticulation prostheses (HDPs) are passive devices with separate joint structures, limiting amputees' ability to control and resulting in abnormal gait patterns. This study introduces a new HDP integrating the hip and knee joints for amputees' natural gait. METHODS: The new HDP restores the physiological rotation center of the hip with a remote center of motion (RCM) structure, and simulates the knee motion with a four-bar structure. Nonlinear programming was employed to optimize the hip-knee joint structure. A hybrid multi-objective drive structure with a series-parallel connection was also designed to ensure motion synergy between the hip and knee joints. Finally, a prototype of the prosthesis was tested using the HDP test system. RESULTS: The optimization results demonstrate that the new HDP accurately restores the rotation center of the femur in amputees, with the knee's instantaneous center of rotation (ICR) trajectory closely resembling that of the human knee (Pearson correlation coefficient is 0.999). The study shows that the new HDP achieves a motion reproduction accuracy of over 95% for the human hip joint at walking speeds above 1.5 km/h, 38% higher than conventional prosthesis. Similarly, at the same walking speed, the new HDP replicates the motion of the human knee at 82.89%, surpassing conventional prosthesis by 57.85%. CONCLUSIONS: The new HDP restores symmetry and replicates synergistic movement in amputees' lower limbs, exhibiting superior movement characteristics compared to conventional prostheses. This innovative HDP has the potential to enhance the quality of life for amputees.

Across Sessions and Subjects Domain Adaptation for Building Robust Myoelectric Interface.

Gesture interaction via surface electromyography (sEMG) signal is a promising approach for advanced human-computer interaction systems. However, improving the performance of the myoelectric interface is challenging due to the domain shift caused by the signal's inherent variability. To enhance the interface's robustness, we propose a novel adaptive information fusion neural network (AIFNN) framework, which could effectively reduce the effects of multiple scenarios. Specifically, domain adversarial training is established to inhibit the shared network's weights from exploiting domain-specific representation, thus allowing for the extraction of domain-invariant features. Effectively, classification loss, domain diversence loss and domain discrimination loss are employed, which improve classification performance while reduce distribution mismatches between the two domains. To simulate the application of myoelectric interface, experiments were carried out involving three scenarios (intra-session, inter-session and inter-subject scenarios). Ten non-disabled subjects were recruited to perform sixteen gestures for ten consecutive days. The experimental results indicated that the performance of AIFNN was better than two other state-of-the-art transfer learning approaches, namely fine-tuning (FT) and domain adversarial network (DANN). This study demonstrates the capability of AIFNN to maintain robustness over time and generalize across users in practical myoelectric interface implementations. These findings could serve as a foundation for future deployments.

A multi-degree-of-freedom reconfigurable ankle rehabilitation robot with adjustable workspace for post-stroke lower limb ankle rehabilitation.

Introduction: A multi-degree-of-freedom ankle rehabilitation robot with an adjustable workspace has been designed to facilitate ankle joint rehabilitation training. It features a rotation center adapted to the human body, making it suitable for patients with ankle dysfunction following a stroke. Method: In this study, a multi-degree-of-freedom reconfigurable ankle rehabilitation robot (RARR) with adaptable features, based on the principles of ergonomics, has been proposed to cater to the varying needs of patients. This robot offers an adjustable workspace, allowing for different types of ankle joint rehabilitation exercises to be performed. By adjusting the assembly of the RARR, personalized and targeted training can be provided to patients, circumventing issues of redundancy in degrees of freedom during its use. A kinematic model of the robot has been established, and finite element simulation has been employed to analyze the strength of critical components, ensuring the safety of the robot. An experimental platform has been set up to assess the smoothness of the rehabilitation process with RARR, with angle measurements conducted using an Inertial Measurement Unit (IMU). Results and discussion: In conclusion, both simulation and experimental results demonstrate that the robot offers an adjustable workspace and exhibits relatively smooth motion, thereby confirming the safety and effectiveness of the robot. These outcomes align with the intended design goals, facilitating ankle joint rehabilitation and advancing the field of reconfigurable robotics. The RARR boasts a compact structure and portability, making it suitable for various usage scenarios. It is easily deployable for at-home use by patients and holds practical application value for wider adoption in rehabilitation settings.

A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.

Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles' EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human-computer interactions.

A Wearable Computer Vision System With Gimbal Enables Position-, Speed-, and Phase-Independent Terrain Classification for Lower Limb Prostheses.

Computer vision can provide upcoming walking environment information for lower limb-assisted robots, thereby enabling more accurate and robust decisions for high-level control. However, current computer vision systems in lower extremity devices are still constrained by the disruptions that occur in the interaction between human, machine, and the environment, which hinder optimal performance. In this paper, we propose a gimbal-based terrain classification system that can be adapted to different lower limb movements, different walking speeds, and gait phases. We use a linear active disturbance rejection controller to realize fast response and anti-disturbance control of the gimbal, which allows computer vision to continuously and stably focus on the desired field of view angle during lower limb motion interaction. We also deployed a lightweight MobileNetV2 model in an embedded vision module for real-time and highly accurate inference performance. By using the proposed terrain classification system, it can provide the ability to classify and predict terrain independent of mounting position (thighs and shanks), gait phase, and walking speed. This also makes our system applicable to subjects with different physical conditions (e.g., non-disabled subjects and individuals with transfemoral amputation) without tuning the parameters, which will contribute to the plug-and-play functionality of terrain classification. Finally, our approach is promising to improve the adaptability of lower limb assisted robots in complex terrain, allowing the wearer to walk more safely.

Construction of Core-Shell MOF CSMnP with Enzyme-Like Activity for Chemotherapy and Chemodynamic Therapy.

Materials with enzyme-like activity have received a lot of attention in the field of tumor catalytic therapy. Here, biocompatible core-shell MOF CSMnP with two valence states of Mn ion, which could process chemodynamic therapy (CDT), was designed and synthesized. Besides, it could also promote a series of catalytic processes in the tumor microenvironment (TME). CSMnP catalyzed endogenous hydrogen peroxide (H2O2) to oxygen (O2) via catalase-like activity and then combined with the outer layer Mn(II)-PBC to convert O2 into superoxide radicals (•O2-), exhibiting oxidase-like activity. Besides, intracellular glutathione (GSH) could be effectively consumed through the glutathione oxidase-like activity of Mn3+. The occurrence of the cascade reactions effectively amplified the enzymatic production to enhance CDT. Furthermore, the therapeutic effect of CSMnP was improved through the loading of cationic drug DOX. The loading capacity was 11.10 wt %, which was 2.2 times that of Mn(III)-PBC (4.95 wt %), and the release of DOX showed a characteristic response. Therefore, the core-shell MOF with enzyme-like activity had a potential application for tumor combination therapy.

Corrigendum: Design of a SMA-based soft composite structure for wearable rehabilitation gloves.

[This corrects the article DOI: 10.3389/fnbot.2023.1047493.].

Development and future prospects of the artificial urinary sphincter.

BACKGROUND: Urinary incontinence is a urinary disorder in which urine leaks out involuntarily. This disorder seriously affects the quality of life of patients. For patients with mild incontinence, conservative treatment and medication may be the ideal treatment modality, but for patients with severe incontinence, an artificial urinary sphincter is currently a better treatment option. METHODS: In order to design an ideal artificial urinary sphincter, this article first searched and collected literature based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses searched strategy by selecting specific subject terms and reviewed the artificial urinary sphincters that are currently in the research stage based on different activation methods. RESULTS: In response to the deficiencies of the existing artificial urinary sphincter, this article discusses the future optimization of the artificial urinary sphincter from three aspects: individual improvement of the artificial urinary sphincter, engineering design elements, and optimization of the artificial urinary sphincter manufacturing process. CONCLUSIONS: The manufacture of an idealized artificial urinary sphincter capable of meeting clinical needs is of great importance to improve the quality of life of patients. However, this approach is a reasonable option to explore and should not be overestimated until further evidence is available.

Improved YOLOv4-tiny based on attention mechanism for skin detection.

Background: An automatic bathing robot needs to identify the area to be bathed in order to perform visually-guided bathing tasks. Skin detection is the first step. The deep convolutional neural network (CNN)-based object detection algorithm shows excellent robustness to light and environmental changes when performing skin detection. The one-stage object detection algorithm has good real-time performance, and is widely used in practical projects. Methods: In our previous work, we performed skin detection using Faster R-CNN (ResNet50 as backbone), Faster R-CNN (MobileNetV2 as backbone), YOLOv3 (DarkNet53 as backbone), YOLOv4 (CSPDarknet53 as backbone), and CenterNet (Hourglass as backbone), and found that YOLOv4 had the best performance. In this study, we considered the convenience of practical deployment and used the lightweight version of YOLOv4, i.e., YOLOv4-tiny, for skin detection. Additionally, we added three kinds of attention mechanisms to strengthen feature extraction: SE, ECA, and CBAM. We added the attention module to the two feature layers of the backbone output. In the enhanced feature extraction network part, we applied the attention module to the up-sampled features. For full comparison, we used other lightweight methods that use MobileNetV1, MobileNetV2, and MobileNetV3 as the backbone of YOLOv4. We established a comprehensive evaluation index to evaluate the performance of the models that mainly reflected the balance between model size and mAP. Results: The experimental results revealed that the weight file of YOLOv4-tiny without attention mechanisms was reduced to 9.2% of YOLOv4, but the mAP maintained 67.3% of YOLOv4. YOLOv4-tiny's performance improved after combining the CBAM and ECA modules, but the addition of SE deteriorated the performance of YOLOv4-tiny. MobileNetVX_YOLOv4 (X = 1, 2, 3), which used MobileNetV1, MobileNetV2, and MobileNetV3 as the backbone of YOLOv4, showed higher mAP than YOLOv4-tiny series (including YOLOv4-tiny and three improved YOLOv4-tiny based on the attention mechanism) but had a larger weight file. The network performance was evaluated using the comprehensive evaluation index. The model, which integrates the CBAM attention mechanism and YOLOv4-tiny, achieved a good balance between model size and detection accuracy.

Identifying the changes in the cortical activity of various brain regions for different balance tasks: A review.

BACKGROUND: Balance support is critical to a person's overall function and health. Previous neuroimaging studies have shown that cortical structures play an essential role in postural control. OBJECTIVE: This review aims to identify differences in the pattern of neural activity induced by balance tasks with different balance control requirements. METHODS: Seventy-four articles were selected from the field of balance training and were examined based on four brain function detection technologies. RESULTS: In general, most studies focused on the activity changes of various cortical areas during training at different difficulty levels, but more and more attention has also begun to focus on the functional changes of other cortical and deep subcortical structures. Our analysis also revealed the neglect of certain task types. CONCLUSION: Based on these results, we identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different tasks.

Current development and clinical applications of artificial anal sphincter.

INTRODUCTION: Fecal incontinence is a common chronic disease, which not only brings inconvenience to the lives of patients, but also causes great psychological harm to patients. Artificial anal sphincter is an innovative method that may treat fecal incontinence, and now has been clinically applied. AREAS COVERED: This article will review recent developments in mechanisms and clinical applications of artificial anal sphincter. The current results of clinical trials show that the implantation of artificial sphincter will cause morphological changes of surrounding tissues, and related biomechanical imbalance will lead to the loss of effectiveness of the device and various complications. In terms of safety, postoperative patients suffer from various complications such as infection, corrosion, tissue ischemia, mechanical failure, and difficulty in emptying. In terms of effectiveness, there is currently no long-term research data to prove that the implanted device can maintain a long-term functional state. EXPERT OPINION: The key issue for the safety and effectiveness of implantable devices is the biomechanical compatibility of implantable devices was proposed. Based on the superelasticity of shape memory alloy, this article proposes a new type of constant force artificial sphincter device, which provides a new direction for solving the clinical application of artificial anal sphincter.

Concept design of hybrid-actuated lower limb exoskeleton to reduce the metabolic cost of walking with heavy loads.

This paper proposes the conceptual design method for a hybrid-actuated lower limb exoskeleton based on energy consumption simulation. Firstly, the human-machine coupling model is established in OpenSim based on the proposed three passive assistance schemes. On this basis, the method of simulating muscle driving is used to find out the scheme that can reduce the metabolic rate the most with 3 passive springs models. Then, an active-passive cooperative control strategy is designed based on the finite state machine to coordinate the operation of the power mechanism and the passive energy storage structure and improve the mobility of the wearer. In the end, a simulation experiment based on the human-machine coupled model with the addition of active actuation is proceeded to evaluate its assistance performance according to reducing metabolic rate. The results show that the average metabolic cost decreased by 7.2% with both spring and motor. The combination of passive energy storage structures with active actuators to help the wearer overcome the additional consumption of energy storage can further reduce the body's metabolic rate. The proposed conceptual design method can also be utilized to implement the rapid design of a hybrid-actuated lower limb exoskeleton.

Magnetically controlled artificial urinary sphincter: An overview from existing devices to future developments.

BACKGROUND: Urinary incontinence is a common clinical problem in the world today. Artificial urinary sphincter is a good treatment approach for severe urinary incontinence, which is designed to mimic the action of the human urinary sphincter and assist patients to regain urinary function. METHODS: There are many control methods based on artificial urinary sphincter, such as hydraulic control, electromechanical control, magnetic control, and shape memory alloy control. In this paper, the literature was first searched and documented based on PRISMA search strategy for selected specific subject terms. Then, a comparison of artificial urethral sphincters based on different control methods was conducted, and the research progress of magnetically controlled artificial urethral sphincters was reviewed, and their advantages and disadvantages were summarized. Finally, the design factors for the clinical application of magnetically controlled artificial urinary sphincter are discussed. RESULTS: As magnetic control allows for non-contact force transfer and does not generate heat, it is proposed that magnetic control may be one of the more promising control methods. The design of future magnetically controlled artificial urinary sphincters will need a variety of considerations, including the structural design of the device, manufacturing materials, manufacturing costs, and convenience. In addition, validation of the safety and effectiveness of the device and device management are equally important. CONCLUSIONS: The design of an ideal magnetically controlled artificial urinary sphincter is of great importance to enhance patient treatment outcomes. However, there are still great challenges to be faced for the clinical application of such devices.

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration-dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.