Lower Limb Movement Research Articles

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively.

Children With Cerebral Palsy's Experiences With Adaptive Climbing: A Qualitative Study on Parents' Perspectives.

Interest in adaptive sports for children with cerebral palsy (CP) is growing, but current evidence on the benefits and indications for one sport, adaptive climbing, is limited. We sought to describe perceived changes observed by parents of children with CP who participated in adaptive climbing. Parents whose children with CP participated in 5 or more adaptive climbing sessions were eligible to participate and were emailed a recruitment letter and flyer. Data were collected through semi-structured interviews, using a moderator guide. Interviews were transcribed and content analyzed, with data grouped into concepts, categories, and themes until data saturation. Ten parents (9 mothers, 1 father) of 10 children with CP (5 girls, 5 boys; ages 7 to 19 years) were interviewed for 15 to 45 minutes each, yielding 4 themes. First, parents perceived that adaptive climbing challenged the children physically (in reach, balance, strength, and head/neck and lower limb motion); second, that it sharpened children's cognitive skills (in focus, problem-solving, and strategic thinking); third, that it raised children's confidence (socially, physically, and emotionally); and fourth, that it expanded children's sense of what they could do (in mastering a challenge, claiming an athletic identity, and participating in a sport like their peers). In this qualitative study, parents described physical, cognitive, and psychosocial benefits of adaptive climbing for their children with CP. These descriptions can be used to inform future studies of children with CP who participate in adaptive climbing.

Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning

Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning

Biomimetic Peripheral Nerve Stimulation Promotes the Rat Hindlimb Motion Modulation in Stepping: An Experimental Analysis.

Peripheral nerve stimulation is an effective neuromodulation method in patients with lower extremity movement disorders caused by stroke, spinal cord injury, or other diseases. However, most current studies on rehabilitation using sciatic nerve stimulation focus solely on ankle motor regulation through stimulation of common peroneal and tibial nerves. Using the electrical nerve stimulation method, we here achieved muscle control via different sciatic nerve branches to facilitate the regulation of lower limb movements during stepping and standing. A map of relationships between muscles and nerve segments was established to artificially activate specific nerve fibers with the biomimetic stimulation waveform. Then, characteristic curves depicting the relationship between neural electrical stimulation intensity and joint control were established. Finally, by testing the selected stimulation parameters in anesthetized rats, we confirmed that single-cathode extraneural electrical stimulation could activate combined movements to promote lower limb movements. Thus, this method is effective and reliable for use in treatment for improving and rehabilitating lower limb motor dysfunction.

FNIRS based study of brain network characteristics in children with cerebral palsy during bilateral lower limb movement.

Motor dysfunctions in children with cerebral palsy (CP) are caused by nonprogressive brain damage. Understanding the functional characteristics of the brain is important for rehabilitation. This paper aimed to study the brain networks of children with CP during bilateral lower limb movement using functional near-infrared spectroscopy (fNIRS) and to explore effective fNIRS indices for reflecting functional brain activity. Using fNIRS, cerebral oxygenation signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) were recorded from fifteen children with spastic CP and seventeen children with typical development (CTDs) in the resting state and during bilateral lower limb movement. Functional connectivity matrices based on phase-locking values (PLVs) were calculated using Hilbert transformation, and binary networks were constructed at different sparsity levels. Network metrics such as the clustering coefficient, global efficiency, local efficiency, and transitivity were calculated. Furthermore, the time-varying curves of network metrics during movement were obtained by dividing the time window and using sparse inverse covariance matrices. Finally, conditional Granger causality (GC) was used to explore the causal relationships between different brain regions. Compared to CTDs, the connectivity between RMC-RPFC (p=0.017) and RMC-LMC (p=0.002) in the brain network was decreased in children with CP, and the clustering coefficient (p=0.003), global efficiency (p=0.034), local efficiency (p=0.015), and transitivity (p=0.009) were significantly lower. The standard deviation of the changes in global efficiency of children with CP during motion was also greater than that of CTDs. Using GC, it was found that there was a significant increase in causal strength from the RMC to the RPFC (p=0.04) and from the RMC to the LMC (p=0.042) in children with CP during motion. Additionally, there were significant negative correlations between the PLV of LMC-RMC (p=0.002) and the Gross Motor Function Classification System (GMFCS) and between the GMFCS and the clustering coefficient (p=0.01). During rehabilitation training of the lower limbs, there were significant differences in brain network indices between children with CP and CTDs. The indicators proposed in this paper are effective at evaluating motor function and the real-time impact of rehabilitation training on the brain network and have great potential for application in guiding clinical motor function assessment and planning rehabilitation strategies.

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System.

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.

Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke.

Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: -0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; -0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.

Wearable Piezoelectric Sensors Based on BaTiO3 Films for Sarcopenia Recognition

AbstractSarcopenia recognition is very crucial in the early diagnosis of sarcopenia. However, the commonly used screening methods are limited by real‐time property, portability, and convenient usability at home. Herein, an electrospun BaTiO3 film is proposed and a piezoelectric sensor with silver electrodes and polyimide substrates is fabricated. The sensor exhibits high piezoelectricity (74.2 pC N−1), sensitivity, linearity, low detection limit (0.2 mN), and significant bending ability (bending angle can exceed 90°), maintaining stable output after more than 20 000 cycles during a week. Due to its excellent performance, the piezoelectric sensor to the recognition of sarcopenia is applied and a wearable system to collect piezoelectric signals from the lower limb movements of the elderly is developed. By selecting features from these signals, eight kinds of machine learning models are employed and their performances in recognizing sarcopenia are compared in both male and female groups. The results indicate that the artificial neural network (ANN) model has the highest performance, with accuracies of 92.9% in males and 98.1% in females. This piezoelectric sensor, combined with a wireless communication module, is expected to provide crucial evidence for sarcopenia detection, offering a new, convenient, and household screening solution for early diagnosis and prevention of sarcopenia.

Reduction in limb-movement complexity at term-equivalent age is associated with motor developmental delay in very-preterm or very-low-birth-weight infants

Reduced complexity during the writhing period can be crucial in the spontaneous movements of high-risk infants for neurologic impairment. This study aimed to verify the association between quantified complexity of upper and lower-limb movements at term-equivalent age and motor development in very-preterm or very-low-birth-weight infants. Video images of spontaneous movements at term-equivalent age were collected from very-preterm or very-low-birth-weight infants. A pretrained pose-estimation model and sample entropy (SE) quantified the complexity of the upper- and lower-limb movements. Motor development was evaluated at 9 months of corrected age using Bayley Scales of Infant and Toddler Development, Third Edition. The SE measures were compared between infants with and without motor developmental delay (MDD). Among 90 infants, 11 exhibited MDD. SE measures at most of the upper and lower limbs were significantly reduced in infants with MDD compared to those without MDD (p < 0.05). Composite scores in the motor domain showed significant positive correlations with SE measures at most upper and lower limbs (p < 0.05). The results show that limb-movement complexity at term-equivalent age is reduced in infants with MDD at 9 months of corrected age. SE of limb movements can be a potentially useful kinematic parameter to detect high-risk infants for MDD.

Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System.

Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user's intention to move or muscle fatigue. These issues impede the user's ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status.

The importance of core strength for change of direction speed.

Change of direction speed (CODS) is determined by several physical aspects, such as linear sprint speed, reactive strength and power of leg muscles. It appears that core strength may also play a role in CODS, however, its relationship to CODS remains unclear. The aim of this narrative review was to analyze the literature addressing a) the relationship between core strength and CODS and b) the effect of core strength training on CODS. This analysis revealed a significant relationship between the parameters of core strength and stability (the pressure of the activated core muscles during lower limb movement and the greatest mean force output of maximum volunteered contraction) and the time in the Agility T-Test. However, this parameter was not significantly related to the strength endurance of core muscles (total time in the plank test). Core training provides a sufficient stimulus for the development of CODS in less-skilled middle-adolescent athletes, while its effectiveness decreases in higher-skilled adult athletes. These findings indicate that core muscle strength contributes significantly to the change of direction speed. Core training is therefore useful for improving CODS.

Kinematics Mathematical Modelling of Lower Limb Exoskeleton for Paralyzed Stroke Patients

This paper presents the development of a lower limb rehabilitation robot to be used with bedridden patients. Strokes are one of the significant causes of death in 17% of the 109,155 medically certified death in 2020 in Malaysia. In most cases, stroke paralysis affects the opposite side of the damaged brain, and any part of the body can be affected. 90% of stroke patients get paralysis to some degree. Patients can recover from the disease and restore body motions by undergoing paralysis stroke physiotherapy, which involves numerous sessions with patients. There were several successful robotic rehabilitations in recent years; however, their design is inflexible and large, requiring the patient to sit or stand in a static position. This project will be built on a motor-driven parallel architecture that will offer motion assistance throughout the human’s wide range of motion (ROM). This project development is divided into two parts: structure design and simulation. The design process for the lower limb devices used syncretization and mathematical analysis. The structure design is from the kinematic analysis. The mathematical models are then used to design in MATLAB simulation which is trajectory simulation. The outcome shows that the simulations that have been developed is compatible with the motion of human lower limb. This robot develops for bedridden use of lower limb rehabilitation exercises.

An LMI-based robust state-feedback controller design for the position control of a knee rehabilitation exoskeleton robot: Comparative analysis

Rehabilitation exoskeleton robots play a crucial role in restoring functional lower limb movements for individuals with locomotor disorders. Numerous research studies have concentrated on adapting the control of these rehabilitation robotic systems. In this study, we investigate an affine state-feedback control law for robust position control of a knee exoskeleton robot, taking into account its nonlinear dynamic model that includes solid and viscous frictions. To ensure robust stabilization, we employ the Lyapunov approach and propose three methods to establish stability conditions using the Schur complement, the Young inequality, the matrix inversion lemma, and the S-procedure lemma. These conditions are formulated as Linear Matrix Inequalities (LMIs). Furthermore, we conduct a comprehensive comparison among these methods to determine the most efficient approach. At the end of this work, we present simulation results to validate the developed LMI conditions and demonstrate the effectiveness of the adopted control law in achieving robust position control of the knee exoskeleton robot.

3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.

Introduction: Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist. Machine learning (ML) models can mitigate these issues in adults, prompting an investigation into their applicability to a heterogeneous pediatric population. This study aimed at 1) quantifying personalized and generalized ML models' performance for predicting gait time series in typically developed (TD) children using IMUs data, 2) Comparing random forest (RF) and convolutional neural networks (CNN) models' performance, 3) Finding the optimal number of IMUs required for accurate predictions. Methodology: Seventeen TD children, aged 6 to 15, participated in data collection involving OMC, force plates, and IMU sensors. Joint kinematics and kinetics (targets) were computed from OMC and force plates' data using OpenSim. Tsfresh, a Python package, extracted features from raw IMU data. Each target's ten most important features were input in the development of personalized and generalized RF and CNN models. This procedure was initially conducted with 7 IMUs placed on all lower limb segments and then performed using only two IMUs on the feet. Results: Findings suggested that the RF and CNN models demonstrated comparable performance. RF predicted joint kinematics with a 9.5% and 19.9% NRMSE for personalized and generalized models, respectively, and joint kinetics with an NRMSE of 10.7% for personalized and 15.2% for generalized models in TD children. Personalized models provided accurate estimations from IMU data in children, while generalized models lacked accuracy due to the limited dataset. Furthermore, reducing the number of IMUs from 7 to 2 did not affect the results, and the performance remained consistent. Discussion: This study proposed a promising personalized approach for gait time series prediction in children, involving an RF model and two IMUs on the feet.

Activation of a Rhythmic Lower Limb Movement Pattern during the Use of a Multimodal Brain-Computer Interface: A Case Study of a Clinically Complete Spinal Cord Injury.

Brain-computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.

Unilateral movement decoding of upper and lower limbs using magnetoencephalography

Problem: The effectiveness of distinguishing unilateral lower limb motor execution (ME) or imagery (MI) based on electroencephalogram (EEG) is limited by its low spatial resolution, since the somatotopies of bilateral lower limbs are close to each other. Aim: This research focused on differentiating unilateral lower limb tasks using magnetoencephalography (MEG) signals. Methods: MEG signals were recorded during unilateral upper and lower limb movements. Channel selection and extraction of band power features were performed at both the conventional sensor level and the proposed source level, reconstructed using beamforming. The classification performance at both levels was compared. Results: The theta band changes exhibited the most significant differences and lateralization across tasks. Task-related features were more prominent at the source than at the sensor level. The classification results indicate that all methods achieved an average accuracy of over 98.33% in classifying the upper limb task. For the classification of lower limb tasks, the average accuracies achieved at the source level (96.97% and 95.86%) were significantly higher than those obtained at the sensor level (90.00% and 92.57%). Conclusion and Significance: Our results demonstrated the feasibility of MEG in classifying unilateral lower limb movements, providing a potentially effective method for brain-computer interfaces (BCIs) to enhance control commands. Furthermore, signal processing at the source level can effectively enhance inter-task differences to achieve higher classification performance. Meanwhile, the theta band is highly effective in movement classification and may play an important role in motor function.

A small-sample time-series signal augmentation and analysis method for quantitative assessment of bradykinesia in Parkinson's disease

Patients with Parkinson's disease (PD) usually have varying degrees of bradykinesia, and the current clinical assessment is mainly based on the Movement Disorder Society Unified PD Rating Scale, which can hardly meet the needs of objectivity and accuracy. Therefore, this paper proposed a small-sample time series classification method (DTW-TapNet) based on dynamic time warping (DTW) data augmentation and attentional prototype network. Firstly, for the problem of small sample sizes of clinical data, a DTW-based data merge method is used to achieve data augmentation. Then, the time series are dimensionally reorganized using random grouping, and convolutional operations are performed to learn features from multivariate time series. Further, attention mechanism and prototype learning are introduced to optimize the distance of the class prototype to which each time series belongs to train a low-dimensional feature representation of the time series, thus reducing the dependency on data volume. Clinical experiments were conducted to collect motion capture data of upper and lower limb movements from 36 patients with PD and eight healthy controls. For the upper limb movement data, the proposed method improved the classification accuracy, weighted precision, and kappa coefficient by 8.89%-15.56%, 9.22%-16.37%, and 0.13-0.23, respectively, compared with support vector machines, long short-term memory, and convolutional prototype network. For the lower limb movement data, the proposed method improved the classification accuracy, weighted precision, and kappa coefficient by 8.16%-20.41%, 10.01%-23.73%, and 0.12-0.28, respectively. The experiments and results show that the proposed method can objectively and accurately assess upper and lower limb bradykinesia in PD.

Can Robotic Gait Training with End Effectors Improve Lower-Limb Functions in Patients Affected by Multiple Sclerosis? Results from a Retrospective Case-Control Study.

Background: Multiple sclerosis (MS) is characterized as a neurodegenerative condition possibly triggered by autoimmune mechanisms, impacting the entire central nervous system. In this context, neurorehabilitation plays a crucial role in every phase of the disease, aiming to restore and preserve motor functions in MS patients. In particular, robotic gait training (RGT) allows intensive, repetitive, and task-oriented training, which is pivotal in boosting neuroplastic processes. Thus, the primary aim of our study is to evaluate the effectiveness of innovative robotic gait training, using the G-EO system, on gait, functional abilities, and quality of life (QoL) in patients affected by MS. Secondly, we evaluated the effect of the robotic rehabilitation on lower-limb motor functioning, balance, sensation, and joint functioning. Methods: The study involved twenty MS patients, divided into two groups with comparable medical characteristics and rehabilitation training duration. The experimental group (EG) underwent robotic gait training with the G-EO system (n. 10), while the control group (CG) received traditional rehabilitation training (n. 10). Results: Both groups exhibited improvements in disability level (Functional Independence Measure), 10 m walking distance (10MWT), gait, and balance performance (Functional Ambulation Classification, Tinetti Scale). However, the EG demonstrated a more significant improvement. The G-EO system notably reduced spasticity in the lower limbs (Modified Ashworth Scale) exclusively in the EG. Discussion: This study suggests that the G-EO system could be a valuable tool for enhancing gait functions, including lower-limb movements, functional abilities, and QoL in individuals with MS.

Spinal cord electrical stimulation with neurophysiological monitoring for treatment of high-risk diabetic foot

To evaluate the clinical efficacy of a single-session implantation of spinal cord electrical stimulation with neurophysiological monitoring a spinal cord electrical stimulator under general anesthesia with neurophysiological monitoring for the treatment of high-risk diabetic foot. The clinical data of seven patients with high-risk diabetic foot who underwent spinal cord electrical stimulation in neurosurgery ward nine of Tianjin Huanhu Hospital from May 2022 to May 2023 were collected. The operation was performed under general anesthesia with the "C" arm X ray machine guidance and neurophysiological monitoring. The arterial diameter and peak flow rate of lower extremity, lower extremity skin temperature (calf skin temperature, foot skin temperature), visual analog scale (VAS), continuous distance of movement, blood glucose level and toe wound were compared between patients before and after surgery. A total of seven patients with high-risk diabetic foot were included. The diameters and peak flow rates of femoral artery, popliteal artery, anterior tibial artery, posterior tibial artery and dorsal foot artery in both lower limbs were significantly improved after surgery. All patients had different degrees of lower limb pain before operation. After operation, VAS score decreased significantly (1.1±0.9 vs. 6.8±3.4), the pain was significantly relieved, and the calf skin temperature and foot skin temperature were significantly higher than those before surgery [calf skin temperature (centigrade): 33.3±0.9 vs. 30.9±0.7, foot skin temperature (centigrade): 31.4±0.8 vs. 29.1±0.6], fasting blood glucose and postprandial blood glucose were significantly lower than those before surgery [fasting blood glucose (mmol/L): 7.6±1.4 vs. 10.5±1.2, postprandial blood glucose (mmol/L): 9.3±2.3 vs. 13.5±1.1], the differences were statistically significant (all P < 0.01). The lower limb movement of all seven patients was significantly improved after surgery, including one patient who needed wheelchair travel before surgery, and one patient who had intermittent claudication before surgery. Among them, one patient needed wheelchair travel and one patient had intermittent claudication before surgery. All patients could walk normally at 2 weeks after operation. Among the seven patients, two patients had the diabetic foot wound ulceration before surgery, which could not heal for a long time. One month after surgery, blood flow around the foot wound recovered and the healing was accelerated. The wound was dry and crusted around the wound, and the wound healed well. For diabetic high-risk foot patients who are intolerant to diabetic peripheral neuralgia and local anesthesia spinal cord electrical stimulation test, one-time implantation of spinal cord electrical stimulator under general anesthesia under neurophysiological monitoring can effectively alleviate peripheral neuralgia and other diabetic foot related symptoms, improve lower limb blood supply, and reduce the risk of toe amputation. Clinical practice has proved the effectiveness of this technique, especially for the early treatment of diabetic high-risk foot patients.

Impact of variations in swimming velocity on wake flow dynamics in human underwater undulatory swimming

Increasing the velocity of the lower-limb movement is crucial for improving underwater undulatory swimming (UUS) velocity. However, the underlying mechanism of how these movements influence swimming velocity have remained unclear. This study aimed to clarify the relationship between changes in swimming movement and the resulting changes in flow field as a result of changes in test flow velocity (U) in a water flume. A male student swimmer was tested with the following three U settings 0.8, 1.0 and 1.2 m/s. The lower-limb movements and wake flow behind the swimmer were compared. A motion capture system was employed for motion analysis, and a stereo PIV for visualizing the flow field. The findings revealed that, as U increased, the velocity vectors of the flow field in all directions (u, v, w) increased, as did the toe velocity. It was also suggested that with increasing U, the outward change in the toe velocity vector down-kick and the inward change in the toe velocity vector up-kick may have a positive effect on the vortices, contributing to an increase in the velocity vectors in the flow field. Furthermore, the high U, vortex re-capturing occurred during the transition from down-kick to up-kick, indicating that this might contribute to increased momentum. This suggests that the transition from the down-kick to the up-kick is necessary for gaining greater momentum. Notably, this study is the first to identify the factors that increase the swimming velocity of the UUS in the context of movement and flow field.