Lower Limb Movement Research Articles

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.

Giant Thoracolumbosacral Myelomeningocele: A Case Report

AbstractNeural tube defects are a broad spectrum of congenital developmental anomalies that include cranial defects and open and closed spinal dysraphism. Myelomeningocele is a common form of spinal dysraphism most commonly seen in the lumbosacral region. Giant myelomeningoceles are rare occurrences. We hereby present the case of a 7-month-old infant with a history of large swelling over the thoracic lumbar region (circumference of 42 cm and diameter of 12.5 cm) that was progressively increasing in size with occasional cerebrospinal fluid (CSF) leakage from the swelling. There was associated progressive increasing size of the head size circumference (51 cm) as well for the past 6 months. There was no movement of both lower limbs since birth without any sensation up to the groin. On evaluation, the infant was found to have gross hydrocephalous. The Evans index was 0.4 with Chiari 2 malformation and a giant thoracolumbosacral myelomeningocele. The patient underwent a low-pressure ventriculoperitoneal (VP) shunt on the right side, followed by the repair of the giant myelomeningocele. The post-op period was uneventful with no CSF leakage or skin necrosis. The infant was discharged on day 5. Giant myelomeningoceles are quite a challenge for neurosurgeons due to the high risk of skin edge necrosis, CSF leaks, meningoencephalitis, etc. Here we managed our case with utmost intraoperative surgical skills and postoperative management due to which our patient was discharged in a satisfactory condition without any complication. This case is supposed to be largest myelomeningocele in the world literature to date.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction.

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Relationship between exacerbating patellofemoral pain and dynamic knee valgus in females with patellofemoral pain after a patellofemoral joint loading protocol: A cross-sectional

ObjectiveThe aim of the present study is to 1) the effect of exacerbating patellofemoral pain (PFP) on dynamic knee valgus (DKV) 2) the relationship between exacerbating PFP and DKV in females with patellofemoral pain. DesignCross-sectional study. MethodsSixty women with PFP were recruited from the orthopedic clinics. We evaluated pain intensity and DKV in two conditions without (condition 1) and with (condition 2) PFJ loading during the single-leg squat (SLS) task. The MANOVA test was used to compare pain intensity and DKV angle between the two conditions. Furthermore, the Pearson correlation was used to examine the correlation between pain intensity with DKV angle. ResultsThe MANOVA analysis showed that pain intensity (P < 0.000, η2 = 0.623) and DKV angle (P < 0.000, η2 = 0.544) with a medium effect size significantly increased after PFJ loading. Furthermore, an excellent positive correlation was observed between an increase in pain intensity and DKV angle (P < 0.000, r = 0.840). ConclusionIt seems that after the PFJ loading protocol and the subsequent pain exacerbation, the neuromuscular biomechanics of the lower limb are deficits. As a result, the stabilizing muscles activation decreases and increases the lower limb movement in the frontal plane.

Deep ensemble learning approach for lower limb movement recognition from multichannel sEMG signals

Deep ensemble learning approach for lower limb movement recognition from multichannel sEMG signals

Research on a new cable-driven lower limb rehabilitation robot with bilateral coordination control

In this paper, a new type of cable-driven lower limb rehabilitation robot (CDLLRR) based on bilateral control is proposed. Compared with the traditional rehabilitation robot, it has multiple cooperative remote rehabilitation modes of patient-therapist interaction with better safety, mechanical compliance and adaptability. Firstly, according to the characteristics of gait movement of human lower limbs and the performance requirements of the robot in therapy and rehabilitation, the overall structure, main components and assembly arrangement of the robot are designed in detail. Secondly, in order to ensure that the rehabilitation robot has excellent force transparency and system stability while still maintaining a certain position tracking, according to the performance measurement index based on the introduced bilateral control strategy, this paper proposes an improved PD controller based on Anderson method (IPDAM). Finally, the feasibility of the proposed CDLLRR and the effectiveness of the IPDAM are verified by simulation experiments.

On the Relation Between Leg Motion Rate and Speech Tempo During Submaximal Cycling Exercise.

This study investigated whether temporal coupling was present between lower limb motion rate and different speech tempi during different exercise intensities. We hypothesized that increased physical workload would increase cycling rate and that this could account for previous findings of increased speech tempo during exercise. We also investigated whether the choice of speech task (read vs. spontaneous speech) affected results. Forty-eight women who were ages 18-35 years participated. A within-participant design was used with fixed-order physical workload and counterbalanced speech task conditions. Motion capture and acoustic data were collected during exercise and at rest. Speech tempo was assessed using the amplitude envelope and two derived intrinsic mode functions that approximated syllable-like and footlike oscillations in the speech signal. Analyses were conducted with linear mixed-effects models. No direct entrainment between leg cycling rate and speech rate was observed. Leg cycling rate significantly increased from low to moderate workload for both speech tasks. All measures of speech tempo decreased when participants changed from rest to either low or moderate workload. Speech tempo does not show temporal coupling with the rate of self-generated leg motion at group level, which highlights the need to investigate potential faster scale momentary coupling. The unexpected finding that speech tempo decreases with increased physical workload may be explained by multiple mental and physical factors that are more diverse and individual than anticipated. The implication for real-world contexts is that even light physical activity-functionally equivalent to walking-may impact speech tempo.