Lower Limb Movement Research Articles

Progress and development trends of lower limb exoskeleton rehabilitation robots

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.

Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.

To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining these advanced techniques, significant improvements in movement classification were achieved. Firstly, sEMG data were collected from 20 subjects as they performed four distinct gait movements: walking upstairs, walking downstairs, walking on a level surface, and squatting. Subsequently, the gathered sEMG data underwent preprocessing, with features extracted from both the time domain and frequency domain. These features were then used as inputs for the machine learning recognition model. Finally, based on the preprocessed sEMG data, the CNN-TL lower limb action recognition model was constructed. The performance of CNN-TL was then compared with that of the CNN, LSTM, and SVM models. The results demonstrated that the accuracy of the CNN-TL model in lower limb action recognition was 3.76%, 5.92%, and 14.92% higher than that of the CNN-LSTM, CNN, and SVM models, respectively, thereby proving its superior classification performance. An effective scheme for improving lower limb motor function in rehabilitation and assistance devices was thus provided.

Effect of Soft Knee Brace on Shank Movement in Running

Background: Soft knee braces are used for the protection and treatment of knee injuries in light-intensity activities of daily life. Soft knee braces have several functions, such as stabilizing and supporting the lower limb posture. However, previous studies did not investigate the effects of a soft knee brace on the lower limbs during vigorous-intensity activities. Aims: The objective of this study was to investigate the effect of a soft knee brace on the physical load and stability of the lower limb during running as a vigorous-intensity activity. Methods: Participants were asked to run with or without a soft knee brace, and lower-limb movements during running were measured using an inertial sensor on the shank. Results: The result showed that soft knee brace significantly reduced the magnitude and mediolateral standard deviation of shank acceleration. Conclusion: The results of this study indicate the possibility that wearing a soft knee brace can improve the physical load and stability of the lower limbs during running.

Comparison of the OpenPose system and the reference optoelectronic system for gait analysis of lower-limb angular parameters in children.

Quantitative Gait Analysis (QGA) is the gold-standard for detailed study of lower-limb movement, angles and forces, especially in pediatrics, providing a decision aid for treatment and for assessment of results. However, widespread use of QGA is hindered by the need for specific equipment and trained personnel and high costs. Recently, the OpenPose system used algorithms for 2D video movement analysis, to determine joint points and angles without any supplementary equipment or great expertise. The present study therefore aimed to validate application of OpenPose for gait analysis in children with locomotor pathology, thereby circumventing the limitations of QGA. The OpenPose system is as precise as QGA for measuring lower-limb angles in gait in children. Gait analysis was studied prospectively, between January and July 2023, in 20 children: 13 boys, 7 girls; mean age, 13 years. There was no selection for pathology or use of walking aids. QGA was performed, measuring joint angles in the hips, knees and ankles. The same measurements were then made using the points obtained on OpenPose. The Mann-Whitney test was used to compare the two methods. There were only slight differences in angle measurements (in degrees) for the knees: right, 0.54 [-0.61 ; 1.71], p = 0.361; left, -1.09 [-2.16 ; 0.01], p = 0.051. Differences were greater for the hips (right, 9.32 [8.28 ; 10.35]; left, 7.54 [6.55 ; 8.54], p < 0.01) and ankles (right, -6.67 [-7.22 ; -6.12]; left, -7.07 [-7.60 ; -6.54], p < 0.01). OpenPose provided angle values close to those of QGA for the knees in the sagittal plane, independently of pathology and walking aid. In the hips and ankles, on the other hand, differences were too great to allow clinical application of OpenPose. IV.

Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs.

Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain-computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time-frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal-occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke.

A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.

Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.

Human gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user's natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG. This study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle. The results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19. This study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

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. 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. Impact Statement The evaluation of robot-assisted lower limb rehabilitation based on brain function detection technology can assess the neurological changes of patients in the rehabilitation process by monitoring brain activities and can also provide more accurate guidance for robot-assisted lower limb rehabilitation. By monitoring the patient's brain activity, the robot can adjust according to the real-time status of the patient to achieve more effective rehabilitation training. This has potential impact on improving the rehabilitation effect and speeding up the rehabilitation process of patients.

Lower Limb Motion Contributions To The Turnout During A Bipedal Jump In Pre-professional Ballet Dancers

Lower Limb Motion Contributions To The Turnout During A Bipedal Jump In Pre-professional Ballet Dancers

Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait.

Pathological gait in patients with Hakim's disease (HD, synonymous with idiopathic normal-pressure hydrocephalus; iNPH), Parkinson's disease (PD), and cervical myelopathy (CM) has been subjectively evaluated in this study. We quantified the characteristics of upper and lower limb movements in patients with pathological gait. We analyzed 1491 measurements of 1 m diameter circular walking from 122, 12, and 93 patients with HD, PD, and CM, respectively, and 200 healthy volunteers using the Three-Dimensional Pose Tracker for Gait Test. Upper and lower limb movements of 2D coordinates projected onto body axis sections were derived from estimated 3D relative coordinates. The hip and knee joint angle ranges on the sagittal plane were significantly smaller in the following order: healthy > CM > PD > HD, whereas the shoulder and elbow joint angle ranges were significantly smaller, as follows: healthy > CM > HD > PD. The outward shift of the leg on the axial plane was significantly greater, as follows: healthy < CM < PD < HD, whereas the outward shift of the upper limb followed the order of healthy > CM > HD > PD. The strongest correlation between the upper and lower limb movements was identified in the angle ranges of the hip and elbow joints on the sagittal plane. The lower and upper limb movements during circular walking were correlated. Patients with HD and PD exhibited reduced back-and-forth swings of the upper and lower limbs.

Knee joint position sense and kinematic control in relation to motor competency in 13 to 14-year-old adolescents

BackgroundMotor competence (MC) is a key component reflecting one’s ability to execute motor tasks and is an important predictor of physical fitness. For adolescents, understanding the factors affecting MC is pertinent to their development of more sophisticated sporting skills. Previous studies considered the influence of poor proprioceptive ability on MC, however, the relationship between lower limb joint position sense, kinematic control, and MC is not well understood. Therefore, the aim of this study was to determine the relation between joint position sense and kinematic control with MC in adolescents during a lower limb movement reproduction task.MethodsThis study was a cross-sectional design. Young people (n = 427, 196 girls and 231 boys) aged 13 to 14 years were recruited. A movement reproduction task was used to assess joint position sense and kinematic control, while the Movement Assessment Battery for Children (mABC-2) was used to assess MC. In this study, participants were categorized into the Typically Developed (TD, n = 231) and Probable Developmental Coordination Disorder (DCD, n = 80) groups for further analysis of joint position sense, kinematic control, and MC between groups.ResultsKinematic data, specifically normalized jerk, showed a significant correlation with MC. There was no correlation between knee joint position sense and MC, and no group differences between DCD and TD were found.ConclusionsJoint position sense should not be used as a measure to distinguish TD and DCD. Rather than joint position sense, control of kinematic movement has a greater influence on the coordination of the lower limbs in adolescents. Movement control training should be implemented in the clinical setting to target kinematic control, rather than focus on joint position sense practice, to improve motor competency.Trial Registration IdentifierNCT03150784. Registered 12 May 2017, https://clinicaltrials.gov/study/NCT03150784.

Lower-limb inter-joint coordination during balance recovery after trips

BackgroundTrips are one of the most common external perturbations that can lead to accidental falls. Knowledge about postural control attributes of balance recovery after trips could help reveal the biomechanical causes for trip-induced falls and provide implications for fall prevention interventions. Research questionThe objective of the present study was to examine coordinated lower-limb movements during balance recovery after trips. MethodsOne hundred and twenty-three volunteers participated in an experimental study. They were tripped unexpectedly by a metal pole when walking on a linear walkway at their self-selected speed. Lower-limb inter-joint coordination quantified by continuous relative phase measures, including the mean of the absolute relative phases (MARP) and the deviation phase (DP), was analyzed during the execution of the first recovery step after unexpected trips. ResultsCompared to unsuccessful balance recovery, smaller MARPknee-ankle and DPknee-ankle of successful recovery were observed with distal inter-joint coordination on the swing side. Inter-joint coordination of the stance limb did not significantly differ between successful and unsuccessful recovery conditions. These findings indicate that the control of the swing limb’s distal joints is crucial for regaining balance after trips. SignificanceAn implication derived from this study is that greater in-phase coordination and smaller coordination variability in distal joints of the swing limb could be considered as potential targets for interventions aimed at preventing trip-induced accidental.

A Full-Process, Fine-Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On-Skin Sensors and Multi-Task Gait Transformer Model.

Rehabilitation of patients with lower limb movement disorders is a gradual process, which requires full-process assessments to guide the implementation of rehabilitation plans. However, the current methods can only complete the assessment in one stage and lack objective and quantitative assessment strategies. Here, a full-process, fine-grained, and quantitative rehabilitation assessments platform (RAP) supported by on-skin sensors and a multi-task gait transformer (MG-former) model for patients with lower limb movement disorders is developed. The signal quality and sensitivity of on-skin sensor is improved by the synthesis of high-performance triboelectric material and structure design. The MG-former model can simultaneously perform multiple tasks including binary classification, multiclassification, and regression, corresponding to assessment of fall risk, walking ability, and rehabilitation progress, covering the whole rehabilitation cycle. The RAP can assess the walking ability of 23 hemiplegic patients, which has highly consistent results with the scores by the experienced physician. Furthermore, the MG-former model outputs fine-grained assessment results when performing regression task to track slight progress of patients that cannot be captured by conventional scales, facilitating adjustment of rehabilitation plans. This work provides an objective and quantitative platform, which is instructive for physicians and patients to implement effective strategy throughout the whole rehabilitation process.

Prediction of abnormal gait behavior of lower limbs based on depth vision

Abstract As a kind of lower-limb motor assistance device, the intelligent walking aid robot plays an essential role in helping people with lower-limb diseases to carry out rehabilitation walking training. In order to enhance the safety performance of the lower-limb walking aid robot, this study proposes a deep vision-based abnormal lower-limb gait prediction model construction method for the problem of abnormal gait prediction of patients’ lower limbs. The point cloud depth vision technique is used to acquire lower-limb motion data, and a multi-posture angular prediction model is trained using long and short-term memory networks to build a model of the user’s lower-limb posture characteristics during normal walking as well as a real-time lower-limb motion prediction model. The experimental results indicate that the proposed lower-limb abnormal behavior prediction model is able to achieve a 97.4% prediction rate of abnormal lower-limb movements within 150 ms. Additionally, the model demonstrates strong generalization ability in practical applications. This paper proposes further ideas to enhance the safety performance of lower-limb rehabilitation robot use for patients with lower-limb disabilities.

Simulating vortex generation to investigate the propulsive and braking mechanisms of breaststroke kick using computational fluid dynamics on a breaststroke swimmer

Swimmers primarily increase their forward velocity through lower limb motion in breaststroke, making the breaststroke kick crucial for optimizing race times. Recent studies have highlighted the generation of vortices around the swimmer’s entire body to propel forward during swimming. However, the investigation of vortex generation during breaststroke kicks remains unexplored. This study aimed to reveal the propulsive and braking mechanisms of breaststroke kicks by simulating vortex generation using computational fluid dynamics (CFD). Kinematic data during the breaststroke kick and a three-dimensional digital model were collected to conduct CFD for a male breaststroke swimmer. Vortex generation was determined during one breaststroke kick from the CFD results. Vortices, which potentially induce a decrease in forward velocity, were generated by the swimmer’s lower legs and feet during the recovery phase. The swimmer generated vortices on the dorsal side of the feet and the posterior and lateral sides of the lower legs to increase the forward velocity during the out-sweep phase. The swimmer generated vortices on the lateral sides of the thighs and lower legs and the dorsal and lateral sides of the feet during the in-sweep phase to maintain forward velocity. Moreover, vortices generated from the out-sweep to the in-sweep merged and were shed backward relative to the swimming direction after the in-sweep phase. This study is the first to reveal the propulsive and braking mechanisms of breaststroke kicks by analyzing the vortex generation.

Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction, limited contextual information and restricted feature extraction scales during feature extraction. This paper proposed a LLMR method based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet). The denoised sEMG time series was transformed into Gramian Angular Difference Field (GADF) matrix based on GAF. The MS-ResNet model, incorporating ResNet and multiscale feature fusion concepts, was proposed to comprehensively capture global and local information through different-scale feature extraction and fusion, so as to improve recognition performance. sEMG signals from 11 muscles of the preferred leg of 15 healthy subjects were recorded during six common lower limb movements. Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance. The study revealed that selecting k as 13, coupled with 11 muscles, yielded optimal model performance with the average cross-individual recognition accuracy reaching 97.62 %, demonstrating the model’s efficiency in LLMR. This method could provide a viable solution for developing more efficient and reliable LLMR systems, applicable to lower limb exoskeleton robots and intelligent prosthetics.

Changes in inflammatory edema and fat fraction of thigh muscles following a half-marathon in recreational marathon runners.

It is known that microtrauma exists in the thigh muscles after long-distance running such as the half-marathon. Moreover, training characteristics of long-distance runners may influence the specificity of the distribution of muscle fiber types in the thigh and affect muscle responses to lipid metabolism. However, the specific changes in microtrauma and intramuscular lipid in thigh muscles after a half-marathon are unknown. A cohort of 20 healthy recreational marathon runners was recruited to complete a half-marathon. MRI T2 mapping and 6-echo q-Dixon sequences were employed at baseline (P1), 2-3h after running (P2), and 1day after running (P3). Inflammatory markers (the T2 values) and intramuscular fat fraction (the proton density fat fraction, PDFF) were measured in thigh muscles to detect microtrauma and intramuscular lipid changes, respectively. One-way analysis of variance showed significant time effects for T2 values and PDFF. Post hoc analysis of the 14 datasets collected at three time points revealed significantly higher T2 values in all thigh muscles after running (all p<0.05). Significant differences in T2 values persisted for all thigh muscles at P3 compared to P1 (all p<0.05). The PDFF of the vastus lateralis and vastus medialis was significantly decreased at P2 compared to P1 (p<0.05). No significant differences in PDFF were observed for the thigh muscles at P3 compared to P1. The manifestations of inflammation edema and intramuscular lipid investigated through MRI may offer valuable insights for recreational marathon runners regarding the lower limb movement characteristics during half-marathon running.

Towards Prosthesis Control: Identification of Locomotion Activities through EEG-Based Measurements

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework for the recognition and classification of locomotion activities using electroencephalography (EEG) data by comparing the performance of different machine learning algorithms. Data of the lower limb movements during level ground walking as well as going up stairs, down stairs, up ramps, and down ramps were collected from 10 healthy volunteers. Time- and frequency-domain features were extracted by applying independent component analysis (ICA). Successively, they were used to train and test random forest and k-nearest neighbors (kNN) algorithms. For the classification, random forest revealed itself as the best-performing one, achieving an overall accuracy up to 92%. The findings of this study contribute to the field of assistive robotics by confirming that EEG-based measurements, when combined with appropriate machine learning models, can serve as robust inputs for prosthesis control systems.

Analysing lower limb motion and muscle activation in athletes with ankle instability during dual-task drop-jump

ABSTRACT This study investigates the impact of chronic ankle instability (CAI) on athletes’ lower extremity mechanics during bounce drop-jump landings with divided attention. Thirty Division I physical education voluntarily participated in the study. They performed two sets of bounce drop jumps: one set with a divided attention task and the other without. The obtained data were analysed using a paired t-test to compare the outcomes between the divided attention (DA) and non-divided attention (NDA) tasks. Athletes with CAI, during the DA task, displayed higher vertical landing forces, increased ankle inversion velocity, and greater range of motion of the ankle, knee, and hip in the frontal and transverse planes. They also exhibited insufficient neuromuscular preparation of the rectus femoris muscle. Notably, distinct kinematic alterations were observed in the ankle, knee, and hip joints regarding frontal and transverse lower-extremity kinematics. The findings suggest that athletes with CAI experience decreased activation of the rectus femoris muscle, which may impact their dynamic postural stability from pre-landing to ascending phases. Furthermore, the results indicate that individuals with CAI closely replicate the injury mechanisms encountered during a drop-jump landing task with divided attention. These insights offer valuable information about the real-time challenges faced by athletes with CAI.

Effects of different sensory integration tasks on the biomechanical characteristics of the lower limb during walking in patients with patellofemoral pain.

This study aimed to analyze the biomechanical characteristics of the lower limb in patients with patellofemoral pain (PFP) while walking under different sensory integration tasks and elucidate the relationship between these biomechanical characteristics and patellofemoral joint stress (PFJS). Our study's findings may provide insights which could help to establish new approaches to treat and prevent PFP. Overall, 28 male university students presenting with PFP were enrolled in this study. The kinematic and kinetic data of the participants during walking were collected. The effects of different sensory integration tasks including baseline (BL), Tactile integration task (TIT), listening integration task (LIT), visual integration task (VIT) on the biomechanical characteristics of the lower limb were examined using a One-way repeated measures ANOVA. The relationship between the aforementioned biomechanical characteristics and PFJS was investigated using Pearson correlation analysis. The increased hip flexion angle (P = 0.016), increased knee extension moment (P = 0.047), decreased step length (P < 0.001), decreased knee flexion angle (P = 0.010), and decreased cadence (P < 0.001) exhibited by patients with PFP while performing a VIT were associated with increased patellofemoral joint stress. The reduced cadence (P < 0.050) achieved by patients with PFP when performing LIT were associated with increased patellofemoral joint stress. VIT significantly influenced lower limb movement patterns during walking in patients with PFP. Specifically, the increased hip flexion angle, increased knee extension moment, decreased knee flexion angle, and decreased cadence resulting from this task may have increased PFJS and may have contributed to the recurrence of PFP. Similarly, patients with PFP often demonstrate a reduction in cadence when exposed to TIT and LIT. This may be the main trigger for increased PFJS under TIT and LIT.