Search Results (55)

This study proposes a hybrid approach combining threshold-based algorithm and deep learning to detect four major gait events—initial contact (IC), toe-off (TO), opposite initial contact (OIC), and opposite toe-off (OTO)—using only a smartphone’s built-in inertial sensor placed in the user’s pocket. The algorithm enables estimation of spatiotemporal gait parameters such as cadence, stride length, loading response (LR), pre-swing (PSw), single limb support (SLS), double limb support (DLS), and swing phase and symmetry. Gait data were collected from 20 healthy individuals and 13 hemiparetic stroke patients. To reduce sensitivity to sensor orientation and suppress noise, sum vector magnitude (SVM) features were extracted and filtered using a second-order Butterworth low-pass filter at 3 Hz. A deep learning model was further compressed using knowledge distillation, reducing model size by 96% while preserving accuracy. The proposed method achieved error rates in event detection below 2% of the gait cycle for healthy gait and a maximum of 4.4% for patient gait in event detection, with corresponding parameter estimation errors also within 4%. These results demonstrated the feasibility of accurate and real-time gait monitoring using a smartphone. In addition, statistical analysis of gait parameters such as symmetry and DLS revealed significant differences between the normal and patient groups. While this study is not intended to provide or guide rehabilitation treatment, it offers a practical means to regularly monitor patients’ gait status and observe gait recovery trends over time. Full article

Post-stroke hemiplegia often leads to gait asymmetry, mobility reduction, and increased fall risk. Foot Drop Stimulation (FDS) is used in rehabilitation to improve dorsiflexion and gait patterns. Through cyclogram-based analysis, this retrospective study evaluated the effectiveness of FDS in enhancing inter-limb gait symmetry in 21 post-stroke hemiplegic individuals following 10 sessions of treadmill training combined with FDS. Participants underwent 3D gait analysis pre- and post-intervention, performed by means of optical motion capture system, from which spatiotemporal and cyclogram features of the hip, knee, and ankle were computed. FDS was found to significantly improve dynamic range of motion (ROM) of the affected side at hip (+5%) and knee (+9%) joints. Cyclogram analysis showed that FDS reduced inter-limb hip asymmetry (orientation: 13.35° to 10.65°, Trend Symmetry Index: 19.09° to 15.46°), though no improvements were observed at the ankle. FDS with treadmill training improved hip and knee symmetry, supporting cyclogram-based assessments for gait rehabilitation and highlighting the need for targeted ankle interventions. Further research is needed to explore long-term effects and optimize rehabilitation strategies. Full article

This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, and loss of motor coordination during rehabilitation. To achieve this, it proposes an innovative design method for a lower limb rehabilitation training system based on Bayesian networks and parallel mechanisms. A Bayesian network model is constructed based on expert knowledge and structural mechanics analysis, considering key factors such as rehabilitation scenarios, motion trajectory deviations, and rehabilitation goals. By utilizing the motion characteristics of parallel mechanisms, we designed a rehabilitation training device that supports multidimensional gait correction. A three-dimensional digital model is developed, and multi-posture ergonomic simulations are conducted. The study focuses on quantitatively assessing the kinematic characteristics of the hip, knee, and ankle joints while wearing the device, establishing a comprehensive evaluation system that includes range of motion (ROM), dynamic load, and optimization matching of motion trajectories. Kinematic analysis verifies that the structural design of the device is reasonable, aiding in improving patients’ gait, enhancing strength, and restoring flexibility. The Bayesian network model achieves personalized rehabilitation goal optimization through dynamic probability updates. The design of parallel mechanisms significantly expands the range of joint motion, such as enhancing hip sagittal plane mobility and reducing dynamic load, thereby validating the notable optimization effect of parallel mechanisms on gait rehabilitation. Full article

Background/Objectives: Gait dysfunction occurs in 80% of stroke survivors. It increases the risk of falls, reduces functional independence, and thus affects the quality of life. Therefore, it is very important to restore the gait function in post-stroke survivors. The purpose of this study was to investigate the functional changes of gait biomechanics in patients with hemiplegia in the subacute stage of ischemic stroke based on spatiotemporal, kinematic, and EMG parameters. Methods: Initial biomechanical gait analyses of 31 patients and 34 controls were selected. The obtained parameters were assessed and compared within and across the study groups (post-stroke hemiparetic patients and healthy controls) to determine the pathognomonic features of the hemiplegic gait. Results: The gait function asymmetry was characterized by reciprocal changes, i.e., harmonic sequences of gait cycles. The most significant changes were in the kinematics of the knee joint and the EMG activity in the anterior tibialis, gastrocnemius, and hamstring muscles on the paretic side. The movements in the lower extremity joints ranged from a typical amplitude decrease to an almost complete lack of movement or involuntary excessive movement, as can occur in the ankle joint. The knee joint showed two distinct patterns: a slight flexion throughout the entire gait cycle and knee hyperextension during the middle stance phase. Conclusions: The gait function asymmetry is characterized by reciprocal changes (in temporal gait parameters). The most significant changes included decreased amplitude in the knee joint and decreased amplitude of EMG of all muscles under study, except for the m. quadriceps femoris. Full article

Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-end deep learning approach that autonomously extracts features from joint angle trajectories for a gait cycle, minimizing human intervention. A total of 74 sub-acute post-stroke hemiplegic patients with lower limb impairments were included in the analysis. The dataset comprised 219 sagittal plane joint angle and angular velocity trajectories from the hip, knee, and ankle joints during gait cycles. Deep temporal clustering was employed to cluster them in an end-to-end manner by simultaneously optimizing feature extraction and clustering, with hyperparameter tuning tailored for kinematic gait cycle data. Through this method, six optimal clusters were selected with a silhouette score of 0.2831, which is a relatively higher value compared to other clustering algorithms. To clarify the characteristics of the selected groups, in-depth statistics of spatiotemporal, kinematic, and clinical features are presented in the results. The results demonstrate the effectiveness of end-to-end deep learning-based clustering, yielding significant performance improvements without the need for manual feature extraction. While this study primarily utilizes sagittal plane data, future analysis incorporating coronal and transverse planes as well as muscle activity and gait symmetry could provide a more comprehensive understanding of gait patterns. Full article

This study investigates the biomechanical differences between typically developed (TD) individuals and those with contralateral hemiplegia (CH) using musculoskeletal modeling in OpenSim. Ten TD participants and ten CH patients were analyzed for joint angles and external joint moments around the three anatomical axes: frontal, sagittal, and transverse. The analysis focused on hip, pelvis, lumbar, knee, ankle, and subtalar joint movements, leveraging MRI-derived bone length data and gait analysis. Significant differences (p < 0.05) were observed in hip flexion, pelvis tilt, lumbar extension, and ankle joint angles, highlighting the impact of hemiplegia on these specific joints. However, parameters like hip adduction and rotation, knee moment, and subtalar joint dynamics did not show significant differences, with p > 0.05. The comparison of joint angle and joint moment correlations between TD and CH participants highlights diverse coordination patterns in CH. Joint angles show significant shifts, such as HF and LR (−0.35 to −0.97) and PR and LR (0.22 to −0.78), reflecting disrupted interactions, while others like HR and LR (0.42 to 0.75) exhibit stronger coupling in CH individuals. Joint moments remain mostly stable, with HF and HA (0.54 to 0.53) and PR and LR (−0.51 to −0.50) showing negligible changes. However, some moments, like KA and HF (0.11 to −0.13) and PT and KA (0.75 to 0.67), reveal weakened or altered relationships. These findings underscore biomechanical adaptations and compensatory strategies in CH patients, affecting joint coordination. Overall, CH individuals exhibit stronger negative correlations, reflecting impaired coordination. These findings provide insight into the musculoskeletal alterations in hemiplegic patients, potentially guiding the development of targeted rehabilitation strategies. Full article

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application. Full article

Background and Objectives: Stroke patients often experience changes in their pelvic tilt, trunk impairments and decreased gait and balance. While various therapeutic interventions have been attempted to improve these symptoms, there is a need for interventions that are easy to apply and reduce the physical labor of physical and occupational therapists. We aimed to investigate the immediate effects of two different methods of trunk elastic taping on the pelvic inclination, trunk impairment, balance, and gait in chronic stroke patients. Materials and Methods: We performed a single-blind randomized controlled trial involving 45 patients with chronic stroke. Participants were randomly assigned to one of three groups: forward rotation with posterior pelvic tilt taping (FRPPT, n = 14), backward rotation with posterior pelvic tilt taping (BRPPT, n = 14), or placebo taping (PT = 14). This study was conducted from December 2023 to January 2024. All the measurements were performed twice: before the intervention and immediately after the intervention. The pelvic inclination was assessed using the anterior pelvic tilt angle. The trunk impairment scale (TIS) was used to measure the trunk impairment. The balance and gait were evaluated using a force plate and walkway system. Results: The pelvic inclination was significantly different in the FRPPT and BRPPT groups compared to the PT group (p < 0.05, p < 0.001). The TIS and gait were significantly increased in the FRPPT group compared to the PT group (p < 0.05). The balance significantly improved in the FRPPT and BRPPT within groups (p < 0.05). Conclusions: Two different methods of posterior pelvic tilt taping improved the anterior pelvic tilt in chronic hemiplegic stroke patients compared with PT, and the FRPPT method also improved the trunk impairment and gait. Therefore, posterior pelvic tilt taping can be used as an intervention with immediate effect. Full article

Children with hemiplegic cerebral palsy (hemi-CP) frequently experience deficits in dynamic balance, a crucial factor influencing gait function. This imbalance can manifest as temporal–spatial gait asymmetry, where movement patterns differ between the affected and less affected sides. This study investigated how temporal–spatial gait asymmetries and dynamic balance are associated in children with hemi-CP. Eighty-five children with hemi-CP (age: 13.27 ± 1.72 years) were included. The temporal (AI_Temporal) and spatial (AI_Spatial) gait asymmetry indices were, respectively, computed with reference to the swing time and step length of affected and less affected sides, which were collected through a 3D gait analysis. Measures of dynamic balance included the directional dynamic limit-of-stability (D-LOS_directional) assessed across multiple directions (forward, rearward, affected, and less affected) and the overall dynamic limit-of-stability (D-LOS_overall) during static stance, in addition to the heel-to-heel base of support (BOS_H-to-H) during walking, the dynamic gait index (DynGI), and the Timed Up and Down Stair (TUDS) test.The D-LOS_overall correlated negatively with the temporal (r = −0.437, p < 0.001) and spatial (r = −0.279, p = 0.009) asymmetries. The D-LOS_directional (forward, rearward, affected, and less affected) correlated negatively with temporal asymmetry (r ranged from −0.219 to −0.411, all p < 0.05), but only the D-LOS_directional rearward (r = −0.325, p = 0.002) and less affected (r = −0.216, p = 0.046) correlated with spatial asymmetry. The BOS_H-to-H correlated positively with both temporal (r = 0.694, p < 0.001) and spatial (r = 0.503, p < 0.001) asymmetries. The variation in D-LOS_overall and BOS_H-to-H accounted for 19.1% and 48.2%, respectively, of the variations in the temporal asymmetry and 7.8% and 25.3% of the variations in the spatial asymmetry. The findings of this study suggest that dynamic balance control is related to the magnitude of temporal–spatial gait asymmetries in children with hemi-CP. This evidence lays the groundwork for further research into the mechanism linking gait asymmetry and dynamic balance, potentially leading to a deeper understanding of these impairments, while also highlighting the need for longitudinal studies with the inclusion of a broader population to enhance the generalizability of the findings. Full article

Cerebral palsy (CP) is a common neurodevelopmental disorder characterized by pronounced motor dysfunction and resulting in physical disability. Neural precursor cells (NPCs) have shown therapeutic promise in mouse models of hypoxic-ischemic (HI) perinatal brain injury, which mirror hemiplegic CP. Constraint-induced movement therapy (CIMT) enhances the functional use of the impaired limb and has emerged as a beneficial intervention for hemiplegic CP. However, the precise mechanisms and optimal application of CIMT remain poorly understood. The potential synergy between a regenerative approach using NPCs and a rehabilitation strategy using CIMT has not been explored. We employed the Rice–Vannucci HI model on C57Bl/6 mice at postnatal day (PND) 7, effectively replicating the clinical and neuroanatomical characteristics of hemiplegic CP. NPCs were transplanted in the corpus callosum (CC) at PND21, which is the age corresponding to a 2-year-old child from a developmental perspective and until which CP is often not formally diagnosed, followed or not by Botulinum toxin injections in the unaffected forelimb muscles at PND23, 26, 29 and 32 to apply CIMT. Both interventions led to enhanced CC myelination and significant functional recovery (as shown by rearing and gait analysis testing), through the recruitment of endogenous oligodendrocytes. The combinatorial treatment indicated a synergistic effect, as shown by newly recruited oligodendrocytes and functional recovery. This work demonstrates the mechanistic effects of CIMT and NPC transplantation and advocates for their combined therapeutic potential in addressing hemiplegic CP. Full article

(1) Background: Traditional gait assessment methods have limitations like time-consuming procedures, the requirement of skilled personnel, soft tissue artifacts, and high costs. Various 3D time scanning techniques are emerging to overcome these issues. This study compares a 3D temporal scanning system (Move4D) with an inertial motion capture system (Xsens) to evaluate their reliability and accuracy in assessing gait spatiotemporal parameters and joint kinematics. (2) Methods: This study included 13 healthy people and one hemiplegic patient, and it examined stance time, swing time, cycle time, and stride length. Statistical analysis included paired samples t-test, Bland–Altman plot, and the intraclass correlation coefficient (ICC). (3) Results: A high degree of agreement and no significant difference (p > 0.05) between the two measurement systems have been found for stance time, swing time, and cycle time. Evaluation of stride length shows a significant difference (p < 0.05) between Xsens and Move4D. The highest root-mean-square error (RMSE) was found in hip flexion/extension (RMSE = 10.99°); (4) Conclusions: The present work demonstrated that the system Move4D can estimate gait spatiotemporal parameters (gait phases duration and cycle time) and joint angles with reliability and accuracy comparable to Xsens. This study allows further innovative research using 4D (3D over time) scanning for quantitative gait assessment in clinical practice. Full article

(1) Background: This study evaluates the effectiveness of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) in improving gait in post-stroke hemiplegic patients, using wearable sensor technology for objective gait analysis. (2) Methods: A total of 72 stroke patients were randomized into control, sham stimulation, and LF-rTMS groups, with all receiving standard medical treatment. The LF-rTMS group underwent stimulation on the unaffected hemisphere for 6 weeks. Key metrics including the Fugl-Meyer Assessment Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and gait parameters were measured before and after treatment. (3) Results: The LF-rTMS group showed significant improvements in the FMA-LE, BBS, MBI, and various gait parameters compared to the control and sham groups (p < 0.05). Specifically, the FMA-LE scores improved by an average of 5 points (from 15 ± 3 to 20 ± 2), the BBS scores increased by 8 points (from 35 ± 5 to 43 ± 4), the MBI scores rose by 10 points (from 50 ± 8 to 60 ± 7), and notable enhancements in gait parameters were observed: the gait cycle time was reduced from 2.05 ± 0.51 s to 1.02 ± 0.11 s, the stride length increased from 0.56 ± 0.04 m to 0.97 ± 0.08 m, and the walking speed improved from 35.95 ± 7.14 cm/s to 75.03 ± 11.36 cm/s (all p < 0.001). No adverse events were reported. The control and sham groups exhibited improvements but were not as significant. (4) Conclusions: LF-rTMS on the unaffected hemisphere significantly enhances lower-limb function, balance, and daily living activities in subacute stroke patients, with the gait parameters showing a notable improvement. Wearable sensor technology proves effective in providing detailed, objective gait analysis, offering valuable insights for clinical applications in stroke rehabilitation. Full article

Most lower limb rehabilitation robots are limited to specific training postures to adapt to stroke patients in multiple stages of recovery. In addition, there is a lack of attention to the switching functions of the training side, including left, right, and bilateral, which enables patients with hemiplegia to rehabilitate with a single device. This article presents an exoskeleton robot named the multistage hemiplegic lower-limb rehabilitation robot, which has been designed to do rehabilitation in multiple training postures and training sides. The mechanism consisting of the thigh, calf, and foot is introduced. Additionally, the design of the multi-mode limit of the hip, knee, and ankle joints supports delivering therapy in any posture and training sides to aid patients with hemiplegia in all stages of recovery. The gait trajectory is planned by extracting the gait motion trajectory model collected by the motion capture device. In addition, a control system for the training module based on adaptive iterative learning has been simulated, and its high-precision tracking performance has been verified. The gait trajectory experiment is carried out, and the results verify that the trajectory tracking performance of the robot has good performance. Full article

Cerebral palsy poses challenges in walking, necessitating ankle foot orthoses (AFOs) for stability. Gait analysis, particularly on slopes, is crucial for effective AFO assessment. The study aimed to compare the performance of commercially available AFOs with a new sports-specific AFO in children with hemiplegic cerebral palsy and to assess the effects of varying slopes on gait. Eighteen participants, aged 6–11, with hemiplegia, underwent gait analysis using GRAIL technology. Two AFO types were tested on slopes (uphill +10 deg, downhill −5 deg, level-ground). Kinematic, kinetic, and spatiotemporal parameters were analyzed. The new AFO contributed to significant changes in ankle dorsi-plantar-flexion, foot progression, and trunk and hip rotation during downhill walking. Additionally, the new AFO had varied effects on spatiotemporal gait parameters, with an increased stride length during downhill walking. Slope variations significantly influenced the kinematics and kinetics. This study provides valuable insights into AFO effectiveness and the impact of slopes on gait in hemiplegic cerebral palsy. The findings underscore the need for personalized interventions, considering environmental factors, and enhancing clinical and research approaches for improving mobility in cerebral palsy. Full article

Gait event detection is essential for controlling an orthosis and assessing the patient’s gait. In this study, patients wearing an electromechanical (EM) knee–ankle–foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of −2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO. Full article