Search Results (821)

Background: Progressive supranuclear palsy (PSP) is characterized by early postural instability and gait dysfunction, with frequent falls. Rehabilitation is an important therapeutic approach for motor dysfunction in patients with PSP. However, no conclusions have yet been drawn regarding the beneficial effects of rehabilitation in PSP, including the optimal duration of rehabilitation and differences in treatment effects among PSP subtypes. Herein, we investigated the effects of short-term rehabilitation and separately analyzed the effects on patients with PSP-Richardson’s syndrome (RS) and PSP-progressive gait freezing (PGF). Methods: The participants underwent several therapeutic exercise programs individualized for each participant, performed over 2 weeks. Analysis was performed on 25 patients with PSP-RS and eight with PSP-PGF. Results: Short-term rehabilitation improved the Berg Balance Scale score in both the PSP-RS and PSP-PGF groups, step length on the symptom-dominant side in PSP-RS, the coefficient of variation of step length on the symptom-dominant side, and the stance phase of the Symmetry Index in PSP-PGF. Conclusions: Overall, this 2-week short-term rehabilitation intervention was shown to have beneficial effects on balance in patients with PSP-RS and PSP-PGF. Full article

The application of gait analysis on patients with Hip Osteoarthritis (HOA) before and after Total Hip Arthroplasty (THA) surgery can provide accurate diagnostics, reliable treatment decision making, and proper rehabilitation efforts. Acquired kinematic trajectories provide discriminating features that can be used to determine the gait patterns of healthy subjects and the effects of surgical operation. However, there is still a lack of consensus on the best discriminating kinematics to achieve this. Our investigation aims to utilize Deep Learning (DL) methodologies and improve classification results for the kinematic parameters of healthy, HOA, and 6 months post-THA gait cycles. Kinematic angles from the lower limb are used directly as one-dimensional inputs into a DL model. Based on the human gait cycle’s features, a hybrid Long Short-Term Memory–Convolutional Neural Network (HLSTM-CNN) is designed for the classification of healthy/HOA/THA gaits. It was found, from the results, that the sagittal angles of hip and knee, and front angles of FPA and knee, provide the most discriminating results with accuracy above 94% between healthy and HOA gaits. Interestingly, when using the sagittal angles of hip and knee to analyze the THA gaits, common subjects have the same results on the misclassifications. This crucial information provides a glimpse in the determination for the success or failure of THA. Full article

Treadmill running simulates various conditions, including flat, uphill, and downhill gradients, making it useful for training and rehabilitation. This study aimed to examine how incline and decline treadmill running affect local dynamic stability of individual running movement components that cooperatively contribute to achieving the running tasks. Principal component analysis (PCA) was used to decompose movement components, termed principal movements (PMs), from kinematic marker data collected from 19 healthy recreational runners (9 females and 10 males, 23.6 ± 3.7 years) during treadmill running at 10 km/h across different gradients (−6, −3, 0, +3, +6 degrees). The largest Lyapunov exponent (LyE) of individual PM positions (higher LyE = greater instability) was analyzed using repeated-measures ANOVA to assess treadmill gradient effects across PMs. The results showed that the effects of treadmill gradient appear in PM₃, which corresponds to the mid-stance phase of the gait cycle. Specifically, decline treadmill running significantly decreased local dynamic stability (greater LyE) compared to equivalent incline conditions (p ≤ 0.005). These findings suggest that decline treadmill running should be used cautiously in rehabilitation settings due to its potential to reduce an ability to control and respond to small perturbations, thereby increasing the risk of instability during the weight-bearing support phase of gait. Full article

In a prospective study, we examined the recovery trajectory of patients with lower extremity fractures to better understand the healing process in the absence of complications. Using a chest-mounted inertial measurement unit (IMU) device for gait analysis and collecting patient-reported outcome measures, we focused on 12 key gait variables, including Mean Leg Lift Acceleration, Stance Time, and Body Orientation. We employed a linear mixed model (LMM) to analyze these variables over time, incorporating both fixed and random effects to account for individual differences and the time since injury. This model also adjusted for varying intervals between assessments. Our study provided insights into gait recovery across different fracture types using data from 318 patients who experienced no complications or readmissions during their recovery. Through LMM analysis, we found that Tibia-Distal fractures demonstrated the fastest recovery, particularly in terms of mobility and strength. Tibia-Proximal fractures showed balanced improvements in both mobility and stability, suggesting that rehabilitation should target both strength and balance. Femur fractures exhibited varied recovery, with Diaphyseal fractures showing clear improvements in stability, while Distal fractures reflected gains in limb strength but with some variability in stability. To examine patients with readmissions, we conducted a Chi-squared test of independence to determine whether there was a relationship between fracture type and readmission rates, revealing a significant association (p < 0.001). Pelvis fractures had the highest readmission rates, while Tibia-Diaphyseal and Tibia-Distal fractures were more prone to infections, highlighting the need for enhanced infection control strategies. Femur fractures showed moderate readmission and infection rates, indicating a mixed risk profile. In conclusion, our findings emphasize the importance of fracture-specific rehabilitation strategies, focusing on infection prevention and individualized treatment plans to optimize recovery outcomes. Full article

Background/Objectives: Gait retraining is widely used in orthopedic rehabilitation to address abnormal movement patterns. However, retaining walking modifications can be challenging without guidance from physical therapists. Real-time auditory biofeedback can help patients learn and maintain gait alterations. This study piloted the feasibility of the musification of feedback to medialize the center of pressure (COP). Methods: To provide musical feedback, COP and plantar pressure were captured in real time at 100 Hz from a wireless 16-sensor pressure insole. Twenty healthy subjects (29 ± 5 years old, 75.9 ± 10.5 Kg, 1.73 ± 0.07 m) were recruited to walk using this system and were further analyzed via marker-based motion capture. A lowpass filter muffled a pre-selected music playlist when the real-time center of pressure exceeded a predetermined lateral threshold. The only instruction participants received was to adjust their walking to avoid the muffling of the music. Results: All participants significantly medialized their COP (−9.38% ± 4.37, range −2.3% to −19%), guided solely by musical feedback. Participants were still able to reproduce this new walking pattern when the musical feedback was removed. Importantly, no significant changes in cadence or walking speed were observed. The results from a survey showed that subjects enjoyed using the system and suggested that they would adopt such a system for rehabilitation. Conclusions: This study highlights the potential of musical feedback for orthopedic rehabilitation. In the future, a portable system will allow patients to train at home, while clinicians could track their progress remotely through cloud-enabled telemetric health data monitoring. 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

Portable monitoring devices based on Inertial Measurement Units (IMUs) have the potential to serve as quantitative assessments of human movement. This article proposes a new method to identify the optimal placements of the IMUs and quantify the smoothness of the gait. First, it identifies gait events: foot-strike (FS) and foot-off (FO). Second, it segments the signals of linear acceleration and angular velocities obtained from the IMUs at four locations into steps and strides. Finally, it applies three smoothness metrics (SPARC, PM, and LDLJ) to determine the most reliable metric and the best location for the sensor, using data from 20 healthy subjects who walked an average of 25 steps on a flat surface for this study (117 measurements were processed). All events were identified with less than a 2% difference from those obtained with the photogrammetry system. The smoothness metric with the least variance in all measurements was SPARC. For the smoothness metrics with the least variance, we found significant differences between applying the metrics with the complete signal (C) and the signal segmented by strides (S). This method is practical, time-effective, and low-cost in terms of computation. Furthermore, it is shown that analyzing gait signals segmented by strides provides more information about gait progression. Full article

Introduction: The evidence showed that the risk of falls was higher in women, and yoga was considered an effective rehabilitation method for preventing falls. However, there had been no previous attempts to synthesize the evidence specifically for the use of yoga in preventing falls among older women. Objectives: This systematic review aimed to strengthen the existing body of evidence by focusing exclusively on the impact of yoga in improving fall-related physical functions among older women. Methods: A systematic review was conducted following the PRISMA guidelines. The protocol was developed in advance of the study and registered on PROSPERO (Registration number: CRD42024506550). Results: The effects of yoga on balance, gait, and lower-limb strength were inconsistent. It showed that yoga programs designed to prevent falls in older women might not demonstrate the same effectiveness as those identified in previous systematic reviews for the general older adult population. Conclusions: This systematic review is the first to exclusively explore the impact of yoga on fall-related physical functions in older women. However, this review did not directly observe a definitive effect of yoga on fall prevention in older women. Future studies should delve deeper into identifying appropriate yoga postures and determining the optimal dose required to enhance physical function and prevent falls. Full article

Background: Gait disturbances characterized by asymmetries in lower limb strength and gait patterns are frequently observed in stroke patients, which increases gait variability and fall risk. However, the extent to which lower limb strength asymmetry influences gait asymmetry and variability in this population remains unclear. Methods: This cross-sectional study included 84 participants, comprising stroke survivors and age- and sex-matched healthy older adults. A portable dynamometer was used to assess lower limb strength, and inertial measurement units to analyze gait parameters. Asymmetry indices were used to quantify strength and gait asymmetries. Statistical analyses included Pearson correlations and stepwise regression to examine the relationships among lower limb strength asymmetry, gait asymmetry, and gait variability. Results: Stroke survivors exhibited significantly greater lower limb strength and gait asymmetries than healthy older adults (p < 0.001). Knee extension (KE) strength asymmetry was a significant predictor of increased gait variability in stroke survivors (R² = 0.448, p < 0.001) but not in healthy controls. Moreover, longer poststroke duration was associated with greater asymmetry in KE strength (r = 0.42, p < 0.05) and double support time (r = 0.45, p < 0.05). Conclusions: Lower limb strength asymmetry, specifically in knee extensors, is a critical determinant of gait asymmetry and variability in stroke survivors. The association between poststroke duration and increased asymmetry indicates the progressive nature of these impairments. These findings emphasize the importance of targeted interventions to reduce strength asymmetry and address chronic impairments in poststroke rehabilitation to improve gait stability and reduce fall risk. Full article

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder that significantly impairs motor function, leading to mobility challenges and an increased risk of falls. Current assessment tools often inadequately measure the complexities of motor impairments associated with PD, highlighting the need for a reliable tool. This study introduces the Motor Assessment Timed Test (MATT), designed to assess functional mobility in PD patients. Methods: A cross-sectional study was conducted involving 57 participants (38 men and 19 women) aged 44 to 84, diagnosed with idiopathic PD. Participants were recruited from three PD associations and underwent a series of assessments, including MATT, to evaluate gait, balance, and dual-task performance under conditions that reflect real-life challenges faced by individuals with PD. Results: MATT demonstrated excellent reliability with intra-rater reliability (ICC = 0.99), inter-rater reliability (ICC = 0.96–0.99), and test–retest reliability (ICC = 0.93–0.99). The coefficient of variation for total time and each segment ranged from 4.73% to 13.71%, indicating consistent performance across trials. The concurrent validity showed very high correlations with established tools such as the Timed Up and Go (TUG) test (ρ = 0.86, p < 0.001) and the Berg Balance Scale (BBS) (ρ = −0.83, p < 0.001), among others. Only 7.3% of participants reported difficulties in understanding the MATT, predominantly those in advanced stages of the disease. In addition, 23.6% of participants experienced significant challenges in performing the test, particularly individuals with lower Mini-Mental State Examination (MMSE) scores (≤ 26) and more advanced disease progression. Conclusions: MATT is a promising tool for assessing motor complications in PD, offering a comprehensive evaluation of functional mobility. Its implementation in clinical practice could enhance the management of PD, facilitating tailored interventions and improving patient outcomes. Full article

Background/Objectives: While most studies on the postoperative condition of patients with spinal cord tumors describe long-term outcomes, data are needed on immediate surgical outcomes demanding rehabilitation to make informed assessments for postoperative planning. The aim of this study was to identify factors predicting function and rehabilitative needs after intradural spinal tumor surgery. Methods: Eighty-five prospectively recruited patients underwent surgery for intradural intramedullary (ID-IM; n = 23) and extramedullary (ID-EM; n = 62) tumors. Neurological and functional status were assessed before surgery, after surgery, and at discharge using the modified McCormick scale (MMS), Karnofsky performance status (KPS) scale, Barthel index (BI), and the gait index (GI). Results: There were no significant predictors of early postoperative rehabilitation in the ID-IM group. In the ID-EM group, age, thoracic level, subtotal resection (STR), repeat surgery, and functional scale scores predicted the need for rehabilitation. In multivariable analysis, MMS (odds ratio (OR) 8.7; 95% confidence interval (CI): 2.37–32.44) and STR (OR 13.00; 95%CI: 1.56–107.87) remained independent predictors of rehabilitation need (area under curve, 92%). Despite their younger age, most patients with ID-IM tumors, especially ependymomas, required rehabilitation but improved quickly (KPS, BI, p < 0.001). Among ID-EM tumors, meningiomas were characterized by poorer preoperative function and low gross total resection (GTR) rates, but did not deteriorate neurologically after surgery. Patients with schwannoma and ID-EM ependymomas achieved the highest GTR rate and had the best function both before and after surgery. Conclusions: These results may be useful for estimating early rehabilitation needs after intradural tumor surgery and counseling patients before surgery about the expected postoperative course. Full article

Purpose: This study aimed to elucidate the relationship between early postoperative gait parameters after total hip arthroplasty (THA) and the Forgotten Joint Score-12 (FJS-12) score at the 2-year follow-up after surgery. In addition, the cutoff gait parameter values for predicting discomfort using specific FJS-12 subitems were evaluated. Methods: Among 313 patients who underwent THA between April and December 2019, 44 (14.0%) who responded to the FJS-12 questionnaire at 2 years postoperatively were included in this study. Gait parameters, including walking speed, stride length, and their coefficients of variation (CVs), were measured at 13.8 ± 3.6 (mean ± standard deviation) days postoperatively. The FJS-12 was used to evaluate patients at 2 years after surgery. The correlation between the FJS-12 score and gait parameters was analyzed using Spearman’s rank correlation coefficient. To determine the significant predictors of the FJS-12 score, multiple regression analysis was performed after adjusting for age as a covariate. Furthermore, receiver operating characteristic curves were used to determine the cutoff gait parameter values for predicting discomfort using specific FJS-12 subitems. Results: The FJS-12 score was significantly positively correlated with walking speed (r_s = 0.38, p < 0.05) and stride length (r_s = 0.51, p < 0.01). Meanwhile, the FJS-12 score was significantly negatively correlated with the CVs of walking speed (r_s = −0.34, p < 0.05) and stride length (r_s = −0.35, p < 0.05). Based on multiple regression analysis, stride length was a significant predictor of discomfort assessed using the FJS-12 score (β = 0.48, p < 0.01). According to the receiver operating characteristic curves, the cutoff stride length values for predicting discomfort using the FJS-12 subitems 9, 10, 11, and 12 showed moderate accuracy (area under the curve > 0.7). Conclusions: Improved walking ability of patients who underwent THA through early rehabilitation is linked to joint discomfort and patient satisfaction in daily life 2 years postoperatively. Full article

Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time. Full article

Background: Parkinson’s disease is a neurodegenerative disorder affecting the central and peripheral nervous systems. Its treatment combines pharmacological and rehabilitative approaches, aimed at improving motor symptoms and preventing cognitive decline. Virtual reality emerges as a complementary rehabilitative option, showing efficacy in enhancing motor and cognitive aspects. This study systematically reviews the effectiveness of virtual reality in the rehabilitation of individuals with Parkinson’s disease. Methods: A systematic review was carried out. The Jadad scale was used to assess the methodological quality of the included studies, and the Oxford Center for Evidence-Based Medicine criteria were applied to determine the level of evidence and recommendation. The review followed PRISMA guidelines, with a literature search conducted in databases such as Cochrane and PubMed. Results: The studies indicate improvements in gait, balance, and overall motor function in Parkinson’s patients using virtual reality, though limitations such as small sample sizes and the lack of control groups in some studies are noted. Conclusions: Virtual reality proves to be an effective tool in Parkinson’s rehabilitation, enhancing balance and motor function. However, more studies with adequate methodological quality and larger sample sizes are needed to validate these findings. Full article

Background. Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton. Methods. Five PwST were recruited in this pilot cross-sectional study. We observed three experimental conditions: walking without and with the UAN.GO exoskeleton and walking with the UAN.GO combined with the OPTIGO walker. Each condition included five trials related to ADLs such as sitting and walking. Results. No statistically significant difference was found between heart rate and R–R of ECG data while comparing all the observed conditions during each respective trial. The NASA Task Load Index did not show significant differences across all trials, except for a significant difference between Condition 2 and Condition 3 in Trial 4 (p = 0.043). However, walking and sit-to-stand tasks seem to be more challenging according to the NASA-TLX. Only one participant scored over 70 points on the System Usability Scale. The TSQ-WT scores for conditions 2 and 3 were 62 (56.5–72.5) and 70 (66.5–75) points, respectively. Conclusions. This study suggests that UAN.GO exoskeleton could be used for RAGT in PwST with disability without compromising cardiovascular function. Full article