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Search Results (253)

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Keywords = gait spatiotemporal parameters

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11 pages, 941 KiB  
Article
Preliminary Evidence That Design Fluency Is Related to Dual-Task Treadmill Gait Variability in Healthy Adults
by Christopher I. Higginson, Morgan K. Bifano, Kelly M. Seymour, Rachel L. Orr, Kurt M. DeGoede and Jill S. Higginson
NeuroSci 2024, 5(3), 328-338; https://doi.org/10.3390/neurosci5030026 - 12 Sep 2024
Viewed by 287
Abstract
Evidence supporting a link between gait and cognition is accumulating. However, the relation between executive functioning and spatiotemporal gait parameters has received little attention. This is surprising since these gait variables are related to falls. The goal of this preliminary study was to [...] Read more.
Evidence supporting a link between gait and cognition is accumulating. However, the relation between executive functioning and spatiotemporal gait parameters has received little attention. This is surprising since these gait variables are related to falls. The goal of this preliminary study was to determine whether performance on measures of inhibition, reasoning, and fluency is related to variability in stride length and step width during dual-task treadmill walking in a sample of healthy adults. Nineteen healthy adults averaging 40 years of age were evaluated. Results indicated that processing speed was reduced, t(18) = 6.31, p = 0.0001, step width increased, t(18) = −8.00, p = 0.0001, and stride length decreased, t(18) = 3.06, p = 0.007, while dual tasking, but variability in gait parameters did not significantly change, consistent with a gait/posture-first approach. As hypothesized, better performance on a visual design fluency task which assesses cognitive flexibility was associated with less dual-task stride length variability, rs(17) = −0.43, p = 0.034, and step width variability, r = −0.56, p = 0.006. The results extend previous findings with older adults walking over ground and additionally suggest that cognitive flexibility may be important for gait maintenance while dual tasking. Full article
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<p>Scatterplot of Design Fluency versus Dual Task Stride Length Variability.</p>
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<p>Scatterplot of Design Fluency versus Dual Task Step Width Variability.</p>
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13 pages, 1093 KiB  
Article
Effects of Treadmill Inclination and Load Position on Gait Parameters while Carrying a Backpack Asymmetrically
by Magdalena Zawadka, Monika Maria Koncerewicz and Piotr Gawda
Appl. Sci. 2024, 14(18), 8148; https://doi.org/10.3390/app14188148 - 11 Sep 2024
Viewed by 259
Abstract
Incline walking with an external load is a common activity in everyday life. Asymmetrical load carriage can lead to abnormal posture and back pain. Thus, this study aimed to examine the effect of walking uphill with an asymmetrical load in two positions on [...] Read more.
Incline walking with an external load is a common activity in everyday life. Asymmetrical load carriage can lead to abnormal posture and back pain. Thus, this study aimed to examine the effect of walking uphill with an asymmetrical load in two positions on the spatiotemporal parameters of gait in young adults. Forty-one asymptomatic human volunteers were enrolled in this study. They were asked to walk at a self-selected pace on level and uphill (+5° incline) surfaces carrying a backpack in two asymmetrical positions (hand and shoulder). Spatiotemporal gait parameters were recorded using a photocell device. We observed a significant effect of incline and load position on gait parameters (p < 0.05). Although adaptation to walking on inclines was similar with and without a backpack, adaptation to load position was different when the load was hand-held and shoulder-held. Asymmetric loading with different load locations should be considered an important factor influencing daily gait patterns. In the future, this relationship should be further investigated in terms of pain disorders and postural abnormalities. Full article
(This article belongs to the Special Issue Human Biomechanics and EMG Signal Processing)
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<p>(<b>a</b>) Backpack with marked dimensions. Height, 38 cm (green line); width, 28 cm (red line); depth, 15 cm (yellow line). (<b>b</b>) Adjusting the backpack position to the participant so that the lower edge of the backpack was at the level of the upper part of the sacrum and the backpack fitted tightly to the back.</p>
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<p>Participant while walking on a treadmill: (<b>a</b>) carrying a backpack on the shoulder; (<b>b</b>) carrying a backpack in the hand.</p>
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<p>Plots show statistically significant (<span class="html-italic">p</span> &lt; 0.05) interactions between inclination and load position. Vertical bars denote 0.95 confidence intervals and markers denote means: (<b>a</b>) cadence; (<b>b</b>) gait cycle; (<b>c</b>) swing phase; (<b>d</b>) stance phase.</p>
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9 pages, 436 KiB  
Article
Long-Term Outcomes of Anterior Cruciate Ligament Reconstruction Based on Gait Analysis
by Dmitry Skvortsov, Alyona Altukhova, Sergey Kaurkin and Alexander Akhpashev
Diagnostics 2024, 14(17), 1977; https://doi.org/10.3390/diagnostics14171977 - 6 Sep 2024
Viewed by 502
Abstract
Background: Currently available studies on the long-term functional outcomes of anterior cruciate ligament (ACL) reconstruction have yielded conflicting results. The purpose of this study was to evaluate the biomechanical characteristics of walking in the long term after ACL reconstruction. Methods: The study included [...] Read more.
Background: Currently available studies on the long-term functional outcomes of anterior cruciate ligament (ACL) reconstruction have yielded conflicting results. The purpose of this study was to evaluate the biomechanical characteristics of walking in the long term after ACL reconstruction. Methods: The study included a test group of 18 patients (3.4 years from the date of ACL reconstruction on average) and a control group of 20 healthy subjects. Their gaits were assessed using functional tests at voluntary walking and fast-walking speeds. The biomechanical assessments utilized included spatiotemporal and kinematic parameters of walking, as well as surface electromyography (EMG) amplitudes of the main flexor-extensor muscles of the lower extremities. Results: Fast-walking speeds and the clearances of the operated-upon limbs in the patient group exceeded those in the control group. The gait cycle in the patient group was significantly longer when walking at a voluntary speed compared to the control group. In the patient group, most of the movements were symmetrical at both speeds, and the parameters did not differ from the control group. The only exception was the hip joint amplitude and the main amplitude of the knee joint flexion, which significantly and simultaneously increased when walking at a fast speed. Conclusions: In the postoperative period, at voluntary speeds, the patient group was characterized by increased amplitudes in the hip and knee joints and higher EMG amplitudes, which almost disappeared at higher speeds. Full article
(This article belongs to the Special Issue Recent Advances in the Diagnosis and Prognosis of Sports Injuries)
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<p>The pipeline diagram showing the process from sensor data acquisition to computer data processing.</p>
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8 pages, 1717 KiB  
Proceeding Paper
Gait Analysis and Fall Risk Assessment in Different Age Groups: A Comparative Study
by Thanaporn Sukpramote and Wongwit Senavongse
Eng. Proc. 2024, 74(1), 19; https://doi.org/10.3390/engproc2024074019 - 28 Aug 2024
Viewed by 136
Abstract
Daily walking reflects the quality of life concerning physical status and its association with the risk of falls. Abnormal walking can lead to injuries and increase the likelihood of future falls. It has been found that older adults are more prone to falls [...] Read more.
Daily walking reflects the quality of life concerning physical status and its association with the risk of falls. Abnormal walking can lead to injuries and increase the likelihood of future falls. It has been found that older adults are more prone to falls than younger persons. However, there is limited research on gait analysis in older adults. Thus, we analyzed gait parameters, involving 10 participants aged between 20 and 30 years old, and 10 participants aged 50 years and older, using the Gait Analysis System (LONGGOOD Meditech Ltd., Taipei, Taiwan), which automatically positions the human body and GaitBEST. GaitBEST is used for analyzing and calculating key timing points and displacement values from the Kinect detector as it captures the location of joint points and adjusts them to the program. After the gait testing, the result is displayed immediately. Each volunteer did not have any surgery that impacted their walking and signed a written informed consent statement before the study. The volunteers walked on a straight flat surface for 4.2 m, repeating the walking test five times at a self-determined comfortable speed. Subsequently, a comparative analysis of the gait parameter outcomes was performed using a parametric test by a t-test. The results showed the balance parameters of both groups significantly differed in the head sway range (p = 0.008), head tilt range (p = 0.018), and pelvis tilt range (p = 0.003). The younger group exhibited better postural control than the other group. The spatiotemporal parameters, stride length, and step length during walking were also significantly different at p = 0.001. This indicated that the older group had shorter lengths compared to the other group, leading to a significant difference in the percentage of falls and functional loss at p = 0.021 and 0.023, respectively. The result of this study assists in examining and assessing the physical condition, preventing falls, optimizing walking efficiency, preventing injuries, and reducing the falling risk. Full article
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<p>Straight flat floor walkways and starting and ending points for laboratory walking tests.</p>
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<p>Experiment with walking using the Gait Analysis System.</p>
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<p>Comparison of significant balance parameters, which includes head sway range, head tilt range, and pelvis tilt range between the volunteers aged 20–39 years group and volunteers aged 50 years and above group.</p>
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<p>Comparison of the significant spatiotemporal parameters, which includes stride length and step length between the volunteers aged 20–39 years group and volunteers aged 50 years and above group.</p>
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<p>Comparison of the significant health risk score parameters, which includes the risk of falling and functional loss between the volunteers aged 20–39 years group and volunteers aged 50 years and above group.</p>
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12 pages, 1677 KiB  
Article
Validity and Test–Retest Reliability of Spatiotemporal Running Parameter Measurement Using Embedded Inertial Measurement Unit Insoles
by Louis Riglet, Baptiste Orliac, Corentin Delphin, Audrey Leonard, Nicolas Eby, Paul Ornetti, Davy Laroche and Mathieu Gueugnon
Sensors 2024, 24(16), 5435; https://doi.org/10.3390/s24165435 - 22 Aug 2024
Viewed by 465
Abstract
Running is the basis of many sports and has highly beneficial effects on health. To increase the understanding of running, DSPro® insoles were developed to collect running parameters during tasks. However, no validation has been carried out for running gait analysis. The [...] Read more.
Running is the basis of many sports and has highly beneficial effects on health. To increase the understanding of running, DSPro® insoles were developed to collect running parameters during tasks. However, no validation has been carried out for running gait analysis. The aims of this study were to assess the test–retest reliability and criterion validity of running gait parameters from DSPro® insoles compared to a motion-capture system. Equipped with DSPro® insoles, a running gait analysis was performed on 30 healthy participants during overground and treadmill running using a motion-capture system. Using an intraclass correlation coefficient (ICC), the criterion validity and test–retest reliability of spatiotemporal parameters were calculated. The test–retest reliability shows moderate to excellent ICC values (ICC > 0.50) except for propulsion time during overground running at a fast speed with the motion-capture system. The criterion validity highlights a validation of running parameters regardless of speeds (ICC > 0.70). This present study validates the good criterion validity and test–retest reliability of DSPro® insoles for measuring spatiotemporal running gait parameters. Without the constraints of a 3D motion-capture system, such insoles seem to be helpful and relevant for improving the care management of active patients or following running performance in sports contexts. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring and Analysis)
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<p>DSPro<sup>®</sup> insole device.</p>
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<p>Bland–Altman plot for all running parameters during fast (black points) and comfortable (red points) overground running. Solid line = mean, dashed line = ±1.96 SD.</p>
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<p>Bland–Altman plot for all running parameters during fast (black points) and comfortable (red points) treadmill running. Solid line = mean, dashed line = ±1.96 SD.</p>
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13 pages, 2083 KiB  
Article
The Overlay, a New Solution for Volume Variations in the Residual Limb for Individuals with a Transtibial Amputation
by Pierre Badaire, Maxime T. Robert and Katia Turcot
Sensors 2024, 24(14), 4744; https://doi.org/10.3390/s24144744 - 22 Jul 2024
Viewed by 1426
Abstract
Background: The company Ethnocare has developed the Overlay, a new pneumatic solution for managing volumetric variations (VVs) of the residual limb (RL) in transtibial amputees (TTAs), which improves socket fitting. However, the impact of the Overlay during functional tasks and on the comfort [...] Read more.
Background: The company Ethnocare has developed the Overlay, a new pneumatic solution for managing volumetric variations (VVs) of the residual limb (RL) in transtibial amputees (TTAs), which improves socket fitting. However, the impact of the Overlay during functional tasks and on the comfort and pain felt in the RL is unknown. Methods: 8 TTAs participated in two evaluations, separated by two weeks. We measured compensatory strategies (CS) using spatio-temporal parameters and three-dimensional lower limb kinematics and kinetics during gait and sit-to-stand (STS) tasks. During each visit, the participant carried out our protocol while wearing the Overlay and prosthetic folds (PFs), the most common solution to VV. Between each task, comfort and pain felt were assessed using visual analog scales. Results: While walking, the cadence with the Overlay was 105 steps/min, while it was 101 steps/min with PFs (p = 0.021). During 35% and 55% of the STS cycle, less hip flexion was observed while wearing the Overlay compared to PFs (p = 0.004). We found asymmetry coefficients of 13.9% with the Overlay and 17% with PFs during the STS (p = 0.016) task. Pain (p = 0.031), comfort (p = 0.017), and satisfaction (p = 0.041) were better with the Overlay during the second visit. Conclusion: The Overlay’s impact is similar to PFs’ but provides less pain and better comfort. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Medical Applications)
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<p>On the left, the Overlay—On the right, the Overlay worn.</p>
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<p>(<b>A</b>) Sit-to-stand phases (Miramand et al., 2022 [<a href="#B5-sensors-24-04744" class="html-bibr">5</a>]) (<b>B</b>) Gait cycle phases (Perry et al., 1992 [<a href="#B23-sensors-24-04744" class="html-bibr">23</a>]).</p>
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<p>Kinematic on the sagittal plane for (<b>A</b>) the hip on the amputated side during the gait, (<b>B</b>) the hip on the amputated side during the STS, (<b>C</b>) the hip on the sound side during the STS. (<b>Left plot</b>) Kinematic under each condition. (<b>Right plot</b>) SPM Analysis, with the red dotted line as the significance threshold.</p>
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<p>(<b>Left plot</b>) Vertical ground reaction force on both sides during the STS. (<b>Right plot</b>) SPM Analysis.</p>
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<p>Pain and comfort evaluation for each task and during each visit. STS = sit to stand; 6MWT = six minute walking test; LS = long slope (6° incline); G = gravel; STEP = walking over a step; SS = short slope (13° incline).</p>
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11 pages, 2728 KiB  
Review
Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review
by Lorenzo Brognara
Geriatrics 2024, 9(4), 95; https://doi.org/10.3390/geriatrics9040095 - 18 Jul 2024
Viewed by 840
Abstract
Spatiotemporal parameters such as gait velocity and stride length are simple indicators of functional status and can be used to predict major adverse outcomes in older adults. A smartphone can be used for gait analysis by providing spatiotemporal parameters useful for improving the [...] Read more.
Spatiotemporal parameters such as gait velocity and stride length are simple indicators of functional status and can be used to predict major adverse outcomes in older adults. A smartphone can be used for gait analysis by providing spatiotemporal parameters useful for improving the diagnosis and rehabilitation processes in frail people. The aim of this study was to review articles published in the last 20 years (from 2004 to 2024) concerning the application of smartphones to assess the spatiotemporal parameters of gait in older adults. This systematic review was performed in line with Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA), and original articles were identified by searching seven electronic databases: SciVerse (ScienceDirect), Excerpta Medica Database (EMBASE), Medline, Scopus, PubMed, Web of Science and the Cochrane Library. Studies were rigorously screened using the inclusion criteria of smartphones and mobile apps, older adults and spatiotemporal gait parameters, and results were narratively synthesized. Seventy-three articles were initially identified while searching the scientific literature regarding this topic. Eleven articles were selected and included in this review. Analysis of these studies covered information about gait assessment using mobile apps recorded in 723 older adults and 164 control cases. Analysis of data related to the application of smartphones to assess spatiotemporal parameters of gait in older adults showed moderate-to-excellent test–retest reliability and validity (ICCs around 0.9) of gait speed, the most common parameter reported. Additionally, gait speeds recorded with mobile apps showed excellent agreement when compared to gold standard systems. Smartphones and mobile apps are useful, non-invasive, low-cost and objective tools that are being extensively used to perform gait analysis in older adults. Smartphones and mobile apps can reliably identify spatiotemporal parameters related to adverse outcomes, such as a slow gait speed, as predictors and outcomes in clinical practice and research involving older adults. Full article
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<p>PRISMA diagram: this figure represents the flow of study selection through identification, screening, eligibility and inclusion.</p>
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<p>Spatiotemporal parameters most frequently evaluated in selected articles.</p>
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<p>Smartphone placement: the corresponding position of the smartphone reported.</p>
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27 pages, 5687 KiB  
Article
Experimental Comparison between 4D Stereophotogrammetry and Inertial Measurement Unit Systems for Gait Spatiotemporal Parameters and Joint Kinematics
by Sara Meletani, Sofia Scataglini, Marco Mandolini, Lorenzo Scalise and Steven Truijen
Sensors 2024, 24(14), 4669; https://doi.org/10.3390/s24144669 - 18 Jul 2024
Viewed by 573
Abstract
(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 [...] Read more.
(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
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<p>Xsens sensors placement. Front (<b>left</b>) and back (<b>right</b>) views.</p>
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<p>Synchronization of both devices (Xsens) and Move4D in A-Pose.</p>
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<p>Foot center velocity with identification of step events [<a href="#B24-sensors-24-04669" class="html-bibr">24</a>]. The first dot minimum represents the time of the initial heel strike. In contrast, the final dot minimum is the final heel strike, so they mark the start at the end of the gait cycle. The absolute dotted maximum instead represents the toe-off.</p>
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<p>(<b>a</b>) Hip flexion (+)/extension (−); (<b>b</b>) knee flexion (+)/extension (−); (<b>c</b>) ankle dorsiflexion (+)/plantarflexion (−).</p>
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<p>Bland–Altman plots of stance and swing duration differences between measurements analyzed by Move4D and Xsens for the first trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For stance time, the mean difference is −0.025 s, while for swing time, it is −0.003 s.</p>
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<p>Bland–Altman plots of stance and swing percentage differences between measurements analyzed by Move4D and Xsens for the first trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. The mean difference for the stance percentage is −0.535%, while the swing percentage is 0.535%.</p>
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<p>Bland–Altman plots cycle time and stride length differences between measurements analyzed by Move4D and Xsens for the first trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For cycle time, the mean difference is −0.027 s, while for stride length, it is 0.272 m.</p>
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<p>Bland–Altman plots of stance and swing duration differences between measurements analyzed by Move4D and Xsens for the second trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For stance time, the mean difference is −0.006 s, while the mean of swing time is −0.001 s.</p>
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<p>Bland–Altman plots of stance and swing percentage differences between measurements analyzed by Move4D and Xsens for the second trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For the stance percentage, the mean difference is −0.089%, while for the swing percentage is 0.089%.</p>
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<p>Bland–Altman plots cycle time and stride length differences between measurements analyzed by Move4D and Xsens for the second trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For cycle time, the mean difference is −0.006 s, while for stride length, it is 0.344 m.</p>
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<p>Bland–Altman plots of stance and swing duration differences between measurements analyzed by Move4D and Xsens for the third trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For stance time, the mean difference is 0.016 s, while for swing time, it is −0.004 s.</p>
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<p>Bland–Altman plots of stance and swing percentage differences between measurements analyzed by Move4D and Xsens for the third trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For the stance percentage, the mean difference is 0.755%, while for the swing percentage is −0.755%.</p>
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<p>Bland–Altman plots cycle time and stride length differences between measurements analyzed by Move4D and Xsens for the third trial. The central red line represents the mean difference. In contrast, the upper and lower red lines represent the upper and lower limits of the 95% CI, respectively. For cycle time, the mean difference is 0.011 s, while for stride length, it is 0.308 m.</p>
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14 pages, 1673 KiB  
Review
Concurrent Validity Evidence for Pressure-Sensing Walkways Measuring Spatiotemporal Features of Gait: A Systematic Review and Meta-Analysis
by Ozell Sanders, Bin Wang and Kimberly Kontson
Sensors 2024, 24(14), 4537; https://doi.org/10.3390/s24144537 - 13 Jul 2024
Cited by 1 | Viewed by 558
Abstract
Technologies that capture and analyze movement patterns for diagnostic or therapeutic purposes are a major locus of innovation in the United States. Several studies have evaluated their measurement properties in different conditions with variable findings. To date, the authors are not aware of [...] Read more.
Technologies that capture and analyze movement patterns for diagnostic or therapeutic purposes are a major locus of innovation in the United States. Several studies have evaluated their measurement properties in different conditions with variable findings. To date, the authors are not aware of any systematic review of studies conducted to assess the concurrent validity of pressure-sensing walkway technologies. The results of such an analysis could establish the body of evidence needed to confidently use these systems as reference or gold-standard systems when validating novel tools or measures. A comprehensive search of electronic databases including MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was performed. The initial search yielded 7670 papers. After removing duplicates and applying study inclusion/exclusion criteria, 11 papers were included in the systematic review with 10 included in a meta-analysis. There were 25 spatial and temporal gait parameters extracted from the included studies. The results showed there was not a significant bias for nearly all spatiotemporal gait parameters when the walkway system was compared to the reference systems. The findings from this analysis should provide confidence in using the walkway systems as reference systems in future studies to support the evaluation and validation of novel technologies deriving gait parameters. Full article
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<p>PRISMA flow diagram of identification, screening, and inclusion of articles for meta-analysis.</p>
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<p>Forest plots of eight gait parameters: (<b>A</b>) gait speed (m/s), (<b>B</b>) cadence (steps/min), (<b>C</b>) stride Length (m), (<b>D</b>) stride Time (s), (<b>E</b>) step length (cm), (<b>F</b>) step Time (s), (<b>G</b>) single support (%), (<b>H</b>) double support (%).</p>
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<p>Forest plots of eight gait parameters: (<b>A</b>) gait speed (m/s), (<b>B</b>) cadence (steps/min), (<b>C</b>) stride Length (m), (<b>D</b>) stride Time (s), (<b>E</b>) step length (cm), (<b>F</b>) step Time (s), (<b>G</b>) single support (%), (<b>H</b>) double support (%).</p>
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11 pages, 1221 KiB  
Article
Femoral Translation in Patients with Unicompartmental Osteoarthritis—A Cohort Study
by Mathis Wegner, Simon Kuwert, Stefan Kratzenstein, Maciej J. K. Simon and Babak Moradi
Biomechanics 2024, 4(3), 428-438; https://doi.org/10.3390/biomechanics4030029 - 12 Jul 2024
Viewed by 644
Abstract
The use of three-dimensional (3D) gait analysis to image femorotibial translation can aid in the diagnosis of pathology and provide additional insight into the severity of KOA (knee osteoarthritis). Femorotibial translation is of particular importance in patients undergoing UKA (unicompartmental knee arthroplasty), as [...] Read more.
The use of three-dimensional (3D) gait analysis to image femorotibial translation can aid in the diagnosis of pathology and provide additional insight into the severity of KOA (knee osteoarthritis). Femorotibial translation is of particular importance in patients undergoing UKA (unicompartmental knee arthroplasty), as the absence or elongation of ligamentous structures results in changes in the kinematic alignment. The aim of the study was to evaluate the parameters of femorotibial translation in patients with MOA (medial unicompartmental OA). An artificial model was employed to develop a method for calculating femorotibial translation in vitro. In a prospective cohort study, gait data using three-dimensional gait analysis were collected from 11 patients (68.73 ± 9.22 years) with severe OA scheduled for UKA and 29 unmatched healthy participants (22.07 ± 2.23 years). The discrete variables characterising femorotibial translation were compared for statistical significance (p < 0.05) using the Student’s t-test and the Mann–Whitney U-test. The results of the study validated an artificial model to mimic femorotibial translation. The comparison of patients scheduled for UKA and a healthy unmatched control group showed no statistically significant differences concerning femorotibial translation in all three planes (p > 0.05). However, the PROMs (patient-reported outcome measures), spatiotemporal, and kinematic parameters showed statistically significant differences between the groups (p < 0.001). The data presented here demonstrate typical changes in PROMs as well as spatiotemporal and kinematic outcomes for MOA as seen in knee OA. The results of the clinical gait analyses demonstrate individualised femorotibial translation. The extent of individual femorotibial translation may prove to be an important parameter for altered joint kinematics in patients with MOA, especially prior to UKA implantation. Full article
(This article belongs to the Section Injury Biomechanics and Rehabilitation)
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<p>Marker system with the virtual tibial marker (blue).</p>
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<p>Reflecting markers used for gait analysis: (<b>a</b>) frontal view; (<b>b</b>) sagittal view.</p>
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<p>Femorotibial translation in the loading response phase (first 12% of the gait cycle) in the anterior–posterior plane of the case–control study: (<b>a</b>) control group; (<b>b</b>) MOA group.</p>
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17 pages, 1598 KiB  
Article
Cautious Gait during Navigational Tasks in People with Hemiparesis: An Observational Study
by Albane Le Roy, Fabien Dubois, Nicolas Roche, Helena Brunel and Céline Bonnyaud
Sensors 2024, 24(13), 4241; https://doi.org/10.3390/s24134241 - 29 Jun 2024
Viewed by 593
Abstract
Locomotor and balance disorders are major limitations for subjects with hemiparesis. The Timed Up and Go (TUG) test is a complex navigational task involving oriented walking and obstacle circumvention. We hypothesized that subjects with hemiparesis adopt a cautious gait during complex locomotor tasks. [...] Read more.
Locomotor and balance disorders are major limitations for subjects with hemiparesis. The Timed Up and Go (TUG) test is a complex navigational task involving oriented walking and obstacle circumvention. We hypothesized that subjects with hemiparesis adopt a cautious gait during complex locomotor tasks. The primary aim was to compare spatio-temporal gait parameters, indicators of cautious gait, between the locomotor subtasks of the TUG (Go, Turn, Return) and a Straight-line walk in people with hemiparesis. Our secondary aim was to analyze the relationships between TUG performance and balance measures, compare spatio-temporal gait parameters between fallers and non-fallers, and identify the biomechanical determinants of TUG performance. Biomechanical parameters during the TUG and Straight-line walk were analyzed using a motion capture system. A repeated measures ANOVA and two stepwise ascending multiple regressions (with performance variables and biomechanical variables) were conducted. Gait speed, step length, and % single support phase (SSP) of the 29 participants were reduced during Turn compared to Go and Return and the Straight-line walk, and step width and % double support phase were increased. TUG performance was related to several balance measures. Turn performance (R2 = 63%) and Turn trajectory deviation followed by % SSP on the paretic side and the vertical center of mass velocity during Go (R2 = 71%) determined TUG performance time. People with hemiparesis adopt a cautious gait during complex navigation at the expense of performance. Full article
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<p>Experimental procedure.</p>
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<p>Illustration of the data processing with the Mokka software.</p>
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<p>Illustration of the comparison of spatio-temporal gait parameters between the TUG subtasks (Go, Turn, and Return) and the Straight-line walk in people with hemiparesis.</p>
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13 pages, 780 KiB  
Article
Post-Arthroplasty Spatiotemporal Gait Parameters in Patients with Hip Osteoarthritis or Developmental Dysplasia of the Hip: An Observational Study
by Sophia Stasi, Georgios Papagiannis, Athanasios Triantafyllou, Panayiotis Papagelopoulos and Panagiotis Koulouvaris
J. Funct. Morphol. Kinesiol. 2024, 9(3), 110; https://doi.org/10.3390/jfmk9030110 - 25 Jun 2024
Viewed by 1274
Abstract
Total hip arthroplasty (THA) is a preferred treatment for primary osteoarthritis (OA) or secondary degenerative arthropathy due to developmental hip dysplasia (DDH). Gait analysis is considered a gold standard for evaluating post-arthroplasty walking patterns. This study compared post-THA spatiotemporal gait parameters (SGPs) between [...] Read more.
Total hip arthroplasty (THA) is a preferred treatment for primary osteoarthritis (OA) or secondary degenerative arthropathy due to developmental hip dysplasia (DDH). Gait analysis is considered a gold standard for evaluating post-arthroplasty walking patterns. This study compared post-THA spatiotemporal gait parameters (SGPs) between OA and DDH patients and explored correlations with demographic and clinical variables. Thirty patients (15 per group) were recorded during gait and their SGPs were analyzed. Functionality was evaluated with the Oxford Hip Score (OHS). The OA patients were significantly older than DDH patients (p < 0.005). Significant and moderate to strong were the correlations between SGPs, age, and four items of the OHS concerning hip pain and activities of daily life (0.31 < Pearson’s r < 0.51 all p < 0.05). Following THA, both groups exhibited similar levels of the examined gait parameters. Post-arthroplasty SGPs and OHS correlations indicate limitations in certain activities. Given the absence of pre-operative data and the correlation between age and SGPs and OHS, ANCOVA testing revealed that age adjusts OHS and SGP values, while pre-operative diagnosis has no main effect. These findings indicate that hip OA or DDH do not affect postoperative SGPs and patients’ functionality. Future studies should examine both kinematic and kinetic data to better evaluate the post-THA gait patterns of OA and DDH patients. Full article
(This article belongs to the Special Issue Biomechanical Analysis in Physical Activity and Sports)
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<p>The ViconNexus software version 2.3 figure of data acquisition procedure, depicting laboratory dimensions, six optoelectronic cameras, and two force plates).</p>
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<p>This study’s flow diagram.</p>
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17 pages, 1988 KiB  
Article
Discrete Relationships between Spatiotemporal Gait Characteristics and Domain-Specific Neuropsychological Performance in Midlife
by Laura Morrison, Adam H. Dyer, Helena Dolphin, Isabelle Killane, Nollaig M. Bourke, Matthew Widdowson, Conor P. Woods, James Gibney, Richard B. Reilly and Sean P. Kennelly
Sensors 2024, 24(12), 3903; https://doi.org/10.3390/s24123903 - 17 Jun 2024
Viewed by 839
Abstract
Midlife risk factors such as type 2 diabetes mellitus (T2DM) confer a significantly increased risk of cognitive impairment in later life with executive function, memory, and attention domains often affected first. Spatiotemporal gait characteristics are emerging as important integrative biomarkers of neurocognitive function [...] Read more.
Midlife risk factors such as type 2 diabetes mellitus (T2DM) confer a significantly increased risk of cognitive impairment in later life with executive function, memory, and attention domains often affected first. Spatiotemporal gait characteristics are emerging as important integrative biomarkers of neurocognitive function and of later dementia risk. We examined 24 spatiotemporal gait parameters across five domains of gait previously linked to cognitive function on usual-pace, maximal-pace, and cognitive dual-task gait conditions in 102 middle-aged adults with (57.5 ± 8.0 years; 40% female) and without (57.0 ± 8.3 years; 62.1% female) T2DM. Neurocognitive function was measured using a neuropsychological assessment battery. T2DM was associated with significant changes in gait phases and rhythm domains at usual pace, and greater gait variability observed during maximal pace and dual tasks. In the overall cohort, both the gait pace and rhythm domains were associated with memory and executive function during usual pace. At maximal pace, gait pace parameters were associated with reaction time and delayed memory. During the cognitive dual task, associations between gait variability and both delayed memory/executive function were observed. Associations persisted following covariate adjustment and did not differ by T2DM status. Principal components analysis identified a consistent association of slower gait pace (step/stride length) and increased gait variability during maximal-pace walking with poorer memory and executive function performance. These data support the use of spatiotemporal gait as an integrative biomarker of neurocognitive function in otherwise healthy middle-aged individuals and reveal discrete associations between both differing gait tasks and gait domains with domain-specific neuropsychological performance. Employing both maximal-pace and dual-task paradigms may be important in cognitively unimpaired populations with risk factors for later cognitive decline—with the aim of identifying individuals who may benefit from potential preventative interventions. Full article
(This article belongs to the Section Biomedical Sensors)
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<p>Spatiotemporal Gait Characteristics by T2DM Status. Participants were instructed to walk at their “usual” (or “normal”) pace, “maximal” (or “fast”) pace and again at their normal pace with the addition of a cognitive dual task (serial 7s). Following previous approaches, 24 spatiotemporal gait characteristics were extracted from the GaitRite™ automatic walkway. Radar plots illustrate the median z-score for each domain by group. (<b>A</b>–<b>D</b>) Radar plots illustrate the median value as a z-score for T2DM and HC groups. Differences between groups were assessed using Wilcoxon rank-sum tests. (<a href="#app1-sensors-24-03903" class="html-app">Supplementary Table S1</a>). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Principal Components Analysis of Spatiotemporal Gait Characteristics Across Usual-Pace, Maximal-Pace, and Cognitive Dual-Task Conditions. Principal components analysis (PCA) was performed separately for each walk—usual pace, maximal pace, cognitive dual-task pace, and dual-task cost for each parameter computed. The first three PCA components explained 61.4%, 62.2%, and 70.4% of the variance across the three tasks and 67.1% of the variance in dual-task cost. Eigenvalues and % variance explained for each component are listed above a breakdown of gait parameters contributing to each component. Full names for each gait domain acronym are provided above in <a href="#sensors-24-03903-f001" class="html-fig">Figure 1</a>. Data are coloured from red (indicating the strongest negative contributions within each PCA) to green (indicating the strongest positive contributions within each PCA). PC1: Principal Component 1; PC2: Principal Component 2; PC3: Principal Component 3; Cog.: cognitive; Eigen.: eigenvalue; Var. %: variance % explained.</p>
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<p>Association Between Maximal Pace Principal Component 2 and Neuropsychological Performance. Scatterplots demonstrate the association between maximal-pace PC2 score and z-scored performance on each neuropsychological test. Linear regression results as given in <a href="#sensors-24-03903-t002" class="html-table">Table 2</a> above are provided for unadjusted and adjusted models. In the unadjusted model, PC2 was the independent variable and neuropsychological test z-score the dependent variable. The adjusted model applied adjustment for age, sex, body mass index, education and type 2 diabetes mellitus status. The results are indicated in the top left of each graph and presented as beta coefficients (β) with corresponding 95% confidence intervals (95% CIs). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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27 pages, 1416 KiB  
Systematic Review
Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis
by Sofia Scataglini, Eveline Abts, Cas Van Bocxlaer, Maxime Van den Bussche, Sara Meletani and Steven Truijen
Sensors 2024, 24(11), 3686; https://doi.org/10.3390/s24113686 - 6 Jun 2024
Cited by 1 | Viewed by 1468
Abstract
(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to [...] Read more.
(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations. Full article
(This article belongs to the Section Environmental Sensing)
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<p>Prisma Flow Chart.</p>
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<p>Meta-analysis data for inter-rater reliability and concurrent validity (ICC) [<a href="#B31-sensors-24-03686" class="html-bibr">31</a>,<a href="#B33-sensors-24-03686" class="html-bibr">33</a>,<a href="#B34-sensors-24-03686" class="html-bibr">34</a>,<a href="#B35-sensors-24-03686" class="html-bibr">35</a>].</p>
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15 pages, 1554 KiB  
Article
The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability
by Pnina Marom, Michael Brik, Nirit Agay, Rachel Dankner, Zoya Katzir, Naama Keshet and Dana Doron
Sensors 2024, 24(11), 3594; https://doi.org/10.3390/s24113594 - 2 Jun 2024
Viewed by 906
Abstract
An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower [...] Read more.
An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen’s d, comparing the application’s readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89–1) and good-to-excellent reliability (ICC range, 0.77–1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58–1). Only ‘step length of the impaired leg’ showed poor-to-good validity (range, 0.37–0.84). Cohen’s d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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<p>Description of data collection procedure. During each walking session, three devices concurrently recorded the participants’ spatiotemporal gait parameters: (<b>a</b>) A phone with the app, placed inside the pocket of the impaired leg; (<b>b</b>) A phone with the app, placed inside the pocket of the opposite leg; (<b>c</b>) The C-Mill treadmill. Each participant was required to wear a standard safety harness. The use of handrails was permitted.</p>
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<p>Bland–Altman plots: limits of agreement between the OneStep smartphone application and the treadmill, as measured on each leg: (<b>a</b>,<b>b</b>) Step length of the impaired/opposite leg; (<b>c</b>,<b>d</b>) Swing phase of the impaired/opposite leg; (<b>e</b>,<b>f</b>) Stance phase of the impaired/opposite leg; and (<b>g</b>,<b>h</b>) Single limb support of the impaired/opposite leg. The yellow and gray lines indicate the upper and lower 95% limits of agreement (±1.96 SD of the bias).</p>
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<p>Bland–Altman plots: limits of agreement between the OneStep smartphone application and the treadmill, as measured on each leg: (<b>a</b>,<b>b</b>) Step length of the impaired/opposite leg; (<b>c</b>,<b>d</b>) Swing phase of the impaired/opposite leg; (<b>e</b>,<b>f</b>) Stance phase of the impaired/opposite leg; and (<b>g</b>,<b>h</b>) Single limb support of the impaired/opposite leg. The yellow and gray lines indicate the upper and lower 95% limits of agreement (±1.96 SD of the bias).</p>
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<p>Bland–Altman plots: limits of agreement between the OneStep smartphone application and the treadmill, as measured on each leg: (<b>a</b>,<b>b</b>) Step length of the impaired/opposite leg; (<b>c</b>,<b>d</b>) Swing phase of the impaired/opposite leg; (<b>e</b>,<b>f</b>) Stance phase of the impaired/opposite leg; and (<b>g</b>,<b>h</b>) Single limb support of the impaired/opposite leg. The yellow and gray lines indicate the upper and lower 95% limits of agreement (±1.96 SD of the bias).</p>
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<p>Bland–Altman plots: limits of agreement between the OneStep smartphone application and the treadmill, as measured on each leg: (<b>a</b>,<b>b</b>) Step length of the impaired/opposite leg; (<b>c</b>,<b>d</b>) Swing phase of the impaired/opposite leg; (<b>e</b>,<b>f</b>) Stance phase of the impaired/opposite leg; and (<b>g</b>,<b>h</b>) Single limb support of the impaired/opposite leg. The yellow and gray lines indicate the upper and lower 95% limits of agreement (±1.96 SD of the bias).</p>
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