Dataset Information

GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait.

ABSTRACT: The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. Therefore, we present GAITREC, a comprehensive and completely annotated large-scale dataset containing bi-lateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bi-lateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.

SUBMITTER: Horsak B

PROVIDER: S-EPMC7217853 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait.

Horsak Brian B Slijepcevic Djordje D Raberger Anna-Maria AM Schwab Caterine C Worisch Marianne M Zeppelzauer Matthias M

Scientific data 20200512 1

The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated ...[more]

PMID: 32398644

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Project description:IntroductionThe incidence of osteopenia and osteoporosis is of concern in adults with Prader-Willi syndrome (PWS). Walking generates reaction forces that could stimulate bone mineralization and is popular in people with PWS. This study compared bone parameters and ground reaction forces (GRF) during gait between young adults with PWS and without PWS and explored associations between bone and GRFs during gait.Methods10 adults with PWS, 10 controls with obesity (OB) and 10 with normal weight (NW) matched on sex participated. Segmental and full body dual-energy x-ray absorptiometry scans provided femoral neck, spine, total body minus the head bone mineral density (BMD), bone mineral content (BMC). Vertical GRF, vertical impulse, posterior force and negative impulse were measured during 5 walking trials at a self-selected speed along a 10 m runway.ResultsMultivariate analyses of variance showed that adults with PWS (n = 7-8) had hip and body BMD and BMC comparable (p > .050) to NW and lower (p < .050) than OB. Adults with PWS showed slower speed than NW (p < .050) but similar to OB (p > .050). Adults with PWS presented lower absolute vertical GRF, vertical impulse and negative impulse than OB (p < .050). Pearson r correlations (p < .050) in those with PWS (n = 7-8) indicated that femoral neck BMC was associated with vertical GRF (r = 0.716), vertical impulse (r = 0.780), posterior force (r = -0.805), and negative impulse (r = -0.748). Spine BMC was associated with speed (r = 0.829) and body BMD was associated with speed (r = 0.893), and posterior force (r = -0.780).ConclusionsIncreased BMC in the femoral neck and body were associated with larger breaking forces during walking, a phenomenon normally observed at greater gait speeds. Faster walking speed was associated with greater BMC in the spine and body. Our preliminary results suggest that young adults with PWS could potentially benefit from faster walking for bone health; however, larger prospective studies are needed to confirm this.

Project description:Gait analysis is a systematic study of human locomotion, which can be utilized in variousapplications, such as rehabilitation, clinical diagnostics and sports activities. The various limitationssuch as cost, non-portability, long setup time, post-processing time etc., of the current gait analysistechniques have made them unfeasible for individual use. This led to an increase in research interestin developing smart insoles where wearable sensors can be employed to detect vertical groundreaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortablefor gait analysis, and can monitor plantar pressure frequently through embedded sensors thatconvert the applied pressure to an electrical signal that can be displayed and analyzed further.Several research teams are still working to improve the insoles' features such as size, sensitivity ofinsoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait bydetecting various complications providing recommendation to enhance walking performance. Eventhough systematic sensor calibration approaches have been followed by different teams to calibrateinsoles' sensor, expensive calibration devices were used for calibration such as universal testingmachines or infrared motion capture cameras equipped in motion analysis labs. This paper providesa systematic design and characterization procedure for three different pressure sensors: forcesensitiveresistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that canbe used for detecting vGRF using a smart insole. A simple calibration method based on a load cellis presented as an alternative to the expensive calibration techniques. In addition, to evaluate theperformance of the different sensors as a component for the smart insole, the acquired vGRF fromdifferent insoles were used to compare them. The results showed that the FSR is the most effectivesensor among the three sensors for smart insole applications, whereas the piezoelectric sensors canbe utilized in detecting the start and end of the gait cycle. This study will be useful for any researchgroup in replicating the design of a customized smart insole for gait analysis.

Project description:PURPOSE:The association between lower-extremity loading and clinically relevant knee symptoms at different time points after anterior cruciate ligament reconstruction (ACLR) is unclear. Vertical ground reaction force (vGRF) from walking was compared between individuals with and without clinically relevant knee symptoms in three cohorts: <12 months post-ACLR, 12-24 months post-ACLR, and >24 months post-ACLR. METHODS:One hundred twenty-eight individuals with unilateral ACLR were classified as symptomatic or asymptomatic, based on previously defined cutoff values for the Knee Osteoarthritis and Injury Outcome Score (<12 months post-ACLR [symptomatic n = 28, asymptomatic n = 24]; 12-24 months post-ACLR [symptomatic n = 15, asymptomatic n = 15], and >24 months post-ACLR [symptomatic, n = 13; asymptomatic, n = 33]). Vertical ground reaction force exerted on the ACLR limb was collected during walking gait, and functional analyses of variance were used to evaluate the effects of symptoms and time post-ACLR on vGRF throughout stance phase (? = 0.05). RESULTS:Symptomatic individuals, <12 months post-ACLR, demonstrated less vGRF during both vGRF peaks (i.e., weight acceptance and propulsion) and greater vGRF during midstance, compared to asymptomatic individuals. Vertical ground reaction force characteristics were not different between symptomatic and asymptomatic individuals for most of stance in individuals between 12 and 24 months post-ACLR. Symptomatic individuals who were >24 months post-ACLR, exhibited greater vGRF during both peaks, but lesser vGRF during midstance, compared to asymptomatic individuals. CONCLUSION:Relative to asymptomatic individuals, symptomatic individuals are more likely to underload the ACLR limb early after ACLR (i.e., <12 months) during both vGRF peaks, but overload the ACLR limb, during both vGRF peaks, at later time points (i.e., >24 months). We propose these differences in lower-extremity loading during walking might have implications for long-term knee health, and should be considered when designing therapeutic interventions for individuals with an ACLR.

Dataset Information

GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait.

Publications

GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait.

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