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A data set with kinematic and ground reaction forces of human balance.

ABSTRACT: This article describes a public data set containing the three-dimensional kinematics of the whole human body and the ground reaction forces (with a dual force platform setup) of subjects who were standing still for 60 s in different conditions, in which the subjects' vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject's sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics contain the three-dimensional positions of 42 markers that were placed on each subject's body and 73 calculated joint angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI: 10.6084/m9.figshare.4525082), and a companion Jupyter Notebook presents programming code to access the data set, generate analyses and other examples. The availability of a public data set on the Internet that contains these measurements and information about how to access and process this data can potentially boost the research on human postural control, increase the reproducibility of studies, and be used for training and education, among other applications.

SUBMITTER: Dos Santos DA

PROVIDER: S-EPMC5534162 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A data set with kinematic and ground reaction forces of human balance.

Dos Santos Damiana A DA Fukuchi Claudiane A CA Fukuchi Reginaldo K RK Duarte Marcos M

PeerJ 20170727

This article describes a public data set containing the three-dimensional kinematics of the whole human body and the ground reaction forces (with a dual force platform setup) of subjects who were standing still for 60 s in different conditions, in which the subjects' vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data ...[more]

PMID: 28761798

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Project description:BackgroundThe anterior-posterior ground reaction force (AP-GRF) and propulsion and braking point metrics derived from the AP-GRF time series are indicators of locomotor function across healthy and neurological diagnostic groups. In this paper, we describe the use of a minimal set of wearable inertial measurement units (IMUs) to indirectly measure the AP-GRFs generated during healthy and hemiparetic walking.MethodsTen healthy individuals and five individuals with chronic post-stroke hemiparesis completed a 6-minute walk test over a walking track instrumented with six forceplates while wearing three IMUs securely attached to the pelvis, thigh, and shank. Subject-specific models driven by IMU-measured thigh and shank angles and an estimate of body acceleration provided by the pelvis IMU were used to generate indirect estimates of the AP-GRF time series. Propulsion and braking point metrics (i.e., peaks, peak timings, and impulses) were extracted from the IMU-generated time series. Peaks and impulses were expressed as % bodyweight (%bw) and peak timing was expressed as % stance phase (%sp). A 75%-25% split of 6-minute walk test data was used to train and validate the models. Indirect estimates of the AP-GRF time series and point metrics were compared to direct measurements made by the forceplates.ResultsIndirect measurements of the AP-GRF time series approximated the direct measurements made by forceplates, with low error and high consistency in both the healthy (RMSE= 4.5%bw; R2= 0.93) and post-stroke (RMSE= 2.64%bw; R2= 0.90) cohorts. In the healthy cohort, the average errors between indirect and direct measurements of the peak propulsion magnitude, peak propulsion timing, and propulsion impulse point estimates were 2.37%bw, 0.67%sp, and 0.43%bw. In the post-stroke cohort, the average errors for these point estimates were 1.07%bw, 1.27%sp, and 0.31%bw. Average errors for the braking estimates were higher, but comparable.ConclusionsAccurate estimates of AP-GRF metrics can be generated using three strategically mounted IMUs and subject-specific calibrations. This study advances the development of point-of-care diagnostic systems that can catalyze the routine assessment and management of propulsion and braking locomotor deficits during rehabilitation.

Project description:IntroductionGait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy.MethodsThe method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements.ResultsThe results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71-0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation.ConclusionTo conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.

Project description:BACKGROUND:Sand is an easy-to-access, cost-free resource that can be used to treat pronated feet (PF). Therefore, the aims of this study were to contrast the effects of walking on stable ground versus walking on sand on ground reaction forces (GRFs) and electromyographic (EMG) activity of selected lower limb muscles in PF individuals compared with healthy controls. METHODS:Twenty-nine controls aged 22.2±2.5 years and 30 PF individuals aged 22.2±1.9 years were enrolled in this study. Participants walked at preferred speed and in randomized order over level ground and sand. A force plate was included in the walkway to collect GRFs. Muscle activities were recorded using EMG system. RESULTS:No statistically significant between-group differences were found in preferred walking speed when walking on stable ground (PF: 1.33±0.12 m/s; controls: 1.35±0.14 m/s; p = 0.575; d = 0.15) and sand (PF: 1.19±0.11 m/s; controls: 1.23±0.18 m/s; p = 0.416; d = 0.27). Irrespective of the group, walking on sand (1.21±0.15 m/s) resulted in significantly lower gait speed compared with stable ground walking (1.34±0.13 m/s) (p<0.001; d = 0.93). Significant main effects of "surface" were found for peak posterior GRFs at heel contact, time to peak for peak lateral GRFs at heel contact, and peak anterior GRFs during push-off (p<0.044; d = 0.27-0.94). Pair-wise comparisons revealed significantly smaller peak posterior GRFs at heel contact (p = 0.005; d = 1.17), smaller peak anterior GRFs during push-off (p = 0.001; d = 1.14), and time to peak for peak lateral GRFs (p = 0.044; d = 0.28) when walking on sand. No significant main effects of "group" were observed for peak GRFs and their time to peak (p>0.05; d = 0.06-1.60). We could not find any significant group by surface interactions for peak GRFs and their time to peak. Significant main effects of "surface" were detected for anterior-posterior impulse and peak positive free moment amplitude (p<0.048; d = 0.54-0.71). Pair-wise comparisons revealed a significantly larger peak positive free moment amplitude (p = 0.010; d = 0.71) and a lower anterior-posterior impulse (p = 0.048; d = 0.38) when walking on sand. We observed significant main effects of "group" for the variable loading rate (p<0.030; d = 0.59). Pair-wise comparisons revealed significantly lower loading rates in PF compared with controls (p = 0.030; d = 0.61). Significant group by surface interactions were observed for the parameter peak positive free moment amplitude (p<0.030; d = 0.59). PF individuals exhibited a significantly lower peak positive free moment amplitude (p = 0.030, d = 0.41) when walking on sand. With regards to EMG, no significant main effects of "surface", main effects of "group", and group by surface interactions were observed for the recorded muscles during the loading and push-off phases (p>0.05; d = 0.00-0.53). CONCLUSIONS:The observed lower velocities during walking on sand compared with stable ground were accompanied by lower peak positive free moments during the push-off phase and loading rates during the loading phase. Our findings of similar lower limb muscle activities during walking on sand compared with stable ground in PF together with lower free moment amplitudes, vertical loading rates, and lower walking velocities on sand may indicate more relative muscle activity on sand compared with stable ground. This needs to be verified in future studies.

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A data set with kinematic and ground reaction forces of human balance.

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A data set with kinematic and ground reaction forces of human balance.

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