Gait Parameters Estimated Using Inertial Measurement Units

Croce, Ugo Della; Cereatti, Andrea; Mancini, Martina

doi:10.1007/978-3-319-14418-4_163

Cited by 12 publications

(13 citation statements)

References 128 publications

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“…It has been shown that gait disturbances are an early indicator for mild cognitive impairment (MCI) and can predict progression from MCI to Alzheimer’s disease [ 5 ]. Furthermore, gait performance is also a predictor of fall status [ 6 , 7 ], morbidity and mortality [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Objective measures of the temporal and spatial parameters of gait allow to define the level of impairment and to characterize functional gait performance, which can serve as a biomarker of mobility [ 4 , 6 , 10 ]. The computation of the spatio-temporal parameters requires, for each gait cycle, the identification of specific gait events (GEs).…”

Section: Introductionmentioning

confidence: 99%

“…While the latter two technologies are relatively expensive and require a controlled and dedicated environment, lengthy set-up and post-processing time, the other two options are comparatively inexpensive and easy to use. In particular, magneto-inertial measurement units (MIMUs) have been frequently presented as an affordable solution to assess gait parameters in a variety of environments [ 4 , 6 , 11 , 12 ]. However, the accuracy of the gait spatio-temporal parameters obtained using MIMUs can vary remarkably depending on the algorithms used to detect ICs and FCs and estimate distances [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults

et al. 2018

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BackgroundThe use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat.ResultsLimits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were − 27 to 27 ms for Stride Time, − 68 to 44 ms for Stance Time, − 31 to 31 ms for Step Time and − 67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (< 30 ms). Stride length mean absolute errors were on average 2% (≈ 25 mm). Error comparisons across centers did not show any significant difference. Significant error differences were found exclusively for stride and step durations between healthy elderly and Parkinsonian subjects, and for the stride length between walking speeds.ConclusionsThe TEADRIP method was effectively validated on a large number of healthy and pathological subjects recorded in four different clinical centers. Results showed that the spatio-temporal parameters estimation errors were consistent with those previously found on smaller population samples in a single center. The combination of robustness and range of applicability suggests the use of the TEADRIP as a suitable MIMU-based method for gait spatio-temporal parameter estimate in the routine clinical use. The present paper was awarded the “SIAMOC Best Methodological Paper 2017”.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults

et al. 2018

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“…PROs are an important basis for assessing the efficacy of clinical interventions [3], yet these scores are subjective, biased and infrequently collected. Recently, wearable accelerometers have been increasingly used to monitor and quantify PA [4] and gait parameters [5], and for activity recognition [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Patient-Reported Physical Activity Scores from Wearable Accelerometer Data: A Feasibility Study

Bahej

Clay

Jäggi

et al. 2018

Biosystems &Amp; Biorobotics

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Many diseases are characterized by limitations in mobility, including a wide range of musculoskeletal and neurological conditions. Reduced mobility impacts a patients ability to perform activities of daily living, which in turn reduces health-related quality of life. Mobility can be assessed by collecting patient-reported outcome scores from standardized questionnaires and by directly measuring physical activity parameters from wearable accelerometer data. In this work, we explored the relationship between subjectively and objectively measured mobility by training machine learning models to predict patient responses based on features derived from realworld acceleration data. Our method achieved up to 82% accuracy using a random forest classifier and set the basis to develop novel data-driven digital biomarkers for objective, quantitative and more frequent evaluation of patients' mobility.

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“…Instrumented motor tests are nowadays widely accepted [12]. In particular, the assessment of turning with inertial sensors allows investigating complex locomotor patterns and gathering additional insights on motor control [13].…”

Section: Introductionmentioning

confidence: 99%

An objective assessment to investigate the impact of turning angle on freezing of gait in Parkinson's disease

Bertoli

Cereatti

Croce

et al. 2017

2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Freezing of gait (FoG) is often described in subjects with Parkinson's disease (PD) as a sudden inability to continue the forward walking progression. FoG occurs most often during turning, especially at sharp angles. Here, we investigated 180 and 360 degrees turns in two groups: PD subjects reporting FoG (FoG+), and PD subjects without FoG (FoG-). Forty-three subjects (25 FoG+, 18 FoG-) wore an inertial sensor on their back while walking back and forth continuously for 2 min (reversing direction with a 180° turn), and while turning in place for 1 min (alternating 360° turning in opposite directions). Objective measures (turn duration, peak velocity, jerkiness and range of acceleration) were computed during the turns and compared across FoG+ and FoG-groups. Results showed that FoG+ compared to FoG-took significantly a longer time to complete 360° turns than 180° turns. A significant lower turn peak velocity, higher jerkiness and an increased range of medio-lateral acceleration was also found in FoG+. Significant differences between the two groups across the two turning tasks validated the hypothesis that sharper turns might cause higher instability in FoG+ compared to FoG-.

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Gait Parameters Estimated Using Inertial Measurement Units

Cited by 12 publications

References 128 publications

Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults

Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults

Prediction of Patient-Reported Physical Activity Scores from Wearable Accelerometer Data: A Feasibility Study

An objective assessment to investigate the impact of turning angle on freezing of gait in Parkinson's disease

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