Diana Trojaniello

Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait.

BackgroundThe step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIMUs), have been developed for the step-by-step estimation of the gait temporal parameters. However, most of them were applied to the gait of healthy subjects and/or of a single pathologic population. Moreover, spatial parameters in pathologic populations have been rarely estimated step-by-step using MIMUs. The validity of clinically suitable MIMU-based methods for the estimation of spatio-temporal parameters is therefore still an open issue. The aim of this study was to propose and validate a method for the determination of both temporal and spatial parameters that could be applied to normal and heavily compromised gait patterns.MethodsTwo MIMUs were attached above each subject’s ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson’s disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.ResultsOver the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11xa0ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.ConclusionsThe proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use.

Accuracy, sensitivity and robustness of five different methods for the estimation of gait temporal parameters using a single inertial sensor mounted on the lower trunk

In the last decade, various methods for the estimation of gait events and temporal parameters from the acceleration signals of a single inertial measurement unit (IMU) mounted at waist level have been proposed. Despite the growing interest for such methodologies, a thorough comparative analysis of methods with regards to number of extra and missed events, accuracy and robustness to IMU location is still missing in the literature. The aim of this work was to fill this gap. Five methods have been tested on single IMU data acquired from fourteen healthy subjects walking while being recorded by a stereo-photogrammetric system and two force platforms. The sensitivity in detecting initial and final contacts varied between 81% and 100% across methods, whereas the positive predictive values ranged between 94% and 100%. For all tested methods, stride and step time estimates were obtained; three of the selected methods also allowed estimation of stance, swing and double support time. Results showed that the accuracy in estimating step and stride durations was acceptable for all methods. Conversely, a statistical difference was found in the error in estimating stance, swing and double support time, due to the larger errors in the final contact determination. Except for one method, the IMU positioning on the lower trunk did not represent a critical factor for the estimation of gait temporal parameters. Results obtained in this study may not be applicable to pathologic gait.

A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease Patients

Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington’s disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject–out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations.

Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects

The estimation of gait temporal parameters with inertial measurement units (IMU) is a research topic of interest in clinical gait analysis. Several methods, based on the use of a single IMU mounted at waist level, have been proposed for the estimate of these parameters showing satisfactory performance when applied to the gait of healthy subjects. However, the above mentioned methods were developed and validated on healthy subjects and their applicability in pathological gait conditions was not systematically explored. We tested the three best performing methods found in a previous comparative study on data acquired from 10 older adults, 10 hemiparetic, 10 Parkinsons disease and 10 Huntingtons disease subjects. An instrumented gait mat was used as gold standard. When pathological populations were analyzed, missed or extra events were found for all methods and a global decrease of their performance was observed to different extents depending on the specific group analyzed. The results revealed that none of the tested methods outperformed the others in terms of accuracy of the gait parameters determination for all the populations except the Parkinsons disease subjects group for which one of the methods performed better than others. The hemiparetic subjects group was the most critical group to analyze (stride duration errors between 4-5 % and step duration errors between 8-13 % of the actual values across methods). Only one method provides estimates of the stance and swing durations which however should be interpreted with caution in pathological populations (stance duration errors between 6-14 %, swing duration errors between 10-32 % of the actual values across populations).

Accurately measuring human movement using magneto-inertial sensors: techniques and challenges

An overview of conceptual, analytical and experimental elements to quantitatively describe human kinematics, with specific focus on gait, using magneto-inertial sensors is presented. It includes a review and a taxonomy scheme of the techniques for the estimation of joint kinematics and spatio-temporal gait parameters.

Hidden Markov model-based strategy for gait segmentation using inertial sensors: Application to elderly, hemiparetic patients and Huntington's disease patients.

A solution to discriminate stance and swing in both healthy and abnormal gait using inertial sensors is proposed. The method is based on a two states hidden Markov model trained in a supervised way. The proposed method can generalize across different groups of subjects, without the need of parameters tuning. Leave-one-subject-out validation tests showed 20 ms and 16 ms errors on average in the determination of foot strike and toe off events across the three groups of subjects including 10 elderly, 10 hemiparetic patients and 10 Huntingtons disease patients. The proposed methodology can be implemented online in portable devices to be used in clinical practice or in everyday personal health assessment.

Foot clearance estimation during overground walking and vertical obstacle passing using shank-mounted MIMUs in healthy and pathological subjects

A method for assessing maximum foot clearance (maxFCl) during overground walking and obstacle passing using magnetic and inertial measurement units (MIMUs) placed above the malleoli is proposed and validated. The method precision and accuracy were evaluated using a stereo-photogrammetric system as a gold standard. The proposed method was applied to the data obtained from the gait of both healthy subjects and patients with various abnormal gaits. First, an optimally filtered direct and reverse integration (OFDRI) was used for each gait cycle to determine the gait velocity. Then, the effect of an additional OFDRI or a simple DRI approach for obtaining vertical foot displacement was explored. The results showed that the mean absolute errors associated to the maxFCl estimates were about 10% of its range of variation for the healthy and pathological subjects during overground walking. An accurate estimate of the maxFCl during obstacle passing was reached (mean absolute errors less than 5%). Additional testing on gait at various gait speed and on a greater number of subjects should be carried out to fully validate the MIMU-based maxFCl estimates.

Automatic classification of gait in children with early-onset ataxia or developmental coordination disorder and controls using inertial sensors

Early-Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two conditions that affect coordination in children. Phenotypic identification of impaired coordination plays an important role in their diagnosis. Gait is one of the tests included in rating scales that can be used to assess motor coordination. A practical problem is that the resemblance between EOA and DCD symptoms can hamper their diagnosis. In this study we employed inertial sensors and a supervised classifier to obtain an automatic classification of the condition of participants. Data from shank and waist mounted inertial measurement units were used to extract features during gait in children diagnosed with EOA or DCD and age-matched controls. We defined a set of features from the recorded signals and we obtained the optimal features for classification using a backward sequential approach. We correctly classified 80.0%, 85.7%, and 70.0% of the control, DCD and EOA children, respectively. Overall, the automatic classifier correctly classified 78.4% of the participants, which is slightly better than the phenotypic assessment of gait by two pediatric neurologists (73.0%). These results demonstrate that automatic classification employing signals from inertial sensors obtained during gait maybe used as a support tool in the differential diagnosis of EOA and DCD. Furthermore, future extension of the classifiers test domains may help to further improve the diagnostic accuracy of pediatric coordination impairment. In this sense, this study may provide a first step towards incorporating a clinically objective and viable biomarker for identification of EOA and DCD.

Comparative Evaluation of Gait Event Detection Methods Based on a Single IMU: Error Sensitivity Analysis to IMU Positioning

In this study, five different gait event detection methods using a single Inertial Measurement Unit (IMU) were evaluated. Since gait events identification is mostly based on the observation of features characterizing the acceleration patterns, a misalignment of the IMU may cause changes in these. Thus, a correct positioning of the IMU is crucial for the successful application of the abovementioned methods. First, IMU data were acquired and IMU inclination in the sagittal plane estimated at different trunk locations on twelve subject standing upright. Inter-subject variability was evaluated to estimate the range of the IMU inclination. Then, acceleration data for each IMU location were acquired from one subject during a 10 m walk. To simulate accelerometer signals related to different IMU inclinations, the IMU was virtually rotated within the range of the IMU inclination. Initial contact instants were then evaluated applying the five methods and compared with the data acquired by a stereo-photogrammetric system.

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

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 −u200927 to 27xa0ms for Stride Time, −u200968 to 44xa0ms for Stance Time, −u200931 to 31xa0ms for Step Time and −u200967 to 52xa0mm 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% (<u200930xa0ms). Stride length mean absolute errors were on average 2% (≈u200925xa0mm). 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”.