Mohamed Boutaayamou

Development and validation of an accelerometer-based method for quantifying gait events

An original signal processing algorithm is presented to automatically extract, on a stride-by-stride basis, four consecutive fundamental events of walking, heel strike (HS), toe strike (TS), heel-off (HO), and toe-off (TO), from wireless accelerometers applied to the right and left foot. First, the signals recorded from heel and toe three-axis accelerometers are segmented providing heel and toe flat phases. Then, the four gait events are defined from these flat phases. The accelerometer-based event identification was validated in seven healthy volunteers and a total of 247 trials against reference data provided by a force plate, a kinematic 3D analysis system, and video camera. HS, TS, HO, and TO were detected with a temporal accuracy ± precision of 1.3 ms ± 7.2 ms, -4.2 ms ± 10.9 ms, -3.7 ms ± 14.5 ms, and -1.8 ms ± 11.8 ms, respectively, with the associated 95% confidence intervals ranging from -6.3 ms to 2.2 ms. It is concluded that the developed accelerometer-based method can accurately and precisely detect HS, TS, HO, and TO, and could thus be used for the ambulatory monitoring of gait features computed from these events when measured concurrently in both feet.

Contribution of a Trunk Accelerometer System to the Characterization of Gait in Patients With Mild-to-Moderate Parkinson's Disease

Objective: Gait disturbances like shuffling and short steps are obvious at visual observation in patients with advanced Parkinsons disease (PD). However, quantitative methods are increasingly used to evaluate the wide range of gait abnormalities that may occur over the disease course. The goal of this study was to test the ability of a trunk accelerometer system to quantify the effects of PD on several gait features when walking at self-selected speed. Methods: We recruited 96 subjects split into three age-matched groups: 32 healthy controls (HC), 32 PD patients at Hoehn and Yahr stage <; II (PD-1), and 32 patients at Hoehn and Yahr stage II-III (PD-2). The following outcomes were extracted from the signals of the triaxial accelerometer worn on the lower back: stride length, cadence, regularity index, symmetry index, and mechanical powers yielded in the cranial-caudal, anteroposterior, and medial-lateral directions. Walking speed was measured using a stopwatch. Results: Besides other gait features, the PD-1 and the PD-2 groups showed significantly reduced stride length normalized to height (p <; 0.02) and symmetry index (p <; 0.009) in comparison to the HC. Regularity index was the only feature significantly decreased in the PD-2 group as compared with the two other groups (p <; 0.01). The clinical relevance of this finding was supported by significant correlations with mobility and gait scales (r is around -0.3; p <; 0.05). Conclusion: Gait quantified by a trunk accelerometer may provide clinically useful information for the screening and follow-up of PD patients.

Validated extraction of gait events from 3D accelerometer recordings

This work is part of a project that deals with the three-dimensional (3D) analysis of normal and pathological gaits based on a newly developed system for clinical applications, using low-cost wireless accelerometers and a signal processing algorithm. This system automatically extracts relevant gait events such as the heel strikes (HS) and the toe-offs (TO), which characterize the stance and the swing phases of walking. The performances of the low-cost accelerometer hardware and related algorithm have been compared to those obtained by a kinematic 3D analysis system and a force plate, used as gold standard methods. The HS and TO times obtained from the gait data of 7 healthy volunteers (147 trials) have been found to be (mean ± standard deviation) 0.42±7.92 ms and 3.11±10.08 ms later than those determined by the force plate, respectively. The experimental results demonstrate that the new hardware and associated algorithm constitute an effective low-cost gait analysis system, which could thus be used for the assessment of mobility in routine clinical practice.

Electrostatic Analysis of Moving Conductors Using a Perturbation Finite Element Method

This paper deals with the analysis of electrostatic problems involving moving devices by means of a perturbation finite element method. A reference problem without any moving parts is first solved and gives the source for a sequence of perturbation problems in subdomains restricted to the neighborhood of these parts. The source accounts for all the previous calculations for preceding positions what increases the efficiency of the simulations. This proposed approach also improves the computation accuracy and decreases the complexity of the analysis of moving conductors thanks to the use of independent and adaptively refined meshes.

Finite Element Modeling of Electrostatic MEMS Including the Impact of Fringing Field Effects on Forces

The numerical models describing the behaviour of electrostatically actuated microsystems often disregard fringing fields. However, taking the fringing fields into account is crucial for an accurate computation of the electrostatic forces. In this work, the finite element method is applied for modeling electrostatic actuators. The electrostatic force distribution is obtained by locally applying the virtual work method. A micro-beam and a comb drive are considered as test cases. The impact of the fringing field effects on the accuracy of electrostatic forces is shown through 2D and 3D parametric studies

An Iterative Finite Element Perturbation Method for Computing Electrostatic Field Distortions

A finite element perturbation method is developed for computing electrostatic field distortions and the ensuing charges and forces on moving conductive regions subjected to fixed potentials. It is based on the subsequent solution of an unperturbed problem in a complete domain, where conductive regions have been extracted, and of perturbation problems in subdomains restricted to the surroundings of the added conductive regions. The solution of the unperturbed problem serves as source (with a very reduced support) for the perturbation subproblems. For every new position of the conductors, the solution of the unperturbed problem does not vary and is thus reused; only the perturbation subproblems have to be solved. Further, this approach allows for the use of independent and well-adapted meshes. An iterative procedure is required if the conductive regions are close to the sources.

Extraction of temporal gait parameters using a reduced number of wearable accelerometers

Wearable inertial systems often require many sensing units in order to reach an accurate extraction of temporal gait parameters. Reconciling easy and fast handling in daily clinical use and accurate extraction of a substantial number of relevant gait parameters is a challenge. This paper describes the implementation of a new accelerometer-based method that accurately and precisely detects gait events/parameters from acceleration signals measured from only two accelerometers attached on the heels of the subject’s usual shoes. The first step of the proposed method uses a gait segmentation based on the continuous wavelet transform (CWT) that provides only a rough estimation of motionless periods defining relevant local acceleration signals. The second step uses the CWT and a novel piecewise-linear fitting technique to accurately extract, from these local acceleration signals, gait events, each labelled as heel strike (HS), toe strike (TS), heel-off (HO), toe-off (TO), or heel clearance (HC). A stride-by-stride validation of these extracted gait events was carried out by comparing the results with reference data provided by a kinematic 3D analysis system (used as gold standard) and a video camera. The temporal accuracy ± precision of the gait events were for HS: 7.2 ms ± 22.1 ms, TS: 0.7 ms ± 19.0 ms, HO: −3.4 ms ± 27.4 ms, TO: 2.2 ms ± 15.7 ms, and HC: 3.2 ms ± 17.9 ms. In addition, the occurrence times of right/left stance, swing, and stride phases were estimated with a mean error of −6 ms ± 15 ms, −5 ms ± 17 ms, and −6 ms ± 17 ms, respectively. The accuracy and precision achieved by the extraction algorithm for healthy subjects, the simplification of the hardware (through the reduction of the number of accelerometer units required), and the validation results obtained, convince us that the proposed accelerometer-based system could be extended for assessing pathological gait (e.g., for patients with Parkinson’s disease).

Segmentation of gait cycles using foot-mounted 3D accelerometers

We describe a new gait segmentation method based on the continuous wavelet transform to identify stride-by-stride gait cycles from measurements of foot-mounted three-dimensional (3D) accelerometers. The detection of such gait cycles is indeed a crucial step for an accurate extraction of relevant gait events such as heel strike, toe strike, heel-off, and toe-off. We demonstrate the ability of this segmentation method, used in conjunction with a validated extraction algorithm, to calculate the following gait (duration) parameters for each gait cycle during the gait of a healthy young subject and of an elderly subject with Parkinsons disease (PD) in OFF and ON states: durations of (1) loading response, (2) mid-stance, (3) push-off, (4) stance, (5) swing, (6) stride, (7) step, and (8) double support phases. The experimental results show that the proposed method can extract relevant refined gait parameters to quantify subtle gait disturbances in subjects with PD.

Development and validation of a 3D kinematic-based method for determining gait events during overground walking

A new signal processing algorithm is developed for quantifying heel strike (HS) and toe-off (TO) event times solely from measured heel and toe coordinates during overground walking. It is based on a rough estimation of relevant local 3D position signals. An original piecewise linear fitting method is applied to these local signals to accurately identify HS and TO times without the need of using arbitrary experimental coefficients. We validated the proposed method with nine healthy subjects and a total of 322 trials. The extracted temporal gait events were compared to reference data obtained from a force plate. HS and TO times were identified with a temporal accuracy ± precision of 0.3 ms ± 7.1 ms, and -2.8 ms ± 7.2 ms in comparison with reference data defined with a force threshold of 10 N. This algorithm improves the accuracy of the HS and TO detection. Furthermore, it can be used to perform stride-by-stride analysis during overground walking with only recorded heel and toe coordinates.

Assessing gait parameters with accelerometer-based methods to identify older adults at risk of falls: a systematic review

PurposeThe purpose of this study was to perform a systematic review to assess the utility of accelerometric methods to identify older adults at risk of falls.MethodsThe Preferred Reporting Item for Systematic review and Meta-Analysis (PRISMA) guidelines were followed during all steps of this systematic review. Cross sectional and longitudinal studies assessing gait parameters in older adults using accelerometric devices, and comparing groups based on the risk of falls or fall history were identified from studies published in the MEDLINE, SCOPUS and Cochrane Database of Systematic Reviews databases between January 1996 and January 2017. Study selection and data extraction were performed independently by two reviewers. The quality of the methodology used in the studies included was assessed using the Newcastle–Ottawa Scale.ResultsIn total, 354 references were identified through the database search. After selection, ten studies were included in this systematic review. According to the cross sectional studies, people who fall or are at risk of fall are slower, and walk with shorter steps, lower step frequency, worse stride and step regularity in terms of time, position and acceleration profiles. One longitudinal study suggests considering harmonic ratio of upper trunk acceleration in the vertical plane. Two other longitudinal studies highlight the importance of considering more than one gait parameter, and sophisticated statistical tools to discern older adults at risk for future fall(s).ConclusionThis systematic review essentially highlights the lack of available literature providing strong evidence that gait parameters obtained using acceleration-based methods could be useful to discern older people at risk of fall. Available literature is encouraging, but further high quality studies are needed to highlight the cross-sectional and longitudinal relationships between gait parameters and falls in older adults.