References of "Boutaayamou, Mohamed"
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See detailValidated assessment of gait sub-phase durations in older adults using an accelerometer-based ambulatory system
Boutaayamou, Mohamed ULiege; GILLAIN, Sophie ULiege; Schwartz, Cédric ULiege et al

in Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) (in press)

Validated extraction of gait sub-phase durations using an ambulatory accelerometer-based system is a current unmet need to quantify subtle changes during the walking of older adults. In this paper, we ... [more ▼]

Validated extraction of gait sub-phase durations using an ambulatory accelerometer-based system is a current unmet need to quantify subtle changes during the walking of older adults. In this paper, we describe (1) a signal processing algorithm to automatically extract not only durations of stride, stance, swing, and double support phases, but also durations of sub-phases that refine the stance and swing phases from foot-worn accelerometer signals in comfortable walking of older adults, and (2) the validation of this extraction using reference data provided by a gold standard system. The results show that we achieve a high agreement between our method and the reference method in the extraction of (1) the temporal gait events involved in the estimation of the phase/sub-phase durations, namely heel strike (HS), toe strike (TS), toe-off (TO), maximum of heel clearance (MHC), and maximum of toe clearance (MTC), with an accuracy and precision that range from ‒3.6 ms to 4.0 ms, and 6.5 ms to 12.0 ms, respectively, and (2) the gait phase/sub-phase durations, namely stride, stance, swing, double support phases, and HS to TS, TO to MHC, MHC to MTC, and MTC to HS sub-phases, with an accuracy and precision that range from ‒4 ms to 5 ms, and 9 ms to 15 ms, respectively, in comfortable walking of a thirty-eight older adults ( (mean ± standard deviation) 71.0 ± 4.1 years old). This demonstrates that the developed accelerometer-based algorithm can extract validated temporal gait events and phase/sub-phase durations, in comfortable walking of older adults, with a promising degree of accuracy/precision compared to reference data, warranting further studies. [less ▲]

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See detailA gait cycle partitioning method using a foot-worn accelerometer system
Boutaayamou, Mohamed ULiege; Bruls, Olivier ULiege; Denoël, Vincent ULiege et al

Conference (2017, November 30)

Detailed reference viewed: 72 (1 ULiège)
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See detailA gait cycle partitioning method using a foot-worn accelerometer system
Boutaayamou, Mohamed ULiege; Bruls, Olivier ULiege; Denoël, Vincent ULiege et al

Conference (2017, November 30)

Detailed reference viewed: 13 (0 ULiège)
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See detailA Novel Accelerometer-Based Method for Stride Length Estimation
Boutaayamou, Mohamed ULiege; Schwartz, Cédric ULiege; Denoël, Vincent ULiege et al

Poster (2017, July 14)

We demonstrate the feasibility of accurately and precisely estimating the left/right average stride length from measured heel/toe accelerations in the gait of healthy, old adults. Our approach relies on ... [more ▼]

We demonstrate the feasibility of accurately and precisely estimating the left/right average stride length from measured heel/toe accelerations in the gait of healthy, old adults. Our approach relies on (1) a novel method that uses only accelerometer data without the need of additional data from, e.g., gyroscopes and/or magnetometers, and on (2) the validation of the results using reference 3D optoelectronic system data. [less ▲]

Detailed reference viewed: 51 (15 ULiège)
See detailAmbulatory System for Gait Analysis
Boutaayamou, Mohamed ULiege; Bruls, Olivier ULiege; Croisier, Jean-Louis ULiege et al

Conference (2017, April 29)

We describe the principle and use of a wireless, 3-axis accelerometer-based ambulatory system that records acceleration signals and automatically analyses them to characterize normal and pathological gait ... [more ▼]

We describe the principle and use of a wireless, 3-axis accelerometer-based ambulatory system that records acceleration signals and automatically analyses them to characterize normal and pathological gait. The associated algorithm is versatile enough to detect, on a stride-by-stride basis, refined gait parameters that quantify subtle gait disturbances in, e.g., in Parkinson’s disease in a rater-independent way. The experimental results show the potential of the developed accelerometer-based technique to be used in neurology (e.g., characterization of Parkinsonian gait: slowness, shuffling, short steps, freezing of gait, asymmetries in gait), rehabilitation, geriatrics (ex. monitoring activity parameters in the elderly), orthopedics and sport. [less ▲]

Detailed reference viewed: 34 (6 ULiège)
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See detailMotion analysis: a prevention tool
Schwartz, Cédric ULiege; CROISIER, Jean-Louis ULiege; Forthomme, Bénédicte ULiege et al

Conference (2017, April 28)

Detailed reference viewed: 27 (8 ULiège)
See detailAlgorithm for Temporal Gait Analysis Using Wireless Foot-Mounted Accelerometers
Boutaayamou, Mohamed ULiege; Denoël, Vincent ULiege; Bruls, Olivier ULiege et al

Book published by Springer (2017)

We present a new signal processing algorithm that extracts five gait events: heel strike, toe strike, heel-off, toe-off, and heel clearance from only two accelerometers attached on the heels of the ... [more ▼]

We present a new signal processing algorithm that extracts five gait events: heel strike, toe strike, heel-off, toe-off, and heel clearance from only two accelerometers attached on the heels of the subjects usual shoes. This algorithm first uses a continuous wavelet-based segmentation that parses the signal of consecutive strides into motionless periods defining relevant local acceleration signals. Then, the algorithm uses versatile techniques to accurately extract the five gait events from these local acceleration signals. We validated, on a stride-by-stride basis, the extraction of these gait events by comparing the results with reference data provided by a kinematic 3D analysis system and a video camera. The accuracy and precision achieved by the extraction algorithm for healthy subjects, the reduced number of accelerometer units required, and the validation results obtained, encourage us to further study this system in pathological conditions. [less ▲]

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See detailGait pattern of healthy old people for fast walking condition
GILLAIN, Sophie ULiege; Boutaayamou, Mohamed ULiege; Schwartz, Cédric ULiege et al

in Gerontechnology (2016, September)

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See detailGait pattern of healthy old people for dual task walking condition
GILLAIN, Sophie ULiege; Boutaayamou, Mohamed ULiege; Schwartz, Cédric ULiege et al

in Gerontechnology (2016, September)

Detailed reference viewed: 57 (18 ULiège)
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See detailAnalysis of temporal gait features extracted from accelerometer-based signals during ambulatory walking in Parkinson’s disease
Boutaayamou, Mohamed ULiege; Demonceau, Marie ULiege; Bruls, Olivier ULiege et al

Poster (2016, June 21)

Objective: To perform a proof-of-concept study showing the utility of versatile algorithms aimed at objectively quantifying the duration of refined gait features during ambulatory walking in a patient ... [more ▼]

Objective: To perform a proof-of-concept study showing the utility of versatile algorithms aimed at objectively quantifying the duration of refined gait features during ambulatory walking in a patient with Parkinson’s disease (PD) in ON and OFF medication states as compared with an age-matched control subject. [less ▲]

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See detailMethod for detecting interest points in images using angular signatures
Grogna, David ULiege; Boutaayamou, Mohamed ULiege; Verly, Jacques ULiege

in IEEE Xplore Digital Library (2016)

We present an innovative method for detecting interest points (IPs) in grayscale and color images. It is based on the use of angular signatures (ASs), produced by spinning, at each pixel in the image, an ... [more ▼]

We present an innovative method for detecting interest points (IPs) in grayscale and color images. It is based on the use of angular signatures (ASs), produced by spinning, at each pixel in the image, an "x-tapered, y-derivative, half-Gaussian kernel" in discrete angular steps. By exploiting the AS(s) produced at each pixel, it automatically "classifies" the pixel as being an IP or not. We present preliminary results on synthetic grayscale and real color 2D images, and these confirm the potential value of the method. It can easily be extended from grayscale and color images to images with any number of components, as well as to 3D volumetric images and images on grids of higher dimensionality. It is useful for stereo matching and video tracking. [less ▲]

Detailed reference viewed: 29 (5 ULiège)
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See detailExtraction of temporal gait parameters using a reduced number of wearable accelerometers
Boutaayamou, Mohamed ULiege; Denoël, Vincent ULiege; Bruls, Olivier ULiege et al

in Proceedings of the 9th International Conference on Bio-inspired Systems and Signal Processing (2016)

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 ... [more ▼]

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). [less ▲]

Detailed reference viewed: 238 (39 ULiège)
See detailGait quantification through accelerometers and clinical tests: application to pathological gait
DEMONCEAU, Marie ULiege; Boutaayamou, Mohamed ULiege; Maquet, Didier ULiege et al

Conference (2015, January 30)

Gait gives essential information to physiotherapists in the screening and follow-up of their patients suffering from orthopaedic, geriatric or neurologic diseases. Most of time, clinical practitioners ... [more ▼]

Gait gives essential information to physiotherapists in the screening and follow-up of their patients suffering from orthopaedic, geriatric or neurologic diseases. Most of time, clinical practitioners rely on visual observation of their patients during specific clinical tests that can highlight gait abnormalities (e,g., the Tinetti assessment tool, the timed up and go test, the 6 minutes walking test), but these tests provide little quantified information about gait. These rough methods are also limited by inter-rater subjectivity and lack of acuteness in the detection of subtle impairments. On the other hand, instrumented gait analyses offer a sharper investigation with the ability to record and quantify gait events that cannot be caught at simple visual observation. Unfortunately, cutting edge technologies often pay the price of a limited number of strides extracted, the need of a strictly controlled laboratory environment, development and maintenance by a specialized staff. For these reasons, instrumented gait analysis may stand beyond the financial and technical reach of many rehabilitative centres and private practitioners. Accelerometer technologies have considerably developed with the progress of wireless technologies. These lightweight and low-cost sensors allow quantified gait analyses that are not restricted to a laboratory environment but can also be used in medical offices and in combination with common clinical gait tests. This presentation relates the experiences of our departments with accelerometer systems and clinical testing in gait analysis of patients suffering from Parkinson’s disease. The aim of this intervention is to cross ideas and knowledge of clinical practitioners and engineers in the development a new gait analysis tool that could integrate routine evaluation of patients suffering from Parkinson’s disease and other conditions characterized by gait impairments. [less ▲]

Detailed reference viewed: 50 (18 ULiège)
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See detailDevelopment and validation of an accelerometer-based method for quantifying gait events
Boutaayamou, Mohamed ULiege; Schwartz, Cédric ULiege; Stamatakis, Julien et al

in Medical Engineering & Physics (2015)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 220 (66 ULiège)