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Affiliation

Laboratory of Human Motion Analysis, University of Liège (ULiège), Liège, Belgium, INTELSIG Laboratory, Department of Electrical Engineering and Computer Science, ULiège, Liège and Belgium

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3 Papers

Terahertz Reflection Imaging of Paraffin-embedded Human Breast Cancer Samples: Some First Results

Topics: Medical Imaging and Diagnosis; Histology and Tissue Imaging

Authors: Mohamed Boutaayamou , Delphine Cerica and Jacques G. Verly

Affiliation: Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

Digital Object Identifier: 10.5220/0009163302000203

Proceedings of BIOIMAGING 2020/BIOSTEC, ISBN: 978-989-758-398-8

Quick Abstract

Several studies have shown that terahertz (THz) pulsed imaging has the potential of identifying the margins of human breast cancer in paraffin-embedded tissue samples. Before using this technique for the assessment of cancer margins during breast-conserving surgery, it is important to study the validity and reproducibility of previously published results. In the present paper, we describe some first results in the characterization of paraffin-embedded human breast cancer tissue through THz reflection imaging based on measurements provided by a newly acquired THz time-domain spectrometer. First, we measured the THz reflection impulse response of these samples using this spectrometer. Second, we processed, for one selected breast cancer tissue sample, the recorded data to generate preliminary images of (1) several maps of parameters extracted in the time- and frequency-domains, and (2) a map of the absorbance.

Adaptive Method for Detecting Zero-Velocity Regions to Quantify Stride-to-Stride Spatial Gait Parameters using Inertial Sensors

Topics: Medical Signal Acquisition, Analysis and Processing; Wearable Sensors and Systems

Authors: Mohamed Boutaayamou ^{1

;

2} , Cédric Schwartz ¹ , Laura Joris ³ , Bénédicte Forthomme ¹ , Vincent Denoël ¹ , Jean-Louis Croisier ¹ , Jacques G. Verly ² , Gaëtan Garraux ⁴ and Olivier Brüls ¹

Affiliations: ¹ Laboratory of Human Motion Analysis, University of Liège (ULiège), Liège, Belgium , ² INTELSIG Laboratory, Department of Electrical Engineering and Computer Science, ULiège, Liège, Belgium , ³ Microsys Laboratory, Department of Electrical Engineering and Computer Science, ULiège, Liège, Belgium , ⁴ GIGA - CRC In vivo Imaging, ULiège, Liège, Belgium

Digital Object Identifier: 10.5220/0007576002290236

Proceedings of BIOSIGNALS 2019/BIOSTEC, ISBN: 978-989-758-353-7

Quick Abstract

We present a new adaptive method that robustly detects zero-velocity regions to accurately and precisely quantify (1) individual stride lengths (SLs), (2) individual stride velocities (SVs), (3) the average of SL, (4) the average of SV, and (5) the cadence during slow, normal, and fast overground walking conditions in young and healthy people. The measurements involved in the estimation of these spatial gait parameters are obtained using only one inertial measurement unit attached on a regular shoe at the level of the heel. This adaptive method reduced the integration drifts across consecutive strides and improved the accuracy and precision in the spatial gait parameter estimation. The validation of the proposed algorithm has been carried out using reference spatial gait parameters obtained from a kinematic reference system. The accuracy ± precision results were for SLs: 0.0 ± 4.7 cm, −0.7 ± 4.4 cm, and −5.8 ± 5.8 cm, during slow, normal, and fast walking conditions, respectively, corresponding to −0.1 ± 4.2 %, −0.5 ± 3.2 %, and −3.3 ± 3.0 % of the respective mean SL. The accuracy ± precision results were for SVs: 0.0 ± 2.9 cm/s, −0.7 ± 3.8 cm/s, and −6.7 ± 6.7 cm/s, during slow, normal, and fast walking conditions, respectively, corresponding to −0.6 ± 3.3 %, −0.1 ± 4.5 %, and −3.5 ± 3.1 % of the respective mean SV. These validation results show a good agreement between the proposed method and the reference, and demonstrate a fairly accurate and precise estimation of these spatial gait parameters. The proposed method paves the way for an objective quantification of spatial gait parameters in routine clinical practice.

Validated Assessment of Gait Sub-Phase Durations in Older Adults using an Accelerometer-based Ambulatory System

Topics: Medical Signal Acquisition, Analysis and Processing; Wearable Sensors and Systems; Detection and Identification

Authors: Mohamed Boutaayamou ^{1

;

2} , Sophie Gillain ³ , Cédric Schwartz ¹ , Vincent Denoël ¹ , Jean Petermans ³ , Jean-Louis Croisier ¹ , Bénédicte Forthomme ¹ , Jacques G. Verly ² , Olivier Brüls ¹ and Gaëtan Garraux ²

Affiliations: ¹ University of Liège (ULiège), Belgium , ² ULiège, Belgium , ³ University Hospital Center of Liège, Belgium

Digital Object Identifier: 10.5220/0006716202480255

Proceedings of BIOSIGNALS 2018/BIOSTEC, ISBN: 978-989-758-279-0

Quick Abstract

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.