Keyword search (3,676 papers available)


File-based localization of numerical perturbations in data analysis pipelines.

Author(s): Salari A, Kiar G, Lewis L, Evans AC, Glatard T

BACKGROUND: Data analysis pipelines are known to be affected by computational conditions, presumably owing to the creation and propagation of numerical errors. While this process could play a major role in the current reproducibility crisis, the precise cau...

Article GUID: 33269388
Comparing perturbation models for evaluating stability of neuroimaging pipelines.

Author(s): Kiar G, de Oliveira Castro P, Rioux P, Petit E, Brown ST, Evans AC, Glatard T

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across ...

Article GUID: 32831546
A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.

Author(s): Dehghani A, Sarbishei O, Glatard T, Shihab E

Sensors (Basel). 2019 Nov 18;19(22): Authors: Dehghani A, Sarbishei O, Glatard T, Shihab E

Article GUID: 31752158
Cyberinfrastructure for Open Science at the Montreal Neurological Institute.

Author(s): Das S, Glatard T, Rogers C, Saigle J, Paiva S, MacIntyre L, Safi-Harab M, Rousseau ME, Stirling J, Khalili-Mahani N, MacFarlane D, Kostopoul...

Front Neuroinform. 2016;10:53 Authors: Das S, Glatard T, Rogers C, Saigle J, Paiva S, MacIntyre L, Safi-Harab M, Rousseau ME, Stirling J, Khalili-Mahani N, MacFarlane D, Kostopoulos P, Rioux P, Ma...

Article GUID: 28111547
Best practices in data analysis and sharing in neuroimaging using MRI.

Author(s): Nichols TE, Das S, Eickhoff SB, Evans AC, Glatard T, Hanke M, Kriegeskorte N, Milham MP, Poldrack RA, Poline JB, Proal E, Thirion B, Van Ess...

Nat Neurosci. 2017 Feb 23;20(3):299-303 Authors: Nichols TE, Das S, Eickhoff SB, Evans AC, Glatard T, Hanke M, Kriegeskorte N, Milham MP, Poldrack RA, Poline JB, Proal E, Thirion B, Van Essen DC, ...

Article GUID: 28230846
The first MICCAI challenge on PET tumor segmentation.

Author(s): Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky...

Med Image Anal. 2018 02;44:177-195 Authors: Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P...

Article GUID: 29268169
Boutiques: a flexible framework to integrate command-line applications in computing platforms.

Author(s): Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F, Das S, Ferreira da...

Gigascience. 2018 05 01;7(5): Authors: Glatard T, Kiar G, Aumentado-Armstrong T, Beck N, Bellec P, Bernard R, Bonnet A, Brown ST, Camarasu-Pop S, Cervenansky F, Das S, Ferreira da Silva R, Flandin...

Article GUID: 29718199
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

Author(s): Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glata...

Sci Rep. 2018 Sep 12;8(1):13650 Authors: Commowick O, Istace A, Kain M, Laurent B, Leray F, Simon M, Pop SC, Girard P, Améli R, Ferré JC, Kerbrat A, Tourdias T, Cervenansky F, Glatard T,...

Article GUID: 30209345
A Serverless Tool for Platform Agnostic Computational Experiment Management.

Author(s): Kiar G, Brown ST, Glatard T, Evans AC

Front Neuroinform. 2019;13:12 Authors: Kiar G, Brown ST, Glatard T, Evans AC

Article GUID: 30890927
Title:A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.
Authors:Dehghani A,  Sarbishei O,  Glatard T,  Shihab E
Link:https://www.ncbi.nlm.nih.gov/pubmed/31752158?dopt=Abstract
DOI:10.3390/s19225026
Category:Sensors (Basel)
PMID:31752158
Dept Affiliation: ENCS
1 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
2 Research and Development Department, Motsai Research, Saint Bruno, QC J3V 6B7, Canada.

Description:

A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.

Sensors (Basel). 2019 Nov 18;19(22):

Authors: Dehghani A, Sarbishei O, Glatard T, Shihab E

Abstract

The sliding window technique is widely used to segment inertial sensor signals, i.e., accelerometers and gyroscopes, for activity recognition. In this technique, the sensor signals are partitioned into fix sized time windows which can be of two types: (1) non-overlapping windows, in which time windows do not intersect, and (2) overlapping windows, in which they do. There is a generalized idea about the positive impact of using overlapping sliding windows on the performance of recognition systems in Human Activity Recognition. In this paper, we analyze the impact of overlapping sliding windows on the performance of Human Activity Recognition systems with different evaluation techniques, namely, subject-dependent cross validation and subject-independent cross validation. Our results show that the performance improvements regarding overlapping windowing reported in the literature seem to be associated with the underlying limitations of subject-dependent cross validation. Furthermore, we do not observe any performance gain from the use of such technique in conjunction with subject-independent cross validation. We conclude that when using subject-independent cross validation, non-overlapping sliding windows reach the same performance as sliding windows. This result has significant implications on the resource usage for training the human activity recognition systems.

PMID: 31752158 [PubMed - in process]