Keyword search (3,448 papers available)


On the Impact of Biceps Muscle Fatigue in Human Activity Recognition.

Author(s): Elshafei M, Costa DE, Shihab E

Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems' performance. I...

Article GUID: 33557239

Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables.

Author(s): Elshafei M, Shihab E

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various inj...

Article GUID: 33498702

Finite Element Modelling of Bandgap Engineered Graphene FET with the Application in Sensing Methanethiol Biomarker.

Author(s): Singh P, Abedini Sohi P, Kahrizi M

In this work, we have designed and simulated a graphene field effect transistor (GFET) with the purpose of developing a sensitive biosensor for methanethiol, a biomarker for bacterial infections. The surface of a graphene layer is functionalized by manipula...

Article GUID: 33467459

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects.

Author(s): Khannouz M; Glatard T;

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usua...

Article GUID: 33202905

Contactless Capacitive Electrocardiography Using Hybrid Flexible Printed Electrodes.

Author(s): Lessard-Tremblay M, Weeks J, Morelli L, Cowan G, Gagnon G, Zednik RJ

Traditional capacitive electrocardiogram (cECG) electrodes suffer from limited patient comfort, difficulty of disinfection and low signal-to-noise ratio in addition to the challenge of integrating them in wearables. A novel hybrid flexible cECG electrode wa...

Article GUID: 32927651

Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City.

Author(s): Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Sensors (Basel). 2020 Apr 16;20(8): Authors: Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Article GUID: 32316356

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

Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development.

Author(s): Alsaig A, Alagar V, Chammaa Z, Shiri N

Sensors (Basel). 2019 May 28;19(11): Authors: Alsaig A, Alagar V, Chammaa Z, Shiri N

Article GUID: 31141899

A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

Author(s): Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Sensors (Basel). 2018 May 15;18(5): Authors: Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Article GUID: 29762493

Fast Feature-Preserving Approach to Carpal Bone Surface Denoising.

Author(s): Salim I, Hamza AB

Sensors (Basel). 2018 Jul 21;18(7): Authors: Salim I, Hamza AB

Article GUID: 30037109

Big Data-Driven Cellular Information Detection and Coverage Identification.

Author(s): Wang H, Xie S, Li K, Ahmad MO

Sensors (Basel). 2019 Feb 22;19(4): Authors: Wang H, Xie S, Li K, Ahmad MO

Article GUID: 30813353

Surface Profiling and Core Evaluation of Aluminum Honeycomb Sandwich Aircraft Panels Using Multi-Frequency Eddy Current Testing.

Author(s): Reyno T, Underhill PR, Krause TW, Marsden C, Wowk D

Sensors (Basel). 2017 Sep 14;17(9): Authors: Reyno T, Underhill PR, Krause TW, Marsden C, Wowk D

Article GUID: 28906434


Title:A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.
Authors:Dehghani ASarbishei OGlatard TShihab 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]