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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
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
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]