Kimiaki Shirahama
Overview
Explore the profile of Kimiaki Shirahama including associated specialties, affiliations and a list of published articles.
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9
Citations
111
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Recent Articles
1.
Nisar M, Shirahama K, Irshad M, Huang X, Grzegorzek M
Sensors (Basel)
. 2023 Oct;
23(19).
PMID: 37837064
Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in...
2.
Huang X, Shirahama K, Irshad M, Nisar M, Piet A, Grzegorzek M
Sensors (Basel)
. 2023 Apr;
23(7).
PMID: 37050506
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the...
3.
Gouverneur P, Li F, Shirahama K, Luebke L, Adamczyk W, Szikszay T, et al.
Sensors (Basel)
. 2023 Feb;
23(4).
PMID: 36850556
Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been...
4.
Kulwa F, Li C, Zhang J, Shirahama K, Kosov S, Zhao X, et al.
Environ Sci Pollut Res Int
. 2022 Mar;
29(34):51909-51926.
PMID: 35257344
Environmental microorganism (EM) offers a highly efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the...
5.
Huang X, Shirahama K, Li F, Grzegorzek M
Artif Intell Med
. 2020 Nov;
110:101981.
PMID: 33250147
Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage...
6.
Li F, Shirahama K, Nisar M, Huang X, Grzegorzek M
Sensors (Basel)
. 2020 Aug;
20(15).
PMID: 32751855
The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from...
7.
Nisar M, Shirahama K, Li F, Huang X, Grzegorzek M
Sensors (Basel)
. 2020 Jun;
20(12).
PMID: 32575451
This paper addresses wearable-based recognition of Activities of Daily Living (ADLs) which are composed of several repetitive and concurrent short movements having temporal dependencies. It is improbable to directly use...
8.
Li F, Shirahama K, Nisar M, Koping L, Grzegorzek M
Sensors (Basel)
. 2018 Mar;
18(2).
PMID: 29495310
Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to...
9.
Koping L, Shirahama K, Grzegorzek M
Comput Biol Med
. 2018 Jan;
95:248-260.
PMID: 29361267
Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify...