Personalized Real-Time Sleep Stage from Past Sleep Data to Today’s Sleep Estimation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016-06-21

AUTHORS

Yusuke Tajima , Tomohiro Harada , Hiroyuki Sato , Keiki Takadama

ABSTRACT

This paper focuses on the real-time sleep stage estimation and proposes the method which appropriately selects the past sleep data as the prior knowledge for improving accuracy of the sleep stage estimation. The prior knowledge in this paper is represented as the parameters for estimating the sleep stage and it is composed of 26 parameters which give an influence to the accuracy of the real-time sleep stage estimation. Concretely, these parameters are acquired from the heartbeat data of a certain past day, and they are used to estimate the heartbeat data of a current day, which data is finally converted to the sleep stage. The role of the proposed method is to select the appropriate parameters of the heartbeat data of a certain past day, which is similar to the heartbeat data of a current day. To investigate the effectiveness of the proposed method, we conducted the human subject experiment which investigated the accuracy of the real-time sleep stage estimation of two adult males (whose age are 20 and 40) and one adult female (whose age is 60) by employing the appropriate parameters of the different day from three days. The experimental results revealed that the accuracy of the real-time sleep stage estimation with the proposed method is higher than that without it. More... »

PAGES

501-510

Book

TITLE

Human Interface and the Management of Information: Applications and Services

ISBN

978-3-319-40396-0
978-3-319-40397-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-40397-7_48

DOI

http://dx.doi.org/10.1007/978-3-319-40397-7_48

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053730656


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The University of Electro-Communications, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "The University of Electro-Communications, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tajima", 
        "givenName": "Yusuke", 
        "id": "sg:person.015061061004.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015061061004.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Human and Computer Intelligence, Ritsumeikan University, Kyoto, Shiga, Japan", 
          "id": "http://www.grid.ac/institutes/grid.262576.2", 
          "name": [
            "Department of Human and Computer Intelligence, Ritsumeikan University, Kyoto, Shiga, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Harada", 
        "givenName": "Tomohiro", 
        "id": "sg:person.013014044611.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013014044611.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Electro-Communications, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "The University of Electro-Communications, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sato", 
        "givenName": "Hiroyuki", 
        "id": "sg:person.07750750604.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07750750604.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Electro-Communications, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "The University of Electro-Communications, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takadama", 
        "givenName": "Keiki", 
        "id": "sg:person.012774267611.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012774267611.99"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2016-06-21", 
    "datePublishedReg": "2016-06-21", 
    "description": "This paper focuses on the real-time sleep stage estimation and proposes the method which appropriately selects the past sleep data as the prior knowledge for improving accuracy of the sleep stage estimation. The prior knowledge in this paper is represented as the parameters for estimating the sleep stage and it is composed of 26 parameters which give an influence to the accuracy of the real-time sleep stage estimation. Concretely, these parameters are acquired from the heartbeat data of a certain past day, and they are used to estimate the heartbeat data of a current day, which data is finally converted to the sleep stage. The role of the proposed method is to select the appropriate parameters of the heartbeat data of a certain past day, which is similar to the heartbeat data of a current day. To investigate the effectiveness of the proposed method, we conducted the human subject experiment which investigated the accuracy of the real-time sleep stage estimation of two adult males (whose age are 20 and 40) and one adult female (whose age is 60) by employing the appropriate parameters of the different day from three days. The experimental results revealed that the accuracy of the real-time sleep stage estimation with the proposed method is higher than that without it.", 
    "editor": [
      {
        "familyName": "Yamamoto", 
        "givenName": "Sakae", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-40397-7_48", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-40396-0", 
        "978-3-319-40397-7"
      ], 
      "name": "Human Interface and the Management of Information: Applications and Services", 
      "type": "Book"
    }, 
    "keywords": [
      "sleep stage estimation", 
      "stage estimation", 
      "prior knowledge", 
      "heartbeat data", 
      "past days", 
      "current day", 
      "appropriate parameters", 
      "human subject experiments", 
      "sleep data", 
      "accuracy", 
      "sleep stages", 
      "subjects experiment", 
      "experimental results", 
      "estimation", 
      "method", 
      "data", 
      "knowledge", 
      "effectiveness", 
      "parameters", 
      "stage", 
      "experiments", 
      "sleep estimation", 
      "days", 
      "adult males", 
      "adult females", 
      "different days", 
      "results", 
      "influence", 
      "role", 
      "males", 
      "females", 
      "paper"
    ], 
    "name": "Personalized Real-Time Sleep Stage from Past Sleep Data to Today\u2019s Sleep Estimation", 
    "pagination": "501-510", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1053730656"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-40397-7_48"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-40397-7_48", 
      "https://app.dimensions.ai/details/publication/pub.1053730656"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-10T10:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_68.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-40397-7_48"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-40397-7_48'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-40397-7_48'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-40397-7_48'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-40397-7_48'


 

This table displays all metadata directly associated to this object as RDF triples.

116 TRIPLES      23 PREDICATES      57 URIs      50 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-40397-7_48 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N0e5ce5d97d764f76b5af30012d757c71
4 schema:datePublished 2016-06-21
5 schema:datePublishedReg 2016-06-21
6 schema:description This paper focuses on the real-time sleep stage estimation and proposes the method which appropriately selects the past sleep data as the prior knowledge for improving accuracy of the sleep stage estimation. The prior knowledge in this paper is represented as the parameters for estimating the sleep stage and it is composed of 26 parameters which give an influence to the accuracy of the real-time sleep stage estimation. Concretely, these parameters are acquired from the heartbeat data of a certain past day, and they are used to estimate the heartbeat data of a current day, which data is finally converted to the sleep stage. The role of the proposed method is to select the appropriate parameters of the heartbeat data of a certain past day, which is similar to the heartbeat data of a current day. To investigate the effectiveness of the proposed method, we conducted the human subject experiment which investigated the accuracy of the real-time sleep stage estimation of two adult males (whose age are 20 and 40) and one adult female (whose age is 60) by employing the appropriate parameters of the different day from three days. The experimental results revealed that the accuracy of the real-time sleep stage estimation with the proposed method is higher than that without it.
7 schema:editor Nbea36922d25847adaf11016a7b8bdd56
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N1d7e46ca6b0048e59d6cf5f9b9d04561
12 schema:keywords accuracy
13 adult females
14 adult males
15 appropriate parameters
16 current day
17 data
18 days
19 different days
20 effectiveness
21 estimation
22 experimental results
23 experiments
24 females
25 heartbeat data
26 human subject experiments
27 influence
28 knowledge
29 males
30 method
31 paper
32 parameters
33 past days
34 prior knowledge
35 results
36 role
37 sleep data
38 sleep estimation
39 sleep stage estimation
40 sleep stages
41 stage
42 stage estimation
43 subjects experiment
44 schema:name Personalized Real-Time Sleep Stage from Past Sleep Data to Today’s Sleep Estimation
45 schema:pagination 501-510
46 schema:productId N3c2220145d7644feb48cf3917d987f8d
47 Nb8fba47c4e0d488290798e56141c56e1
48 schema:publisher N11f47fbae11e4218b29985ed493fb363
49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053730656
50 https://doi.org/10.1007/978-3-319-40397-7_48
51 schema:sdDatePublished 2022-05-10T10:55
52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
53 schema:sdPublisher N567799a36aea481d9a5bbfe7fa0af3da
54 schema:url https://doi.org/10.1007/978-3-319-40397-7_48
55 sgo:license sg:explorer/license/
56 sgo:sdDataset chapters
57 rdf:type schema:Chapter
58 N0e5ce5d97d764f76b5af30012d757c71 rdf:first sg:person.015061061004.27
59 rdf:rest Nf736f0b5907a4d8da2cdfb19de16fab0
60 N10937ecff9ba4591b1880f7505242da7 rdf:first sg:person.012774267611.99
61 rdf:rest rdf:nil
62 N11f47fbae11e4218b29985ed493fb363 schema:name Springer Nature
63 rdf:type schema:Organisation
64 N1d7e46ca6b0048e59d6cf5f9b9d04561 schema:isbn 978-3-319-40396-0
65 978-3-319-40397-7
66 schema:name Human Interface and the Management of Information: Applications and Services
67 rdf:type schema:Book
68 N3c2220145d7644feb48cf3917d987f8d schema:name dimensions_id
69 schema:value pub.1053730656
70 rdf:type schema:PropertyValue
71 N567799a36aea481d9a5bbfe7fa0af3da schema:name Springer Nature - SN SciGraph project
72 rdf:type schema:Organization
73 N750aafeea5d647379d62b89d1461e0ee rdf:first sg:person.07750750604.05
74 rdf:rest N10937ecff9ba4591b1880f7505242da7
75 Nb8fba47c4e0d488290798e56141c56e1 schema:name doi
76 schema:value 10.1007/978-3-319-40397-7_48
77 rdf:type schema:PropertyValue
78 Nbea36922d25847adaf11016a7b8bdd56 rdf:first Nccddac7bfcea4758bcc43ad7342dce41
79 rdf:rest rdf:nil
80 Nccddac7bfcea4758bcc43ad7342dce41 schema:familyName Yamamoto
81 schema:givenName Sakae
82 rdf:type schema:Person
83 Nf736f0b5907a4d8da2cdfb19de16fab0 rdf:first sg:person.013014044611.53
84 rdf:rest N750aafeea5d647379d62b89d1461e0ee
85 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
86 schema:name Information and Computing Sciences
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
89 schema:name Artificial Intelligence and Image Processing
90 rdf:type schema:DefinedTerm
91 sg:person.012774267611.99 schema:affiliation grid-institutes:grid.266298.1
92 schema:familyName Takadama
93 schema:givenName Keiki
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012774267611.99
95 rdf:type schema:Person
96 sg:person.013014044611.53 schema:affiliation grid-institutes:grid.262576.2
97 schema:familyName Harada
98 schema:givenName Tomohiro
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013014044611.53
100 rdf:type schema:Person
101 sg:person.015061061004.27 schema:affiliation grid-institutes:grid.266298.1
102 schema:familyName Tajima
103 schema:givenName Yusuke
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015061061004.27
105 rdf:type schema:Person
106 sg:person.07750750604.05 schema:affiliation grid-institutes:grid.266298.1
107 schema:familyName Sato
108 schema:givenName Hiroyuki
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07750750604.05
110 rdf:type schema:Person
111 grid-institutes:grid.262576.2 schema:alternateName Department of Human and Computer Intelligence, Ritsumeikan University, Kyoto, Shiga, Japan
112 schema:name Department of Human and Computer Intelligence, Ritsumeikan University, Kyoto, Shiga, Japan
113 rdf:type schema:Organization
114 grid-institutes:grid.266298.1 schema:alternateName The University of Electro-Communications, Tokyo, Japan
115 schema:name The University of Electro-Communications, Tokyo, Japan
116 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...