Activity inference engine for real-time cognitive assistance in smart environments View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-06

AUTHORS

Jianguo Hao, Abdenour Bouzouane, Bruno Bouchard, Sébastien Gaboury

ABSTRACT

Recent research in ambient intelligence allows wireless sensor networks to perceive environmental states and their changes in smart environments. An intelligent living environment could not only provide better interactions with its ambiance, inside electrical devices and everyday objects, but also offer smart services, even smart assistance to disabled or elderly people when necessary. This paper proposes a new inference engine based on the formal concept analysis to achieve activity prediction and recognition, even abnormal behavioral pattern detection for ambient-assisted living. According to occupants’ historical data, we explore useful frequent patterns to guide future prediction, recognition and detection tasks. Like the way of human reasoning, the engine could incrementally infer the most probable activity according to successive observations. Furthermore, we propose a hierarchical clustering approach to merge activities according to their semantic similarities. As an optimized knowledge discovery approach in hierarchical ambient intelligence environments, it could optimize the prediction accuracies at the earliest stages when only a few observations are available. More... »

PAGES

679-698

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12652-017-0467-7

DOI

http://dx.doi.org/10.1007/s12652-017-0467-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084036127


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "LIARA Laboratory, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 Boulevard de l\u2019Universit\u00e9, G7H 2B1, Chicoutimi, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hao", 
        "givenName": "Jianguo", 
        "id": "sg:person.015222531515.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015222531515.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "LIARA Laboratory, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 Boulevard de l\u2019Universit\u00e9, G7H 2B1, Chicoutimi, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bouzouane", 
        "givenName": "Abdenour", 
        "id": "sg:person.015150332556.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015150332556.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "LIARA Laboratory, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 Boulevard de l\u2019Universit\u00e9, G7H 2B1, Chicoutimi, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bouchard", 
        "givenName": "Bruno", 
        "id": "sg:person.014102265512.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014102265512.68"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "LIARA Laboratory, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 Boulevard de l\u2019Universit\u00e9, G7H 2B1, Chicoutimi, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gaboury", 
        "givenName": "S\u00e9bastien", 
        "id": "sg:person.0761614046.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761614046.94"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.pmcj.2009.04.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002419569"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3109/09638289709166525", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004190609"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10489-013-0451-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005427309", 
          "https://doi.org/10.1007/s10489-013-0451-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.inffus.2015.06.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007506736"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1922649.1922653", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010328552"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0004-3702(90)90041-w", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010466171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0004-3702(90)90041-w", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010466171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.engappai.2012.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011610852"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.future.2013.01.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011867666"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1015571330", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-59830-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015571330", 
          "https://doi.org/10.1007/978-3-642-59830-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-59830-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015571330", 
          "https://doi.org/10.1007/978-3-642-59830-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.pmcj.2006.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015809463"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00155578", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016957726", 
          "https://doi.org/10.1007/bf00155578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00155578", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016957726", 
          "https://doi.org/10.1007/bf00155578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.engappai.2013.08.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020702196"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2783258.2783408", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020980704"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-25167-2_9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022150660", 
          "https://doi.org/10.1007/978-3-642-25167-2_9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ipm.2011.04.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024788516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.future.2016.05.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025629737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-13186-3_31", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029058332", 
          "https://doi.org/10.1007/978-3-319-13186-3_31"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12652-014-0219-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029448212", 
          "https://doi.org/10.1007/s12652-014-0219-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1541880.1541882", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030762489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2910674.2910707", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030802475"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mprv.2010.7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033194203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2015.04.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034234730"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2015/643273", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035094291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1882471.1882478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035938734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2910674.2910689", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037510324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2005.11.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037766032"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2013.05.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041619607"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/08839510701492579", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042210598"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1978802.1978815", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045750431"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-79860-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050403817", 
          "https://doi.org/10.1007/978-3-540-79860-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-79860-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050403817", 
          "https://doi.org/10.1007/978-3-540-79860-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-24646-6_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052944211", 
          "https://doi.org/10.1007/978-3-540-24646-6_1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-24646-6_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052944211", 
          "https://doi.org/10.1007/978-3-540-24646-6_1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/comst.2014.2320099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061258213"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jproc.2013.2262913", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061297811"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mis.2008.19", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061406049"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmca.2004.838456", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061795022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcc.2012.2198883", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061798435"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0218213001000441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062964245"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/me0592", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071311979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4017/gt.2009.08.02.002.00", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071875561"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2991/978-94-91216-05-3_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088313041"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2991/978-94-91216-05-3_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088313041", 
          "https://doi.org/10.2991/978-94-91216-05-3_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2991/978-94-91216-05-3_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088313041", 
          "https://doi.org/10.2991/978-94-91216-05-3_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/seus-wccia.2006.25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094230301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ie.2016.24", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094396484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2011.6126349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094585415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ie.2010.22", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095060909"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ie.2016.52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095670351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ia.2013.6595187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095823739"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9781139924801", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098687581"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-06", 
    "datePublishedReg": "2018-06-01", 
    "description": "Recent research in ambient intelligence allows wireless sensor networks to perceive environmental states and their changes in smart environments. An intelligent living environment could not only provide better interactions with its ambiance, inside electrical devices and everyday objects, but also offer smart services, even smart assistance to disabled or elderly people when necessary. This paper proposes a new inference engine based on the formal concept analysis to achieve activity prediction and recognition, even abnormal behavioral pattern detection for ambient-assisted living. According to occupants\u2019 historical data, we explore useful frequent patterns to guide future prediction, recognition and detection tasks. Like the way of human reasoning, the engine could incrementally infer the most probable activity according to successive observations. Furthermore, we propose a hierarchical clustering approach to merge activities according to their semantic similarities. As an optimized knowledge discovery approach in hierarchical ambient intelligence environments, it could optimize the prediction accuracies at the earliest stages when only a few observations are available.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12652-017-0467-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043999", 
        "issn": [
          "1868-5137", 
          "1868-5145"
        ], 
        "name": "Journal of Ambient Intelligence and Humanized Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Activity inference engine for real-time cognitive assistance in smart environments", 
    "pagination": "679-698", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3f7aff38c32238ae8c39ae2e45456a7da1b0028e315c22d59ad8ab946f0a3d49"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12652-017-0467-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1084036127"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12652-017-0467-7", 
      "https://app.dimensions.ai/details/publication/pub.1084036127"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:43", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000363_0000000363/records_70064_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs12652-017-0467-7"
  }
]
 

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/s12652-017-0467-7'

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/s12652-017-0467-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12652-017-0467-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12652-017-0467-7'


 

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

242 TRIPLES      21 PREDICATES      76 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12652-017-0467-7 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N3a1637f8383e46e38432d39b92e8a5a3
4 schema:citation sg:pub.10.1007/978-3-319-13186-3_31
5 sg:pub.10.1007/978-3-540-24646-6_1
6 sg:pub.10.1007/978-3-540-79860-6
7 sg:pub.10.1007/978-3-642-25167-2_9
8 sg:pub.10.1007/978-3-642-59830-2
9 sg:pub.10.1007/bf00155578
10 sg:pub.10.1007/s10489-013-0451-7
11 sg:pub.10.1007/s12652-014-0219-x
12 sg:pub.10.2991/978-94-91216-05-3_2
13 https://app.dimensions.ai/details/publication/pub.1015571330
14 https://doi.org/10.1016/0004-3702(90)90041-w
15 https://doi.org/10.1016/j.engappai.2012.05.002
16 https://doi.org/10.1016/j.engappai.2013.08.004
17 https://doi.org/10.1016/j.eswa.2013.05.009
18 https://doi.org/10.1016/j.eswa.2015.04.024
19 https://doi.org/10.1016/j.future.2013.01.010
20 https://doi.org/10.1016/j.future.2016.05.039
21 https://doi.org/10.1016/j.inffus.2015.06.004
22 https://doi.org/10.1016/j.ins.2005.11.014
23 https://doi.org/10.1016/j.ipm.2011.04.003
24 https://doi.org/10.1016/j.pmcj.2006.12.001
25 https://doi.org/10.1016/j.pmcj.2009.04.001
26 https://doi.org/10.1017/cbo9781139924801
27 https://doi.org/10.1080/08839510701492579
28 https://doi.org/10.1109/comst.2014.2320099
29 https://doi.org/10.1109/ia.2013.6595187
30 https://doi.org/10.1109/iccv.2011.6126349
31 https://doi.org/10.1109/ie.2010.22
32 https://doi.org/10.1109/ie.2016.24
33 https://doi.org/10.1109/ie.2016.52
34 https://doi.org/10.1109/jproc.2013.2262913
35 https://doi.org/10.1109/mis.2008.19
36 https://doi.org/10.1109/mprv.2010.7
37 https://doi.org/10.1109/seus-wccia.2006.25
38 https://doi.org/10.1109/tsmca.2004.838456
39 https://doi.org/10.1109/tsmcc.2012.2198883
40 https://doi.org/10.1142/s0218213001000441
41 https://doi.org/10.1145/1541880.1541882
42 https://doi.org/10.1145/1882471.1882478
43 https://doi.org/10.1145/1922649.1922653
44 https://doi.org/10.1145/1978802.1978815
45 https://doi.org/10.1145/2783258.2783408
46 https://doi.org/10.1145/2910674.2910689
47 https://doi.org/10.1145/2910674.2910707
48 https://doi.org/10.1155/2015/643273
49 https://doi.org/10.2991/978-94-91216-05-3_2
50 https://doi.org/10.3109/09638289709166525
51 https://doi.org/10.3414/me0592
52 https://doi.org/10.4017/gt.2009.08.02.002.00
53 schema:datePublished 2018-06
54 schema:datePublishedReg 2018-06-01
55 schema:description Recent research in ambient intelligence allows wireless sensor networks to perceive environmental states and their changes in smart environments. An intelligent living environment could not only provide better interactions with its ambiance, inside electrical devices and everyday objects, but also offer smart services, even smart assistance to disabled or elderly people when necessary. This paper proposes a new inference engine based on the formal concept analysis to achieve activity prediction and recognition, even abnormal behavioral pattern detection for ambient-assisted living. According to occupants’ historical data, we explore useful frequent patterns to guide future prediction, recognition and detection tasks. Like the way of human reasoning, the engine could incrementally infer the most probable activity according to successive observations. Furthermore, we propose a hierarchical clustering approach to merge activities according to their semantic similarities. As an optimized knowledge discovery approach in hierarchical ambient intelligence environments, it could optimize the prediction accuracies at the earliest stages when only a few observations are available.
56 schema:genre research_article
57 schema:inLanguage en
58 schema:isAccessibleForFree false
59 schema:isPartOf N1e4a7aca6277461eab519cf3e7645eba
60 Nc011a03f36694a8db4142c405c5c99ba
61 sg:journal.1043999
62 schema:name Activity inference engine for real-time cognitive assistance in smart environments
63 schema:pagination 679-698
64 schema:productId N384151fdb8df4a3fb857d1e2c93bec36
65 N70d8543a72c5451ea608bcea8e58bc04
66 N9b25c05653aa4d16af3a237b9f41e7a1
67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084036127
68 https://doi.org/10.1007/s12652-017-0467-7
69 schema:sdDatePublished 2019-04-11T12:43
70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
71 schema:sdPublisher N287bb61dc0694a879a4d68c284fe4990
72 schema:url https://link.springer.com/10.1007%2Fs12652-017-0467-7
73 sgo:license sg:explorer/license/
74 sgo:sdDataset articles
75 rdf:type schema:ScholarlyArticle
76 N138cc122d3d44b29b4b240d068733378 rdf:first sg:person.0761614046.94
77 rdf:rest rdf:nil
78 N175190937f2b475490e1288d23e229a2 schema:name LIARA Laboratory, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, G7H 2B1, Chicoutimi, Canada
79 rdf:type schema:Organization
80 N1a4c21059cf844d0892aa1d38cd946be schema:name LIARA Laboratory, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, G7H 2B1, Chicoutimi, Canada
81 rdf:type schema:Organization
82 N1e4a7aca6277461eab519cf3e7645eba schema:issueNumber 3
83 rdf:type schema:PublicationIssue
84 N287bb61dc0694a879a4d68c284fe4990 schema:name Springer Nature - SN SciGraph project
85 rdf:type schema:Organization
86 N384151fdb8df4a3fb857d1e2c93bec36 schema:name dimensions_id
87 schema:value pub.1084036127
88 rdf:type schema:PropertyValue
89 N3a1637f8383e46e38432d39b92e8a5a3 rdf:first sg:person.015222531515.49
90 rdf:rest N9e7aa2c07d014a32880011f1f58577cc
91 N6bb38d45dfbe4a2c8b4e45ca491aaa0d schema:name LIARA Laboratory, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, G7H 2B1, Chicoutimi, Canada
92 rdf:type schema:Organization
93 N70d8543a72c5451ea608bcea8e58bc04 schema:name doi
94 schema:value 10.1007/s12652-017-0467-7
95 rdf:type schema:PropertyValue
96 N9b25c05653aa4d16af3a237b9f41e7a1 schema:name readcube_id
97 schema:value 3f7aff38c32238ae8c39ae2e45456a7da1b0028e315c22d59ad8ab946f0a3d49
98 rdf:type schema:PropertyValue
99 N9e7aa2c07d014a32880011f1f58577cc rdf:first sg:person.015150332556.91
100 rdf:rest Ne6716018471b485ea8adb7066a0454fe
101 Nc011a03f36694a8db4142c405c5c99ba schema:volumeNumber 9
102 rdf:type schema:PublicationVolume
103 Nd84d07ce0e0d45afa28866e8c363bc3a schema:name LIARA Laboratory, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, G7H 2B1, Chicoutimi, Canada
104 rdf:type schema:Organization
105 Ne6716018471b485ea8adb7066a0454fe rdf:first sg:person.014102265512.68
106 rdf:rest N138cc122d3d44b29b4b240d068733378
107 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
108 schema:name Information and Computing Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
111 schema:name Artificial Intelligence and Image Processing
112 rdf:type schema:DefinedTerm
113 sg:journal.1043999 schema:issn 1868-5137
114 1868-5145
115 schema:name Journal of Ambient Intelligence and Humanized Computing
116 rdf:type schema:Periodical
117 sg:person.014102265512.68 schema:affiliation N6bb38d45dfbe4a2c8b4e45ca491aaa0d
118 schema:familyName Bouchard
119 schema:givenName Bruno
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014102265512.68
121 rdf:type schema:Person
122 sg:person.015150332556.91 schema:affiliation N1a4c21059cf844d0892aa1d38cd946be
123 schema:familyName Bouzouane
124 schema:givenName Abdenour
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015150332556.91
126 rdf:type schema:Person
127 sg:person.015222531515.49 schema:affiliation Nd84d07ce0e0d45afa28866e8c363bc3a
128 schema:familyName Hao
129 schema:givenName Jianguo
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015222531515.49
131 rdf:type schema:Person
132 sg:person.0761614046.94 schema:affiliation N175190937f2b475490e1288d23e229a2
133 schema:familyName Gaboury
134 schema:givenName Sébastien
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761614046.94
136 rdf:type schema:Person
137 sg:pub.10.1007/978-3-319-13186-3_31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029058332
138 https://doi.org/10.1007/978-3-319-13186-3_31
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/978-3-540-24646-6_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052944211
141 https://doi.org/10.1007/978-3-540-24646-6_1
142 rdf:type schema:CreativeWork
143 sg:pub.10.1007/978-3-540-79860-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050403817
144 https://doi.org/10.1007/978-3-540-79860-6
145 rdf:type schema:CreativeWork
146 sg:pub.10.1007/978-3-642-25167-2_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022150660
147 https://doi.org/10.1007/978-3-642-25167-2_9
148 rdf:type schema:CreativeWork
149 sg:pub.10.1007/978-3-642-59830-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015571330
150 https://doi.org/10.1007/978-3-642-59830-2
151 rdf:type schema:CreativeWork
152 sg:pub.10.1007/bf00155578 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016957726
153 https://doi.org/10.1007/bf00155578
154 rdf:type schema:CreativeWork
155 sg:pub.10.1007/s10489-013-0451-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005427309
156 https://doi.org/10.1007/s10489-013-0451-7
157 rdf:type schema:CreativeWork
158 sg:pub.10.1007/s12652-014-0219-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1029448212
159 https://doi.org/10.1007/s12652-014-0219-x
160 rdf:type schema:CreativeWork
161 sg:pub.10.2991/978-94-91216-05-3_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088313041
162 https://doi.org/10.2991/978-94-91216-05-3_2
163 rdf:type schema:CreativeWork
164 https://app.dimensions.ai/details/publication/pub.1015571330 schema:CreativeWork
165 https://doi.org/10.1016/0004-3702(90)90041-w schema:sameAs https://app.dimensions.ai/details/publication/pub.1010466171
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.engappai.2012.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011610852
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.engappai.2013.08.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020702196
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.eswa.2013.05.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041619607
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.eswa.2015.04.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034234730
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.future.2013.01.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011867666
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.future.2016.05.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025629737
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.inffus.2015.06.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007506736
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.ins.2005.11.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037766032
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/j.ipm.2011.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024788516
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/j.pmcj.2006.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015809463
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/j.pmcj.2009.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002419569
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1017/cbo9781139924801 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098687581
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1080/08839510701492579 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042210598
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1109/comst.2014.2320099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061258213
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1109/ia.2013.6595187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095823739
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1109/iccv.2011.6126349 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094585415
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1109/ie.2010.22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095060909
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1109/ie.2016.24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094396484
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1109/ie.2016.52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095670351
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1109/jproc.2013.2262913 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061297811
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1109/mis.2008.19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061406049
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1109/mprv.2010.7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033194203
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1109/seus-wccia.2006.25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094230301
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1109/tsmca.2004.838456 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061795022
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1109/tsmcc.2012.2198883 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061798435
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1142/s0218213001000441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062964245
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1145/1541880.1541882 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030762489
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1145/1882471.1882478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035938734
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1145/1922649.1922653 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010328552
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1145/1978802.1978815 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045750431
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1145/2783258.2783408 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020980704
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1145/2910674.2910689 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037510324
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1145/2910674.2910707 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030802475
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1155/2015/643273 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035094291
234 rdf:type schema:CreativeWork
235 https://doi.org/10.2991/978-94-91216-05-3_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088313041
236 rdf:type schema:CreativeWork
237 https://doi.org/10.3109/09638289709166525 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004190609
238 rdf:type schema:CreativeWork
239 https://doi.org/10.3414/me0592 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071311979
240 rdf:type schema:CreativeWork
241 https://doi.org/10.4017/gt.2009.08.02.002.00 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071875561
242 rdf:type schema:CreativeWork
 




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


...