Multiple Kinect based system to monitor and analyze key performance indicators of physical training View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-12-14

AUTHORS

Karolis Ryselis, Tautvydas Petkus, Tomas Blažauskas, Rytis Maskeliūnas, Robertas Damaševičius

ABSTRACT

Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent. More... »

PAGES

51

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13673-020-00256-4

DOI

http://dx.doi.org/10.1186/s13673-020-00256-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1133494062


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": "Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania", 
          "id": "http://www.grid.ac/institutes/grid.6901.e", 
          "name": [
            "Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ryselis", 
        "givenName": "Karolis", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "UAB Telesoftas, 44150, Kaunas, Lithuania", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "UAB Telesoftas, 44150, Kaunas, Lithuania"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Petkus", 
        "givenName": "Tautvydas", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania", 
          "id": "http://www.grid.ac/institutes/grid.6901.e", 
          "name": [
            "Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bla\u017eauskas", 
        "givenName": "Tomas", 
        "id": "sg:person.07640643375.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07640643375.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Multimedia Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania", 
          "id": "http://www.grid.ac/institutes/grid.6901.e", 
          "name": [
            "Department of Multimedia Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maskeli\u016bnas", 
        "givenName": "Rytis", 
        "id": "sg:person.07601177551.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07601177551.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania", 
          "id": "http://www.grid.ac/institutes/grid.6901.e", 
          "name": [
            "Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dama\u0161evi\u010dius", 
        "givenName": "Robertas", 
        "id": "sg:person.016412337535.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016412337535.97"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11517-018-1868-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106236439", 
          "https://doi.org/10.1007/s11517-018-1868-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12938-015-0070-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033023566", 
          "https://doi.org/10.1186/s12938-015-0070-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-030-55807-9_37", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1130213947", 
          "https://doi.org/10.1007/978-3-030-55807-9_37"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12984-019-0492-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111767643", 
          "https://doi.org/10.1186/s12984-019-0492-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11042-016-3759-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049936018", 
          "https://doi.org/10.1007/s11042-016-3759-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s42600-019-00029-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1122920195", 
          "https://doi.org/10.1007/s42600-019-00029-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-33868-7_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033298856", 
          "https://doi.org/10.1007/978-3-642-33868-7_10"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2020-12-14", 
    "datePublishedReg": "2020-12-14", 
    "description": "Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test\u2013retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s13673-020-00256-4", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1136381", 
        "issn": [
          "2192-1962"
        ], 
        "name": "Human-centric Computing and Information Sciences", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "keywords": [
      "human skeleton tracking", 
      "skeleton tracking", 
      "Kinect device", 
      "novel data fusion algorithm", 
      "multi-Kinect system", 
      "smart health applications", 
      "data fusion algorithm", 
      "multiple Kinects", 
      "fusion algorithm", 
      "recognition errors", 
      "skeletal data", 
      "standard pose", 
      "Kinect system", 
      "rehabilitation domains", 
      "human motion", 
      "key performance indicators", 
      "health applications", 
      "reliability metrics", 
      "algebraic operations", 
      "high accuracy", 
      "pose", 
      "practical solution", 
      "dynamic characteristics", 
      "tracking", 
      "human joints", 
      "vector space", 
      "test-retest reliability metrics", 
      "performance indicators", 
      "accuracy", 
      "Kinect", 
      "usability", 
      "system", 
      "spatial coverage", 
      "reliability", 
      "algorithm", 
      "deployment", 
      "devices", 
      "error", 
      "metrics", 
      "fusion", 
      "applications", 
      "operation", 
      "joints", 
      "domain", 
      "motion", 
      "training", 
      "space", 
      "solution", 
      "physical exercising", 
      "order", 
      "data", 
      "coverage", 
      "characteristics", 
      "improvement", 
      "analysis", 
      "intra-session reliability", 
      "comparison", 
      "length", 
      "units", 
      "lack", 
      "exercising", 
      "indicators", 
      "medicine", 
      "subjects", 
      "lying", 
      "physical training", 
      "sports medicine", 
      "paper"
    ], 
    "name": "Multiple Kinect based system to monitor and analyze key performance indicators of physical training", 
    "pagination": "51", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1133494062"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13673-020-00256-4"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13673-020-00256-4", 
      "https://app.dimensions.ai/details/publication/pub.1133494062"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:41", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_845.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s13673-020-00256-4"
  }
]
 

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.10.1186/s13673-020-00256-4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13673-020-00256-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13673-020-00256-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13673-020-00256-4'


 

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

183 TRIPLES      21 PREDICATES      99 URIs      84 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13673-020-00256-4 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N5ede252760df419182eae0e31fb0f371
4 schema:citation sg:pub.10.1007/978-3-030-55807-9_37
5 sg:pub.10.1007/978-3-642-33868-7_10
6 sg:pub.10.1007/s11042-016-3759-6
7 sg:pub.10.1007/s11517-018-1868-2
8 sg:pub.10.1007/s42600-019-00029-8
9 sg:pub.10.1186/s12938-015-0070-0
10 sg:pub.10.1186/s12984-019-0492-1
11 schema:datePublished 2020-12-14
12 schema:datePublishedReg 2020-12-14
13 schema:description Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.
14 schema:genre article
15 schema:isAccessibleForFree true
16 schema:isPartOf N0a403685938942c6ab47af39cf9c7775
17 Nb5a6656c10284266928d77118f0a04d1
18 sg:journal.1136381
19 schema:keywords Kinect
20 Kinect device
21 Kinect system
22 accuracy
23 algebraic operations
24 algorithm
25 analysis
26 applications
27 characteristics
28 comparison
29 coverage
30 data
31 data fusion algorithm
32 deployment
33 devices
34 domain
35 dynamic characteristics
36 error
37 exercising
38 fusion
39 fusion algorithm
40 health applications
41 high accuracy
42 human joints
43 human motion
44 human skeleton tracking
45 improvement
46 indicators
47 intra-session reliability
48 joints
49 key performance indicators
50 lack
51 length
52 lying
53 medicine
54 metrics
55 motion
56 multi-Kinect system
57 multiple Kinects
58 novel data fusion algorithm
59 operation
60 order
61 paper
62 performance indicators
63 physical exercising
64 physical training
65 pose
66 practical solution
67 recognition errors
68 rehabilitation domains
69 reliability
70 reliability metrics
71 skeletal data
72 skeleton tracking
73 smart health applications
74 solution
75 space
76 spatial coverage
77 sports medicine
78 standard pose
79 subjects
80 system
81 test-retest reliability metrics
82 tracking
83 training
84 units
85 usability
86 vector space
87 schema:name Multiple Kinect based system to monitor and analyze key performance indicators of physical training
88 schema:pagination 51
89 schema:productId N61873b6af6fb44499a9c6e2271789b48
90 Na3ed7aacc63b49c5a8213e84f9529b83
91 schema:sameAs https://app.dimensions.ai/details/publication/pub.1133494062
92 https://doi.org/10.1186/s13673-020-00256-4
93 schema:sdDatePublished 2022-12-01T06:41
94 schema:sdLicense https://scigraph.springernature.com/explorer/license/
95 schema:sdPublisher Na3dd32aae4284100a523d73804876a8f
96 schema:url https://doi.org/10.1186/s13673-020-00256-4
97 sgo:license sg:explorer/license/
98 sgo:sdDataset articles
99 rdf:type schema:ScholarlyArticle
100 N0770107bf28944858a31596f22c75c2a rdf:first sg:person.07601177551.18
101 rdf:rest N9ec58b48f6504d4aa9aa117b7f32956a
102 N0a403685938942c6ab47af39cf9c7775 schema:volumeNumber 10
103 rdf:type schema:PublicationVolume
104 N33d5615ba91c42cbbd6e6f2010c5189f schema:affiliation grid-institutes:grid.6901.e
105 schema:familyName Ryselis
106 schema:givenName Karolis
107 rdf:type schema:Person
108 N5ede252760df419182eae0e31fb0f371 rdf:first N33d5615ba91c42cbbd6e6f2010c5189f
109 rdf:rest N9ca0a148c4ee4038828aab63ec585a05
110 N61873b6af6fb44499a9c6e2271789b48 schema:name dimensions_id
111 schema:value pub.1133494062
112 rdf:type schema:PropertyValue
113 N6de6cebb546a45379a996d4f9a67bbed schema:affiliation grid-institutes:None
114 schema:familyName Petkus
115 schema:givenName Tautvydas
116 rdf:type schema:Person
117 N9ca0a148c4ee4038828aab63ec585a05 rdf:first N6de6cebb546a45379a996d4f9a67bbed
118 rdf:rest Nd19d392587dd47e8aa30f27d358a0365
119 N9ec58b48f6504d4aa9aa117b7f32956a rdf:first sg:person.016412337535.97
120 rdf:rest rdf:nil
121 Na3dd32aae4284100a523d73804876a8f schema:name Springer Nature - SN SciGraph project
122 rdf:type schema:Organization
123 Na3ed7aacc63b49c5a8213e84f9529b83 schema:name doi
124 schema:value 10.1186/s13673-020-00256-4
125 rdf:type schema:PropertyValue
126 Nb5a6656c10284266928d77118f0a04d1 schema:issueNumber 1
127 rdf:type schema:PublicationIssue
128 Nd19d392587dd47e8aa30f27d358a0365 rdf:first sg:person.07640643375.41
129 rdf:rest N0770107bf28944858a31596f22c75c2a
130 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
131 schema:name Information and Computing Sciences
132 rdf:type schema:DefinedTerm
133 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
134 schema:name Artificial Intelligence and Image Processing
135 rdf:type schema:DefinedTerm
136 sg:journal.1136381 schema:issn 2192-1962
137 schema:name Human-centric Computing and Information Sciences
138 schema:publisher Springer Nature
139 rdf:type schema:Periodical
140 sg:person.016412337535.97 schema:affiliation grid-institutes:grid.6901.e
141 schema:familyName Damaševičius
142 schema:givenName Robertas
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016412337535.97
144 rdf:type schema:Person
145 sg:person.07601177551.18 schema:affiliation grid-institutes:grid.6901.e
146 schema:familyName Maskeliūnas
147 schema:givenName Rytis
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07601177551.18
149 rdf:type schema:Person
150 sg:person.07640643375.41 schema:affiliation grid-institutes:grid.6901.e
151 schema:familyName Blažauskas
152 schema:givenName Tomas
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07640643375.41
154 rdf:type schema:Person
155 sg:pub.10.1007/978-3-030-55807-9_37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1130213947
156 https://doi.org/10.1007/978-3-030-55807-9_37
157 rdf:type schema:CreativeWork
158 sg:pub.10.1007/978-3-642-33868-7_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033298856
159 https://doi.org/10.1007/978-3-642-33868-7_10
160 rdf:type schema:CreativeWork
161 sg:pub.10.1007/s11042-016-3759-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049936018
162 https://doi.org/10.1007/s11042-016-3759-6
163 rdf:type schema:CreativeWork
164 sg:pub.10.1007/s11517-018-1868-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106236439
165 https://doi.org/10.1007/s11517-018-1868-2
166 rdf:type schema:CreativeWork
167 sg:pub.10.1007/s42600-019-00029-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122920195
168 https://doi.org/10.1007/s42600-019-00029-8
169 rdf:type schema:CreativeWork
170 sg:pub.10.1186/s12938-015-0070-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033023566
171 https://doi.org/10.1186/s12938-015-0070-0
172 rdf:type schema:CreativeWork
173 sg:pub.10.1186/s12984-019-0492-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111767643
174 https://doi.org/10.1186/s12984-019-0492-1
175 rdf:type schema:CreativeWork
176 grid-institutes:None schema:alternateName UAB Telesoftas, 44150, Kaunas, Lithuania
177 schema:name UAB Telesoftas, 44150, Kaunas, Lithuania
178 rdf:type schema:Organization
179 grid-institutes:grid.6901.e schema:alternateName Department of Multimedia Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania
180 Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania
181 schema:name Department of Multimedia Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania
182 Department of Software Engineering, Kaunas University of Technology, 51368, Kaunas, Lithuania
183 rdf:type schema:Organization
 




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


...