{"id":"https://openalex.org/W2804907753","doi":"https://doi.org/10.3390/s18051654","title":"Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry","display_name":"Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry","publication_year":2018,"publication_date":"2018-05-22","ids":{"openalex":"https://openalex.org/W2804907753","doi":"https://doi.org/10.3390/s18051654","mag":"2804907753","pmid":"https://pubmed.ncbi.nlm.nih.gov/29786659"},"language":"en","primary_location":{"id":"doi:10.3390/s18051654","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18051654","pdf_url":"https://www.mdpi.com/1424-8220/18/5/1654/pdf?version=1526973863","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"review","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/18/5/1654/pdf?version=1526973863","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034892320","display_name":"Ahmed Nait Aicha","orcid":"https://orcid.org/0000-0001-8966-9736"},"institutions":[{"id":"https://openalex.org/I55106644","display_name":"Amsterdam University of Applied Sciences","ror":"https://ror.org/00y2z2s03","country_code":"NL","type":"education","lineage":["https://openalex.org/I55106644"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Ahmed Nait Aicha","raw_affiliation_strings":["Department of Computer Science, Amsterdam University of Applied Sciences, 1091 GM Amsterdam, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0001-8966-9736","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Amsterdam University of Applied Sciences, 1091 GM Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I55106644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074888054","display_name":"Gwenn Englebienne","orcid":"https://orcid.org/0000-0002-3130-2082"},"institutions":[{"id":"https://openalex.org/I4210142372","display_name":"Human Media","ror":"https://ror.org/04072nk43","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210142372"]},{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL","US"],"is_corresponding":false,"raw_author_name":"Gwenn Englebienne","raw_affiliation_strings":["Human Media Interaction, University of Twente, 7522 NH Enschede, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Human Media Interaction, University of Twente, 7522 NH Enschede, The Netherlands","institution_ids":["https://openalex.org/I4210142372","https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001141884","display_name":"Kimberley S. van Schooten","orcid":"https://orcid.org/0000-0003-0902-8440"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I83657090","display_name":"Neuroscience Research Australia","ror":"https://ror.org/01g7s6g79","country_code":"AU","type":"facility","lineage":["https://openalex.org/I83657090"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kimberley S. Van Schooten","raw_affiliation_strings":["Neuroscience Research Australia, University of New South Wales, Sydney 2031, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0902-8440","affiliations":[{"raw_affiliation_string":"Neuroscience Research Australia, University of New South Wales, Sydney 2031, Australia","institution_ids":["https://openalex.org/I83657090","https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011439774","display_name":"Mirjam Pijnappels","orcid":"https://orcid.org/0000-0001-8416-2602"},"institutions":[{"id":"https://openalex.org/I865915315","display_name":"Vrije Universiteit Amsterdam","ror":"https://ror.org/008xxew50","country_code":"NL","type":"education","lineage":["https://openalex.org/I865915315"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mirjam Pijnappels","raw_affiliation_strings":["Department of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0001-8416-2602","affiliations":[{"raw_affiliation_string":"Department of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I865915315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049349937","display_name":"Ben Kr\u00f6se","orcid":"https://orcid.org/0000-0003-1237-0618"},"institutions":[{"id":"https://openalex.org/I55106644","display_name":"Amsterdam University of Applied Sciences","ror":"https://ror.org/00y2z2s03","country_code":"NL","type":"education","lineage":["https://openalex.org/I55106644"]},{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ben Kr\u00f6se","raw_affiliation_strings":["Department of Computer Science, Amsterdam University of Applied Sciences, 1091 GM Amsterdam, The Netherlands","Informatics Institute, University of Amsterdam, 1098 XH Amsterdam, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0003-1237-0618","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Amsterdam University of Applied Sciences, 1091 GM Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I55106644"]},{"raw_affiliation_string":"Informatics Institute, University of Amsterdam, 1098 XH Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5034892320"],"corresponding_institution_ids":["https://openalex.org/I55106644"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":30.7482,"has_fulltext":true,"cited_by_count":206,"citation_normalized_percentile":{"value":0.9978488,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"18","issue":"5","first_page":"1654","last_page":"1654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9728999733924866,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7929782271385193},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.7484939694404602},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7310848236083984},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6882572770118713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6839088797569275},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5924209356307983},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5018863677978516},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.45139452815055847},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.45113760232925415},{"id":"https://openalex.org/keywords/fall-prevention","display_name":"Fall prevention","score":0.4329381287097931},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.41151291131973267},{"id":"https://openalex.org/keywords/poison-control","display_name":"Poison control","score":0.27320992946624756},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17589181661605835},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15508481860160828},{"id":"https://openalex.org/keywords/injury-prevention","display_name":"Injury prevention","score":0.12615859508514404}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7929782271385193},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.7484939694404602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7310848236083984},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6882572770118713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6839088797569275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5924209356307983},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5018863677978516},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.45139452815055847},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.45113760232925415},{"id":"https://openalex.org/C2776516907","wikidata":"https://www.wikidata.org/wiki/Q5432181","display_name":"Fall prevention","level":4,"score":0.4329381287097931},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.41151291131973267},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.27320992946624756},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17589181661605835},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15508481860160828},{"id":"https://openalex.org/C190385971","wikidata":"https://www.wikidata.org/wiki/Q373494","display_name":"Injury prevention","level":3,"score":0.12615859508514404},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":"Q000517","qualifier_name":"prevention & control","is_major_topic":false},{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":"Q000517","qualifier_name":"prevention & control","is_major_topic":false},{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":"Q000517","qualifier_name":"prevention & control","is_major_topic":false},{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":"Q000517","qualifier_name":"prevention & control","is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000203","descriptor_name":"Activities of Daily Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000203","descriptor_name":"Activities of Daily Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000203","descriptor_name":"Activities of Daily Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000203","descriptor_name":"Activities of Daily Living","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000369","descriptor_name":"Aged, 80 and over","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D018670","descriptor_name":"Monitoring, Ambulatory","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D018670","descriptor_name":"Monitoring, Ambulatory","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D018670","descriptor_name":"Monitoring, Ambulatory","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D018670","descriptor_name":"Monitoring, Ambulatory","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D061725","descriptor_name":"Accelerometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D061725","descriptor_name":"Accelerometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D061725","descriptor_name":"Accelerometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D061725","descriptor_name":"Accelerometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":12,"locations":[{"id":"doi:10.3390/s18051654","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18051654","pdf_url":"https://www.mdpi.com/1424-8220/18/5/1654/pdf?version=1526973863","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:29786659","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29786659","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:research.vu.nl:openaire/4b08de4d-d2fd-46f5-9d75-ffa99270cbc9","is_oa":true,"landing_page_url":"https://research.vu.nl/en/publications/4b08de4d-d2fd-46f5-9d75-ffa99270cbc9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401107","display_name":"VU Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I865915315","host_organization_name":"Vrije Universiteit Amsterdam","host_organization_lineage":["https://openalex.org/I865915315"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Nait Aicha, A, Englebienne, G, van Schooten, K S, Pijnappels, M & Kr\u00f6se, B 2018, 'Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry', Sensors (Switzerland), vol. 18, no. 5, 1654, pp. 1-14. https://doi.org/10.3390/s18051654","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:ris.utwente.nl:openaire/26ba33a6-65e3-434d-8e29-37457049830e","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/26ba33a6-65e3-434d-8e29-37457049830e","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Nait Aicha, A, Englebienne, G, Van Schooten, K, Pijnappels, M & Kr\u00f6se, B 2018, 'Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry', Sensors, vol. 18, no. 5, 1654. https://doi.org/10.3390/s18051654","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:uvapub:oai:dare.uva.nl:publications/8302c005-22aa-44dc-9d44-e18f8e808769","is_oa":true,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/deep-learning-to-predict-falls-in-older-adults-based-on-dailylife-trunk-accelerometry(8302c005-22aa-44dc-9d44-e18f8e808769).html","pdf_url":"https://dare.uva.nl/personal/pure/en/publications/deep-learning-to-predict-falls-in-older-adults-based-on-dailylife-trunk-accelerometry(8302c005-22aa-44dc-9d44-e18f8e808769).html","source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, 18(5):1654. Multidisciplinary Digital Publishing Institute (MDPI)","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:hbo:oai:hbokennisbank.nl:amsterdam_pure:oai:pure.hva.nl:publications/8281b067-1d76-4fa0-af75-f6cf24a5a928","is_oa":true,"landing_page_url":"https://research.hva.nl/en/publications/8281b067-1d76-4fa0-af75-f6cf24a5a928","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, 18(5). MDPI","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:dare.uva.nl:openaire/8302c005-22aa-44dc-9d44-e18f8e808769","is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/deep-learning-to-predict-falls-in-older-adults-based-on-dailylife-trunk-accelerometry(8302c005-22aa-44dc-9d44-e18f8e808769).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nait Aicha, A, Englebienne, G, van Schooten, K S, Pijnappels, M & Kr\u00f6se, B 2018, 'Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry', Sensors, vol. 18, no. 5, 1654. https://doi.org/10.3390/s18051654","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:dare.uva.nl:publications/8302c005-22aa-44dc-9d44-e18f8e808769","is_oa":true,"landing_page_url":"http://dare.uva.nl/personal/pure/en/publications/deep-learning-to-predict-falls-in-older-adults-based-on-dailylife-trunk-accelerometry(8302c005-22aa-44dc-9d44-e18f8e808769).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nait Aicha, A, Englebienne, G, van Schooten, K S, Pijnappels, M & Kr\u00f6se, B 2018, 'Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry', Sensors, vol. 18, no. 5, 1654. https://doi.org/10.3390/s18051654","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:europepmc.org:4940416","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5981199","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:mdpi.com:/1424-8220/18/5/1654/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s18051654","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 18; Issue 5; Pages: 1654","raw_type":"Text"},{"id":"pmh:oai:research.vu.nl:openaire_cris_publications/4b08de4d-d2fd-46f5-9d75-ffa99270cbc9","is_oa":true,"landing_page_url":"https://hdl.handle.net/1871.1/4b08de4d-d2fd-46f5-9d75-ffa99270cbc9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401107","display_name":"VU Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I865915315","host_organization_name":"Vrije Universiteit Amsterdam","host_organization_lineage":["https://openalex.org/I865915315"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nait Aicha, A, Englebienne, G, van Schooten, K S, Pijnappels, M & Kr\u00f6se, B 2018, 'Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry', Sensors (Switzerland), vol. 18, no. 5, 1654, pp. 1-14. https://doi.org/10.3390/s18051654","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/unsworks_52767","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/unsworks_52767","pdf_url":null,"source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors Switzerland, 18, 5, 1654","raw_type":"http://purl.org/coar/resource_type/c_6501"}],"best_oa_location":{"id":"doi:10.3390/s18051654","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s18051654","pdf_url":"https://www.mdpi.com/1424-8220/18/5/1654/pdf?version=1526973863","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2804907753.pdf","grobid_xml":"https://content.openalex.org/works/W2804907753.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W176103743","https://openalex.org/W1563098518","https://openalex.org/W1614862348","https://openalex.org/W1847168837","https://openalex.org/W1974704788","https://openalex.org/W1989496527","https://openalex.org/W2008854521","https://openalex.org/W2029358771","https://openalex.org/W2034878282","https://openalex.org/W2040851961","https://openalex.org/W2050246990","https://openalex.org/W2058161128","https://openalex.org/W2060762518","https://openalex.org/W2064675550","https://openalex.org/W2101115134","https://openalex.org/W2103252230","https://openalex.org/W2107878631","https://openalex.org/W2110320181","https://openalex.org/W2113325037","https://openalex.org/W2125890869","https://openalex.org/W2148080316","https://openalex.org/W2148317675","https://openalex.org/W2155273149","https://openalex.org/W2155502962","https://openalex.org/W2156809737","https://openalex.org/W2156915253","https://openalex.org/W2163605009","https://openalex.org/W2167311298","https://openalex.org/W2270470215","https://openalex.org/W2286190554","https://openalex.org/W2357843841","https://openalex.org/W2467677331","https://openalex.org/W2594820217","https://openalex.org/W2782697586","https://openalex.org/W2919115771","https://openalex.org/W6679436768"],"related_works":["https://openalex.org/W3090300519","https://openalex.org/W2514492205","https://openalex.org/W4250401876","https://openalex.org/W2943851981","https://openalex.org/W2566526749","https://openalex.org/W2907667791","https://openalex.org/W3047461507","https://openalex.org/W3126390843","https://openalex.org/W4245880644","https://openalex.org/W4312415459"],"abstract_inverted_index":{"Early":[0],"detection":[1],"of":[2,10,37,43,70,102,110,127,163,165,203,232],"high":[3],"fall":[4,11,44,56,71,95,179,227],"risk":[5,180,228],"is":[6],"an":[7,99],"essential":[8],"component":[9],"prevention":[12],"in":[13,154,161,207,221],"older":[14,104],"adults.":[15,105],"Wearable":[16],"sensors":[17,47],"can":[18,51],"provide":[19,52],"valuable":[20,53],"insight":[21,54],"into":[22,55],"daily-life":[23],"activities;":[24],"biomechanical":[25,59,140],"features":[26,60,88,141],"extracted":[27],"from":[28,62,80,89],"such":[29,48],"inertial":[30],"data":[31,64,92,205],"have":[32],"been":[33],"shown":[34],"to":[35,85,131,135],"be":[36],"added":[38],"value":[39],"for":[40,67],"the":[41,68,108,143,150,166,175,204,208,230],"assessment":[42,69],"risk.":[45,57,72,96],"Body-worn":[46],"as":[49,190],"accelerometers":[50],"Currently,":[58],"derived":[61],"accelerometer":[63,91],"are":[65,83,159],"used":[66,98],"Here,":[73],"we":[74],"studied":[75],"whether":[76],"deep":[77,112,151,193,217],"learning":[78,82,113,152,157,194,218],"methods":[79],"machine":[81],"suited":[84],"automatically":[86],"derive":[87],"raw":[90],"that":[93,149,169,201,216],"assess":[94,226],"We":[97,106,198,214],"existing":[100],"dataset":[101],"296":[103],"compared":[107],"performance":[109,210],"three":[111],"model":[114,138],"architectures":[115],"(convolutional":[116],"neural":[117],"network":[118],"(CNN),":[119],"long":[120],"short-term":[121],"memory":[122],"(LSTM)":[123],"and":[124,134,188,220],"a":[125,136,155],"combination":[126],"these":[128,170],"two":[129],"(ConvLSTM))":[130],"each":[132],"other":[133],"baseline":[137,176],"with":[139,186],"on":[142,178,229],"same":[144],"dataset.":[145],"The":[146],"results":[147],"show":[148],"models":[153,171,195],"single-task":[156],"mode":[158],"strong":[160],"recognition":[162],"identity":[164],"subject,":[167],"but":[168],"only":[172],"slightly":[173],"outperform":[174],"method":[177],"assessment.":[181],"When":[182],"using":[183],"multi-task":[184,223],"learning,":[185,224],"gender":[187],"age":[189],"auxiliary":[191],"tasks,":[192],"perform":[196],"better.":[197],"also":[199],"found":[200],"preprocessing":[202],"resulted":[206],"best":[209],"(AUC":[211],"=":[212],"0.75).":[213],"conclude":[215],"models,":[219],"particular":[222],"effectively":[225],"basis":[231],"wearable":[233],"sensor":[234],"data.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":32},{"year":2022,"cited_by_count":40},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":3}],"updated_date":"2026-06-05T09:01:59.212387","created_date":"2018-06-01T00:00:00"}