How can AI reduce fall injuries in the workplace? Article Swipe

PDF

Nicholas Cartocci , Antonios E. Gkikakis , Roberto F. Pitzalis , Fabio Pera , Maria Teresa Settino , Darwin G. Caldwell , Jesús Ortiz ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.24507

Fall-caused injuries are common in all types of work environments, including offices. They are the main cause of absences longer than three days, especially for small and medium-sized businesses (SMEs). However, data, data amount, data heterogeneity, and stringent processing time constraints continue to pose challenges to real-time fall detection. This work proposes a new approach based on a recurrent neural network (RNN) for Fall Detection and a Kolmogorov-Arnold Network (KAN) to estimate the time of impact of the fall. The approach is tested on SisFall, a dataset consisting of 2706 Activities of Daily Living (ADLs) and 1798 falls recorded by three sensors. The results show that the proposed approach achieves an average TPR of 82.6% and TNR of 98.4% for fall sequences and 94.4% in ADL. Besides, the Root Mean Squared Error of the estimated time of impact is approximately 160ms.

Related Topics

The Spirit Catches You And You Fall Down

Fall (2022 Film)

Legends Of The Fall

The Fall Guy (2024 Film)

Anatomy Of A Fall

Fall Of The Western Roman Empire

Fall Out Boy

The Fall Of The House Of Usher (Miniseries)

Tacko Fall

Fall Of Saigon

The Harder They Fall (2021 Film)

The Fall Of The House Of Usher

Concepts

No concepts available.

Metadata

Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2505.24507
PDF: https://arxiv.org/pdf/2505.24507
OA Status: green
OpenAlex ID: https://openalex.org/W4414857712

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4414857712

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.48550/arxiv.2505.24507

Digital Object Identifier
Title: How can AI reduce fall injuries in the workplace?

Work title
Type: preprint

OpenAlex work type
Language: en

Primary language
Publication year: 2025

Year of publication
Publication date: 2025-05-30

Full publication date if available
Authors: Nicholas Cartocci, Antonios E. Gkikakis, Roberto F. Pitzalis, Fabio Pera, Maria Teresa Settino, Darwin G. Caldwell, Jesús Ortiz

List of authors in order
Landing page: https://arxiv.org/abs/2505.24507

Publisher landing page
PDF URL: https://arxiv.org/pdf/2505.24507

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://arxiv.org/pdf/2505.24507

Direct OA link when available
Cited by: 0

Total citation count in OpenAlex

Full payload

id	https://openalex.org/W4414857712
doi	https://doi.org/10.48550/arxiv.2505.24507
ids.doi	https://doi.org/10.48550/arxiv.2505.24507
ids.openalex	https://openalex.org/W4414857712
fwci
type	preprint
title	How can AI reduce fall injuries in the workplace?
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
topics[0].id	https://openalex.org/T10809
topics[0].field.id	https://openalex.org/fields/36
topics[0].field.display_name	Health Professions
topics[0].score	0.986299991607666
topics[0].domain.id	https://openalex.org/domains/4
topics[0].domain.display_name	Health Sciences
topics[0].subfield.id	https://openalex.org/subfields/3614
topics[0].subfield.display_name	Radiological and Ultrasound Technology
topics[0].display_name	Occupational Health and Safety Research
is_xpac	False
apc_list
apc_paid
language	en
locations[0].id	pmh:oai:arXiv.org:2505.24507
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400194
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	arXiv (Cornell University)
locations[0].source.host_organization	https://openalex.org/I205783295
locations[0].source.host_organization_name	Cornell University
locations[0].source.host_organization_lineage	https://openalex.org/I205783295
locations[0].license
locations[0].pdf_url	https://arxiv.org/pdf/2505.24507
locations[0].version	submittedVersion
locations[0].raw_type	text
locations[0].license_id
locations[0].is_accepted	False
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	http://arxiv.org/abs/2505.24507
locations[1].id	doi:10.48550/arxiv.2505.24507
locations[1].is_oa	True
locations[1].source.id	https://openalex.org/S4306400194
locations[1].source.issn
locations[1].source.type	repository
locations[1].source.is_oa	True
locations[1].source.issn_l
locations[1].source.is_core	False
locations[1].source.is_in_doaj	False
locations[1].source.display_name	arXiv (Cornell University)
locations[1].source.host_organization	https://openalex.org/I205783295
locations[1].source.host_organization_name	Cornell University
locations[1].source.host_organization_lineage	https://openalex.org/I205783295
locations[1].license
locations[1].pdf_url
locations[1].version
locations[1].raw_type	article
locations[1].license_id
locations[1].is_accepted	False
locations[1].is_published
locations[1].raw_source_name
locations[1].landing_page_url	https://doi.org/10.48550/arxiv.2505.24507
indexed_in	arxiv, datacite
authorships[0].author.id	https://openalex.org/A5002373483
authorships[0].author.orcid	https://orcid.org/0000-0002-4908-9769
authorships[0].author.display_name	Nicholas Cartocci
authorships[0].author_position	first
authorships[0].raw_author_name	Cartocci, Nicholas
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5088094084
authorships[1].author.orcid	https://orcid.org/0000-0002-5339-0431
authorships[1].author.display_name	Antonios E. Gkikakis
authorships[1].author_position	middle
authorships[1].raw_author_name	Gkikakis, Antonios E.
authorships[1].is_corresponding	False
authorships[2].author.id	https://openalex.org/A5083559753
authorships[2].author.orcid	https://orcid.org/0000-0003-1464-6219
authorships[2].author.display_name	Roberto F. Pitzalis
authorships[2].author_position	middle
authorships[2].raw_author_name	Pitzalis, Roberto F.
authorships[2].is_corresponding	False
authorships[3].author.id	https://openalex.org/A5046455612
authorships[3].author.orcid
authorships[3].author.display_name	Fabio Pera
authorships[3].author_position	middle
authorships[3].raw_author_name	Pera, Fabio
authorships[3].is_corresponding	False
authorships[4].author.id	https://openalex.org/A5050488117
authorships[4].author.orcid
authorships[4].author.display_name	Maria Teresa Settino
authorships[4].author_position	middle
authorships[4].raw_author_name	Settino, Maria Teresa
authorships[4].is_corresponding	False
authorships[5].author.id	https://openalex.org/A5044243940
authorships[5].author.orcid	https://orcid.org/0000-0002-6233-9961
authorships[5].author.display_name	Darwin G. Caldwell
authorships[5].author_position	middle
authorships[5].raw_author_name	Caldwell, Darwin G.
authorships[5].is_corresponding	False
authorships[6].author.id	https://openalex.org/A5025925261
authorships[6].author.orcid	https://orcid.org/0000-0001-9475-1945
authorships[6].author.display_name	Jesús Ortiz
authorships[6].author_position	last
authorships[6].raw_author_name	Ortiz, Jesús
authorships[6].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://arxiv.org/pdf/2505.24507
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	How can AI reduce fall injuries in the workplace?
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T06:51:31.235846
primary_topic.id	https://openalex.org/T10809
primary_topic.field.id	https://openalex.org/fields/36
primary_topic.field.display_name	Health Professions
primary_topic.score	0.986299991607666
primary_topic.domain.id	https://openalex.org/domains/4
primary_topic.domain.display_name	Health Sciences
primary_topic.subfield.id	https://openalex.org/subfields/3614
primary_topic.subfield.display_name	Radiological and Ultrasound Technology
primary_topic.display_name	Occupational Health and Safety Research
cited_by_count	0
locations_count	2
best_oa_location.id	pmh:oai:arXiv.org:2505.24507
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400194
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	arXiv (Cornell University)
best_oa_location.source.host_organization	https://openalex.org/I205783295
best_oa_location.source.host_organization_name	Cornell University
best_oa_location.source.host_organization_lineage	https://openalex.org/I205783295
best_oa_location.license
best_oa_location.pdf_url	https://arxiv.org/pdf/2505.24507
best_oa_location.version	submittedVersion
best_oa_location.raw_type	text
best_oa_location.license_id
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	http://arxiv.org/abs/2505.24507
primary_location.id	pmh:oai:arXiv.org:2505.24507
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400194
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	arXiv (Cornell University)
primary_location.source.host_organization	https://openalex.org/I205783295
primary_location.source.host_organization_name	Cornell University
primary_location.source.host_organization_lineage	https://openalex.org/I205783295
primary_location.license
primary_location.pdf_url	https://arxiv.org/pdf/2505.24507
primary_location.version	submittedVersion
primary_location.raw_type	text
primary_location.license_id
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	http://arxiv.org/abs/2505.24507
publication_date	2025-05-30
publication_year	2025
referenced_works_count	0
abstract_inverted_index.a	52, 57, 66, 85
abstract_inverted_index.an	110
abstract_inverted_index.by	99
abstract_inverted_index.in	4, 124
abstract_inverted_index.is	81, 138
abstract_inverted_index.of	7, 17, 74, 76, 88, 91, 113, 117, 132, 136
abstract_inverted_index.on	56, 83
abstract_inverted_index.to	42, 45, 70
abstract_inverted_index.TNR	116
abstract_inverted_index.TPR	112
abstract_inverted_index.The	79, 102
abstract_inverted_index.all	5
abstract_inverted_index.and	26, 36, 65, 95, 115, 122
abstract_inverted_index.are	2, 13
abstract_inverted_index.for	24, 62, 119
abstract_inverted_index.new	53
abstract_inverted_index.the	14, 72, 77, 106, 127, 133
abstract_inverted_index.1798	96
abstract_inverted_index.2706	89
abstract_inverted_index.ADL.	125
abstract_inverted_index.Fall	63
abstract_inverted_index.Mean	129
abstract_inverted_index.Root	128
abstract_inverted_index.They	12
abstract_inverted_index.This	49
abstract_inverted_index.data	32, 34
abstract_inverted_index.fall	47, 120
abstract_inverted_index.main	15
abstract_inverted_index.pose	43
abstract_inverted_index.show	104
abstract_inverted_index.than	20
abstract_inverted_index.that	105
abstract_inverted_index.time	39, 73, 135
abstract_inverted_index.work	8, 50
abstract_inverted_index.(KAN)	69
abstract_inverted_index.(RNN)	61
abstract_inverted_index.82.6%	114
abstract_inverted_index.94.4%	123
abstract_inverted_index.98.4%	118
abstract_inverted_index.Daily	92
abstract_inverted_index.Error	131
abstract_inverted_index.based	55
abstract_inverted_index.cause	16
abstract_inverted_index.data,	31
abstract_inverted_index.days,	22
abstract_inverted_index.fall.	78
abstract_inverted_index.falls	97
abstract_inverted_index.small	25
abstract_inverted_index.three	21, 100
abstract_inverted_index.types	6
abstract_inverted_index.(ADLs)	94
abstract_inverted_index.160ms.	140
abstract_inverted_index.Living	93
abstract_inverted_index.common	3
abstract_inverted_index.impact	75, 137
abstract_inverted_index.longer	19
abstract_inverted_index.neural	59
abstract_inverted_index.tested	82
abstract_inverted_index.(SMEs).	29
abstract_inverted_index.Network	68
abstract_inverted_index.Squared	130
abstract_inverted_index.amount,	33
abstract_inverted_index.average	111
abstract_inverted_index.dataset	86
abstract_inverted_index.network	60
abstract_inverted_index.results	103
abstract_inverted_index.Besides,	126
abstract_inverted_index.However,	30
abstract_inverted_index.SisFall,	84
abstract_inverted_index.absences	18
abstract_inverted_index.achieves	109
abstract_inverted_index.approach	54, 80, 108
abstract_inverted_index.continue	41
abstract_inverted_index.estimate	71
abstract_inverted_index.injuries	1
abstract_inverted_index.offices.	11
abstract_inverted_index.proposed	107
abstract_inverted_index.proposes	51
abstract_inverted_index.recorded	98
abstract_inverted_index.sensors.	101
abstract_inverted_index.Detection	64
abstract_inverted_index.estimated	134
abstract_inverted_index.including	10
abstract_inverted_index.real-time	46
abstract_inverted_index.recurrent	58
abstract_inverted_index.sequences	121
abstract_inverted_index.stringent	37
abstract_inverted_index.Activities	90
abstract_inverted_index.businesses	28
abstract_inverted_index.challenges	44
abstract_inverted_index.consisting	87
abstract_inverted_index.detection.	48
abstract_inverted_index.especially	23
abstract_inverted_index.processing	38
abstract_inverted_index.Fall-caused	0
abstract_inverted_index.constraints	40
abstract_inverted_index.medium-sized	27
abstract_inverted_index.approximately	139
abstract_inverted_index.environments,	9
abstract_inverted_index.heterogeneity,	35
abstract_inverted_index.Kolmogorov-Arnold	67
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	7
citation_normalized_percentile