Differentiative Feature-based Fall Detection System

Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder but also the caregiver. Although there are numbers of applications and devices in the market for the user, the cutting-edge technology as a m...

Full description

Saved in:
Bibliographic Details
Published in:International Conference on Control, Automation and Systems (Online) pp. 359 - 364
Main Authors: Chotipant, Supannada, Jamsri, Pornsuree
Format: Conference Proceeding
Language:English
Published: ICROS 12.10.2021
Subjects:
Accelerometers
Differentiative feature-based approach
Fall detection
Feature extraction
Machine learning algorithms
Machine learning-based algorithm
Performance evaluation
Process control
Support vector machines
Training
ISSN:2642-3901
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder but also the caregiver. Although there are numbers of applications and devices in the market for the user, the cutting-edge technology as a machine learning-based algorithm can increase effectiveness of fall detection model into device's effectiveness. The available technology is embedded accelerometer and gyroscope sensor into a smartphone provide benefit dataset. These data can be used for reducing and managing serious injury and caregiver can assist on time. The leverage performance of a Smart Steps application by including the essence of machine learning algorithm and 5-fold cross validation rises accuracy in fall detection. Thus, this paper proposed a novel method of 4 binary classification--Decision Tree, SVM, K-Nearest Neighbors, and Gradient Boosting. The focusing on acceleration magnitude, angular velocity magnitude, and difference between pre-current, current-post values are taken into account in the study. The opened dataset, MobiFall, are split into 2 groups 1) train group 80% and 2) test group 20% for gathering effectiveness result. The model's assessment measures in 4-dimension 1) accuracy, 2) precision, 3) recall and, 4) F1-Score. The results demonstrates increasing values that 95.65% of accuracy, 91.20% precision, 90.86% recall and, 91.03% F1-Score. The fall detection of the study can conclude that the machine learning-based algorithm offers more accuracy and effectively than threshold-based algorithm.
AbstractList Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder but also the caregiver. Although there are numbers of applications and devices in the market for the user, the cutting-edge technology as a machine learning-based algorithm can increase effectiveness of fall detection model into device's effectiveness. The available technology is embedded accelerometer and gyroscope sensor into a smartphone provide benefit dataset. These data can be used for reducing and managing serious injury and caregiver can assist on time. The leverage performance of a Smart Steps application by including the essence of machine learning algorithm and 5-fold cross validation rises accuracy in fall detection. Thus, this paper proposed a novel method of 4 binary classification--Decision Tree, SVM, K-Nearest Neighbors, and Gradient Boosting. The focusing on acceleration magnitude, angular velocity magnitude, and difference between pre-current, current-post values are taken into account in the study. The opened dataset, MobiFall, are split into 2 groups 1) train group 80% and 2) test group 20% for gathering effectiveness result. The model's assessment measures in 4-dimension 1) accuracy, 2) precision, 3) recall and, 4) F1-Score. The results demonstrates increasing values that 95.65% of accuracy, 91.20% precision, 90.86% recall and, 91.03% F1-Score. The fall detection of the study can conclude that the machine learning-based algorithm offers more accuracy and effectively than threshold-based algorithm.
Author Chotipant, Supannada
Jamsri, Pornsuree
Author_xml – sequence: 1
  givenname: Supannada
  surname: Chotipant
  fullname: Chotipant, Supannada
  email: supannada@it.kmitl.ac.th
  organization: King Mongkut's Institute of Technology Ladkrabang,Faculty of Information Technology,Bangkok,Thailand,10520
– sequence: 2
  givenname: Pornsuree
  surname: Jamsri
  fullname: Jamsri, Pornsuree
  email: pornsuree@it.kmitl.ac.th
  organization: King Mongkut's Institute of Technology Ladkrabang,Faculty of Information Technology,Bangkok,Thailand,10520
BookMark eNotj8tKAzEUQKMo2NZ-gZv5gRlvbt7LMrW1UHDR7ksmuYHIdCozUejfK9jV2RwOnDl7GC4DMVZxaFA47l53bbs6KDRSNQjIG6cVgLZ3bG6dE8gVcnfPZqgl1sIBf2LLafoEAIEg_8QZE-ucEo00lOxL_qFqQ758j1R3fqJYbXzfV2sqFEq-DNXhOhU6P7PH5PuJljcu2HHzdmzf6_3Hdteu9nWWFusYQUuVODdkO-kBpAw6BudTkoYADQ8mKu5j8gqs4YgxaZc68AGD1SQW7OU_m4no9DXmsx-vp9ui-AWbT0ff
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.23919/ICCAS52745.2021.9650068
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 8993215219
9788993215212
EISSN 2642-3901
EndPage 364
ExternalDocumentID 9650068
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i482-dd0645f117e8b4a0044c6dc9aff47e0271c7d51adfa5087122df69fb0ac2c86e3
IEDL.DBID RIE
IngestDate Wed Aug 27 04:58:30 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i482-dd0645f117e8b4a0044c6dc9aff47e0271c7d51adfa5087122df69fb0ac2c86e3
PageCount 6
ParticipantIDs ieee_primary_9650068
PublicationCentury 2000
PublicationDate 2021-Oct.-12
PublicationDateYYYYMMDD 2021-10-12
PublicationDate_xml – month: 10
  year: 2021
  text: 2021-Oct.-12
  day: 12
PublicationDecade 2020
PublicationTitle International Conference on Control, Automation and Systems (Online)
PublicationTitleAbbrev ICCAS
PublicationYear 2021
Publisher ICROS
Publisher_xml – name: ICROS
SSID ssj0003204068
Score 1.7728287
Snippet Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder...
SourceID ieee
SourceType Publisher
StartPage 359
SubjectTerms Accelerometers
Differentiative feature-based approach
Fall detection
Feature extraction
Machine learning algorithms
Machine learning-based algorithm
Performance evaluation
Process control
Support vector machines
Training
Title Differentiative Feature-based Fall Detection System
URI https://ieeexplore.ieee.org/document/9650068
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB7a4kEvPlrxzR48mrbJ7uZxlNaiIKVgD72VNJlAoWyl7vr7TbJLVfDiLSQEkszAfDOZ-QbgXlnjnFo5knOlidcQTqRlSLTwK94muSGPhcKvYjqVi4WateBhXwuDiDH5DPthGP_y7dZUIVQ2UB5ODLlsQ1sIXtdq7eMpKfPqyGWdrMNSRdXgZTR6fMu925V7P5DRfrP9Vx-VaEYmx_87wAn0vuvxktne0pxCC4szOPpBJdiFdNx0OinXkco7Cdiu2iEJZsomE73ZJGMsY-JVkdQ85T2YT57mo2fSNEQg68wDYWsDuZyjVKBcZTr8xRpujdLOZQK9f0mNsDnV1mkPuwRlzDqu3GqoDTOSY3oOnWJb4AUkGUfrRUSVzVWWGqnRI480tKyRyqGSl9ANt1--15QXy-biV39PX8NheGASEz1uoFPuKryFA_NZrj92d1FOX7c3k3g
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5qFdSLjyq-3YNH026yu9nkKK2lxVoK9tBbSZMJFMpW6tbfb5ItVcGLt5BASDID881k5huAB2m0tXJmScalIk5DOBGGIVG5W3E2ycY8FAoP8uFQTCZyVIPHbS0MIobkM2z6YfjLN0u99qGylnRwIuZiB3azNGVxVa21jagkzCkkF1W6Dkskla1-u_30ljnHK3OeIKPNzQa_OqkEQ9I9-t8RjuHsuyIvGm1tzQnUsDiFwx9kgg1IOpteJ-U8kHlHHt2tV0i8oTJRVy0WUQfLkHpVRBVT-RmMu8_jdo9sWiKQeeqgsDGeXs5SmqOYpcr_xmputFTWpjk6D5Pq3GRUGasc8MopY8ZyaWex0kwLjsk51ItlgRcQpRyNExKVJpNpooVChz0S37RGSItSXELD3376XpFeTDcXv_p7-h72e-PXwXTQH75cw4F_bBLSPm6gXq7WeAt7-rOcf6zugsy-AJbHlr8
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=International+Conference+on+Control%2C+Automation+and+Systems+%28Online%29&rft.atitle=Differentiative+Feature-based+Fall+Detection+System&rft.au=Chotipant%2C+Supannada&rft.au=Jamsri%2C+Pornsuree&rft.date=2021-10-12&rft.pub=ICROS&rft.eissn=2642-3901&rft.spage=359&rft.epage=364&rft_id=info:doi/10.23919%2FICCAS52745.2021.9650068&rft.externalDocID=9650068