{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/15984"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/15984","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Fall detection system using low cost computing and online communication","abstract":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Falls are prevalent among elderly and sometime may result in fatal injuries, which impacts their ability of independent living. Thus, a reliable as well as cost effective fall detection system is required. We have developed an inexpensive fall detection system, which detects falls and automatically sends notification to the caregiver. It uses low cost, unobtrusive wall mounted Sheevaplug computer and an Internet based free messaging service. The fall detection solution is based on the carpet foil sensors' information received from the hardware support system, and uses a connected component algorithm to discover the simultaneous activation of group of contiguous sensors during a fall. In addition to the fall detection, the floor sensor data was stored on the Sheevaplug server and then communicated to the remote computer using Internet socket connection. An executable application was developed using Eclipse environment for providing a visual representation of this data on the remote computer. A web application was further developed by reusing the same source code. The falls algorithm was tested for 11 different fall scenarios with 4 volunteers. Experimental results show that the system is highly reliable and accurate with the average sensitivity as 92.72 % and the average specificity as 95.9%, for a single person. Further research on improving the floor sensor data will also improve the results of fall detection system.","abstract_html":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Falls are prevalent among elderly and sometime may result in fatal injuries, which impacts their ability of independent living. Thus, a reliable as well as cost effective fall detection system is required. We have developed an inexpensive fall detection system, which detects falls and automatically sends notification to the caregiver. It uses low cost, unobtrusive wall mounted Sheevaplug computer and an Internet based free messaging service. The fall detection solution is based on the carpet foil sensors' information received from the hardware support system, and uses a connected component algorithm to discover the simultaneous activation of group of contiguous sensors during a fall. In addition to the fall detection, the floor sensor data was stored on the Sheevaplug server and then communicated to the remote computer using Internet socket connection. An executable application was developed using Eclipse environment for providing a visual representation of this data on the remote computer. A web application was further developed by reusing the same source code. The falls algorithm was tested for 11 different fall scenarios with 4 volunteers. Experimental results show that the system is highly reliable and accurate with the average sensitivity as 92.72 % and the average specificity as 95.9%, for a single person. Further research on improving the floor sensor data will also improve the results of fall detection system.","abstract_has_math":false,"creators":["Gadre, Kaustubh Raghunath"],"institution":"University of Missouri--Columbia","degree_name":"M.S.","degree_level":"Masters","degree_discipline":"Computer engineering (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Tyrer, Harry W."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-24T03:07:35Z","subjects":["fall detection","elder care","Sheevaplug","floor sensor"],"languages":["eng","English"],"rights":["Access to files is limited to the University of Missouri--Columbia with SSO login."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10355/15984","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Tyrer, Harry W."]},{"key":"dc:creator","label":"Author","values":["Gadre, Kaustubh Raghunath"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-11-05T20:38:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-11-05T20:38:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer engineering (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Columbia"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["fall detection","elder care","Sheevaplug","floor sensor"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Access to files is limited to the University of Missouri--Columbia with SSO login."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10355/15984"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page (University of Missouri--Columbia, viewed on November 5, 2012).","The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.","Thesis advisor: Dr. Harry W. Tyrer","Includes bibliographical references.","M.S. University of Missouri--Columbia 2012.","\"July 2012\""]},{"key":"dc:description.abstract","label":"Abstract","values":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Falls are prevalent among elderly and sometime may result in fatal injuries, which impacts their ability of independent living. Thus, a reliable as well as cost effective fall detection system is required. We have developed an inexpensive fall detection system, which detects falls and automatically sends notification to the caregiver. It uses low cost, unobtrusive wall mounted Sheevaplug computer and an Internet based free messaging service. The fall detection solution is based on the carpet foil sensors' information received from the hardware support system, and uses a connected component algorithm to discover the simultaneous activation of group of contiguous sensors during a fall. In addition to the fall detection, the floor sensor data was stored on the Sheevaplug server and then communicated to the remote computer using Internet socket connection. An executable application was developed using Eclipse environment for providing a visual representation of this data on the remote computer. A web application was further developed by reusing the same source code. The falls algorithm was tested for 11 different fall scenarios with 4 volunteers. Experimental results show that the system is highly reliable and accurate with the average sensitivity as 92.72 % and the average specificity as 95.9%, for a single person. Further research on improving the floor sensor data will also improve the results of fall detection system."]},{"key":"dc:title","label":"Title","values":["Fall detection system using low cost computing and online communication"]}]}],"canonical_facts":{"dc:contributor.advisor":["Tyrer, Harry W."],"dc:creator":["Gadre, Kaustubh Raghunath"],"dc:date.accessioned":["2012-11-05T20:38:15Z"],"dc:date.available":["2012-11-05T20:38:15Z"],"dc:date.issued":["2012"],"dc:description":["Title from PDF of title page (University of Missouri--Columbia, viewed on November 5, 2012).","The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.","Thesis advisor: Dr. Harry W. Tyrer","Includes bibliographical references.","M.S. University of Missouri--Columbia 2012.","\"July 2012\""],"dc:description.abstract":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Falls are prevalent among elderly and sometime may result in fatal injuries, which impacts their ability of independent living. Thus, a reliable as well as cost effective fall detection system is required. We have developed an inexpensive fall detection system, which detects falls and automatically sends notification to the caregiver. It uses low cost, unobtrusive wall mounted Sheevaplug computer and an Internet based free messaging service. The fall detection solution is based on the carpet foil sensors' information received from the hardware support system, and uses a connected component algorithm to discover the simultaneous activation of group of contiguous sensors during a fall. In addition to the fall detection, the floor sensor data was stored on the Sheevaplug server and then communicated to the remote computer using Internet socket connection. An executable application was developed using Eclipse environment for providing a visual representation of this data on the remote computer. A web application was further developed by reusing the same source code. The falls algorithm was tested for 11 different fall scenarios with 4 volunteers. Experimental results show that the system is highly reliable and accurate with the average sensitivity as 92.72 % and the average specificity as 95.9%, for a single person. Further research on improving the floor sensor data will also improve the results of fall detection system."],"dc:identifier.uri":["http://hdl.handle.net/10355/15984"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["University of Missouri--Columbia"],"dc:rights":["Access to files is limited to the University of Missouri--Columbia with SSO login."],"dc:subject":["fall detection","elder care","Sheevaplug","floor sensor"],"dc:title":["Fall detection system using low cost computing and online communication"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer engineering (MU)"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Missouri--Columbia"]},"updated_at":"2026-07-24T03:07:35Z"}