{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/79579"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/79579","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Investigation of the effects of body type on accelerometer based fall detection","abstract":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Precision or personalized medicine follows the idea that analyzing a patient's genome can help stratify patients into treatment groups [1] [2]. Patients with cancer, for example, are split into groups based on the genetic make-up, rather than the location, of their tumor and given medicine that corresponds to their specified treatment group [3]. Instead of prescribing the same medicine for all patients with lung cancer, doctors are able to personalize treatment based on a person's genetics. Could fall detection algorithms benefit from this type of personalized approach? This project analyzes accelerometer data containing falls and activities of daily living gathered from 16 stunt actors and 20 elderly volunteers as well as 22 falls from the FARSEEING dataset with the goal of discovering whether algorithms personalized to a person's physiological features (such as height or weight) perform better than the traditional generalized approach. This thesis demonstrates that personalized algorithms can potentially make fall detection more efficient for some people. The unscripted test results show that the algorithms personalized to build and weight, decreased false alarms in several participants as compared to the overall algorithm. The FARSEEING data results show that algorithms, personalized to weight and sex, may increase the efficiency and accuracy of fall detection algorithms. An analysis of which physiological factors help determine whether a person needs a personalized or a general algorithm is needed to determine whether or not personalized algorithms make economic sense.","abstract_html":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Precision or personalized medicine follows the idea that analyzing a patient's genome can help stratify patients into treatment groups [1] [2]. Patients with cancer, for example, are split into groups based on the genetic make-up, rather than the location, of their tumor and given medicine that corresponds to their specified treatment group [3]. Instead of prescribing the same medicine for all patients with lung cancer, doctors are able to personalize treatment based on a person's genetics. Could fall detection algorithms benefit from this type of personalized approach? This project analyzes accelerometer data containing falls and activities of daily living gathered from 16 stunt actors and 20 elderly volunteers as well as 22 falls from the FARSEEING dataset with the goal of discovering whether algorithms personalized to a person's physiological features (such as height or weight) perform better than the traditional generalized approach. This thesis demonstrates that personalized algorithms can potentially make fall detection more efficient for some people. The unscripted test results show that the algorithms personalized to build and weight, decreased false alarms in several participants as compared to the overall algorithm. The FARSEEING data results show that algorithms, personalized to weight and sex, may increase the efficiency and accuracy of fall detection algorithms. An analysis of which physiological factors help determine whether a person needs a personalized or a general algorithm is needed to determine whether or not personalized algorithms make economic sense.","abstract_has_math":false,"creators":["Kurkowski, Samantha"],"institution":"University of Missouri--Columbia","degree_name":"M.S.","degree_level":"Masters","degree_discipline":"Electrical and computer engineering (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Skubic, Marjorie"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-24T03:08:32Z","subjects":[],"languages":["eng","English"],"rights":["Access to files is limited to the University of Missouri--Columbia with SSO login."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/79579"],"render_values":[{"text":"https://doi.org/10.32469/10355/79579","href":"https://doi.org/10.32469/10355/79579","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/79579","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Skubic, Marjorie"]},{"key":"dc:creator","label":"Author","values":["Kurkowski, Samantha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-12-24T20:17:42Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-12-24T20:17:42Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"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":["Electrical and 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":"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.doi","label":"DOI","values":["https://doi.org/10.32469/10355/79579"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/79579"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Precision or personalized medicine follows the idea that analyzing a patient's genome can help stratify patients into treatment groups [1] [2]. Patients with cancer, for example, are split into groups based on the genetic make-up, rather than the location, of their tumor and given medicine that corresponds to their specified treatment group [3]. Instead of prescribing the same medicine for all patients with lung cancer, doctors are able to personalize treatment based on a person's genetics. Could fall detection algorithms benefit from this type of personalized approach? This project analyzes accelerometer data containing falls and activities of daily living gathered from 16 stunt actors and 20 elderly volunteers as well as 22 falls from the FARSEEING dataset with the goal of discovering whether algorithms personalized to a person's physiological features (such as height or weight) perform better than the traditional generalized approach. This thesis demonstrates that personalized algorithms can potentially make fall detection more efficient for some people. The unscripted test results show that the algorithms personalized to build and weight, decreased false alarms in several participants as compared to the overall algorithm. The FARSEEING data results show that algorithms, personalized to weight and sex, may increase the efficiency and accuracy of fall detection algorithms. An analysis of which physiological factors help determine whether a person needs a personalized or a general algorithm is needed to determine whether or not personalized algorithms make economic sense."]},{"key":"dc:title","label":"Title","values":["Investigation of the effects of body type on accelerometer based fall detection"]}]}],"canonical_facts":{"dc:contributor.advisor":["Skubic, Marjorie"],"dc:creator":["Kurkowski, Samantha"],"dc:date.accessioned":["2020-12-24T20:17:42Z"],"dc:date.available":["2020-12-24T20:17:42Z"],"dc:date.issued":["2019"],"dc:description.abstract":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Precision or personalized medicine follows the idea that analyzing a patient's genome can help stratify patients into treatment groups [1] [2]. 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The unscripted test results show that the algorithms personalized to build and weight, decreased false alarms in several participants as compared to the overall algorithm. The FARSEEING data results show that algorithms, personalized to weight and sex, may increase the efficiency and accuracy of fall detection algorithms. An analysis of which physiological factors help determine whether a person needs a personalized or a general algorithm is needed to determine whether or not personalized algorithms make economic sense."],"dc:identifier.doi":["https://doi.org/10.32469/10355/79579"],"dc:identifier.uri":["https://hdl.handle.net/10355/79579"],"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:title":["Investigation of the effects of body type on accelerometer based fall detection"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and computer engineering (MU)"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Missouri--Columbia"]},"updated_at":"2026-07-24T03:08:32Z"}