Dimitri Vargemidis, K. Gerling, L. Geurts, V. Abeele
{"title":"Flexible Activity Tracking for Older Adults Using Mobility Aids — An Exploratory Study on Automatically Identifying Movement Modality","authors":"Dimitri Vargemidis, K. Gerling, L. Geurts, V. Abeele","doi":"10.1145/3517428.3550371","DOIUrl":null,"url":null,"abstract":"Wearable activity trackers are inaccessible to older adults who use mobility aids (e.g., walker, wheelchair), because the accuracy of trackers drops considerably for such movement modalities (MMs). As an initial step to address this problem, we implemented and tested a minimum distance classifier to automatically identify the used MM out of seven modalities, including movement with or without a mobility aid, and no movement. Depending on the test setup, our classifier achieves accuracies between 82 % and 100 %. These findings can be leveraged in future work to combine the classifier with algorithms tailored to each mobility aid to make activity trackers accessible to users with limited mobility.","PeriodicalId":384752,"journal":{"name":"Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3517428.3550371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Wearable activity trackers are inaccessible to older adults who use mobility aids (e.g., walker, wheelchair), because the accuracy of trackers drops considerably for such movement modalities (MMs). As an initial step to address this problem, we implemented and tested a minimum distance classifier to automatically identify the used MM out of seven modalities, including movement with or without a mobility aid, and no movement. Depending on the test setup, our classifier achieves accuracies between 82 % and 100 %. These findings can be leveraged in future work to combine the classifier with algorithms tailored to each mobility aid to make activity trackers accessible to users with limited mobility.