{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/110763"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/110763","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Improving the Accessibility of Smartwatches as Research Tools by Developing a Software Library","abstract":"Over the past 10 years, smartwatches have become increasingly popular for commercial use. Their ever-increasing capabilities, accuracy, and sophistication of smartwatches is making them increasingly appealing to physical activity researchers as a valuable research tool. The non-invasive nature, prevalence, and versatility of smartwatches is being utilized to track heart rate, blood-oxygen levels, activity and movement, and sleep. However, the current state of the art lacks a uniform method to extract, organize, and analyze data collected from these devices. The objective of this research was to develop a Python software library that is widely available, highly capable, and easy to use with the data collected by the Apple Watch. The library was designed to offer data science, visualization, and mining features that help physical activity research find and communicate patterns in the Apple Health data. The custom-built caching system of the library provides near-instant runtime to parse and analyze large files without trading off on memory usage. The Wanjara Smartwatch Library has significantly better performance, proven reliability and robustness, and improved usability than the alternatives discovered in the review of the literature.","abstract_html":"Over the past 10 years, smartwatches have become increasingly popular for commercial use. Their ever-increasing capabilities, accuracy, and sophistication of smartwatches is making them increasingly appealing to physical activity researchers as a valuable research tool. The non-invasive nature, prevalence, and versatility of smartwatches is being utilized to track heart rate, blood-oxygen levels, activity and movement, and sleep. However, the current state of the art lacks a uniform method to extract, organize, and analyze data collected from these devices. The objective of this research was to develop a Python software library that is widely available, highly capable, and easy to use with the data collected by the Apple Watch. The library was designed to offer data science, visualization, and mining features that help physical activity research find and communicate patterns in the Apple Health data. The custom-built caching system of the library provides near-instant runtime to parse and analyze large files without trading off on memory usage. The Wanjara Smartwatch Library has significantly better performance, proven reliability and robustness, and improved usability than the alternatives discovered in the review of the literature.","abstract_has_math":false,"creators":["Wanjara, Dhwan Devendra"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Buehrer, Richard M.","Lester, Luke F."],"committee_members":["Martin, Thomas L.","Volpe, Stella L."],"year":2022,"date_issued":"2022-06-13","date_published":"2022-06-13","updated_at":"2026-07-22T22:19:45Z","subjects":["Apple Watch","Apple Health Data","Python Library","Health and Exercise","Smartwatch","software library"],"languages":["en"],"rights":["Creative Commons Attribution 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:34814"],"render_values":[{"text":"vt_gsexam:34814","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/110763","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Buehrer, Richard M.","Lester, Luke F."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Martin, Thomas L.","Volpe, Stella L."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Wanjara, Dhwan Devendra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-06-14T08:00:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-14T08:00:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-06-13"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Apple Watch","Apple Health Data","Python Library","Health and Exercise","Smartwatch","software library"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:34814"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/110763"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Over the past 10 years, smartwatches have become increasingly popular for commercial use. Their ever-increasing capabilities, accuracy, and sophistication of smartwatches is making them increasingly appealing to physical activity researchers as a valuable research tool. The non-invasive nature, prevalence, and versatility of smartwatches is being utilized to track heart rate, blood-oxygen levels, activity and movement, and sleep. However, the current state of the art lacks a uniform method to extract, organize, and analyze data collected from these devices. The objective of this research was to develop a Python software library that is widely available, highly capable, and easy to use with the data collected by the Apple Watch. The library was designed to offer data science, visualization, and mining features that help physical activity research find and communicate patterns in the Apple Health data. The custom-built caching system of the library provides near-instant runtime to parse and analyze large files without trading off on memory usage. The Wanjara Smartwatch Library has significantly better performance, proven reliability and robustness, and improved usability than the alternatives discovered in the review of the literature."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Over the past 10 years, smartwatches have become increasingly popular for commercial use. Their ever-increasing capabilities, accuracy, and sophistication of smartwatches is making them increasingly appealing to physical activity researchers as a valuable research tool. The non-invasive nature, prevalence, and versatility of smartwatches is being utilized to track heart rate, blood-oxygen levels, activity and movement, and sleep. However, the current state of the art lacks a uniform method to extract, organize, and analyze data collected from these devices. The objective of this research was to develop a Python software library that is widely available, highly capable, and easy to use with the data collected by the Apple Watch. The library was designed to offer data science, visualization, and mining features that help physical activity research find and communicate patterns in the Apple Health data. The custom-built caching system of the library provides near-instant runtime to parse and analyze large files without trading off on memory usage. The Wanjara Smartwatch Library has significantly better performance, proven reliability and robustness, and improved usability than the alternatives discovered in the review of the literature."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Improving the Accessibility of Smartwatches as Research Tools by Developing a Software Library"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Buehrer, Richard M.","Lester, Luke F."],"dc:contributor.committeemember":["Martin, Thomas L.","Volpe, Stella L."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Wanjara, Dhwan Devendra"],"dc:date.accessioned":["2022-06-14T08:00:22Z"],"dc:date.available":["2022-06-14T08:00:22Z"],"dc:date.issued":["2022-06-13"],"dc:description.abstract":["Over the past 10 years, smartwatches have become increasingly popular for commercial use. Their ever-increasing capabilities, accuracy, and sophistication of smartwatches is making them increasingly appealing to physical activity researchers as a valuable research tool. The non-invasive nature, prevalence, and versatility of smartwatches is being utilized to track heart rate, blood-oxygen levels, activity and movement, and sleep. However, the current state of the art lacks a uniform method to extract, organize, and analyze data collected from these devices. The objective of this research was to develop a Python software library that is widely available, highly capable, and easy to use with the data collected by the Apple Watch. The library was designed to offer data science, visualization, and mining features that help physical activity research find and communicate patterns in the Apple Health data. The custom-built caching system of the library provides near-instant runtime to parse and analyze large files without trading off on memory usage. The Wanjara Smartwatch Library has significantly better performance, proven reliability and robustness, and improved usability than the alternatives discovered in the review of the literature."],"dc:description.abstractgeneral":["Over the past 10 years, smartwatches have become increasingly popular for commercial use. Their ever-increasing capabilities, accuracy, and sophistication of smartwatches is making them increasingly appealing to physical activity researchers as a valuable research tool. The non-invasive nature, prevalence, and versatility of smartwatches is being utilized to track heart rate, blood-oxygen levels, activity and movement, and sleep. However, the current state of the art lacks a uniform method to extract, organize, and analyze data collected from these devices. The objective of this research was to develop a Python software library that is widely available, highly capable, and easy to use with the data collected by the Apple Watch. The library was designed to offer data science, visualization, and mining features that help physical activity research find and communicate patterns in the Apple Health data. The custom-built caching system of the library provides near-instant runtime to parse and analyze large files without trading off on memory usage. The Wanjara Smartwatch Library has significantly better performance, proven reliability and robustness, and improved usability than the alternatives discovered in the review of the literature."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:34814"],"dc:identifier.uri":["http://hdl.handle.net/10919/110763"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Apple Watch","Apple Health Data","Python Library","Health and Exercise","Smartwatch","software library"],"dc:title":["Improving the Accessibility of Smartwatches as Research Tools by Developing a Software Library"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:45Z"}