{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97278"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97278","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Predictive modeling of health status using motion analysis from mobile phones","abstract":"It is unknown what physiological functions can be monitored at clinical quality with a normal smartphone, which is ubiquitous. There are standard measures like walk speed, pulmonary function and oxygen saturation variation for health status of cardiopulmonary patients. The dissertation is to summarize my studies of using sensor data from regular smartphones to accurately measure walking patterns, in order to monitoring health status for cardiopulmonary patients. Fifty five pulmonary patients were participated in the study. The sensor data for their walk test and free walk are collected and stored by a novel designed Android smartphone application. Different machine learning techniques are applied and compared to predict gait speed, pulmonary function and oxygen saturation. The result shows that walking patterns are highly correlated with health status. The trained models can predict health status accurately for each patient. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. The ultimate goal is that patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.","abstract_html":"It is unknown what physiological functions can be monitored at clinical quality with a normal smartphone, which is ubiquitous. There are standard measures like walk speed, pulmonary function and oxygen saturation variation for health status of cardiopulmonary patients. The dissertation is to summarize my studies of using sensor data from regular smartphones to accurately measure walking patterns, in order to monitoring health status for cardiopulmonary patients. Fifty five pulmonary patients were participated in the study. The sensor data for their walk test and free walk are collected and stored by a novel designed Android smartphone application. Different machine learning techniques are applied and compared to predict gait speed, pulmonary function and oxygen saturation. The result shows that walking patterns are highly correlated with health status. The trained models can predict health status accurately for each patient. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. The ultimate goal is that patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.","abstract_has_math":false,"creators":["Cheng, Qian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Schatz, Bruce R.","Han, Jiawei","Smaragdis, Paris","Konig, Christian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:14:34Z","date_published":"2017-08-10T19:14:34Z","updated_at":"2026-07-22T22:24:32Z","subjects":["Mobile health","Smartphone application","Medical information retrieval","Chronic disease"],"languages":["en"],"rights":["Copyright 2017 Qian Cheng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97278","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Schatz, Bruce R.","Han, Jiawei","Smaragdis, Paris","Konig, Christian"]},{"key":"dc:creator","label":"Author","values":["Cheng, Qian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:14:34Z","2017-03-29","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mobile health","Smartphone application","Medical information retrieval","Chronic disease"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Qian Cheng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97278"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["It is unknown what physiological functions can be monitored at clinical quality with a normal smartphone, which is ubiquitous. There are standard measures like walk speed, pulmonary function and oxygen saturation variation for health status of cardiopulmonary patients. The dissertation is to summarize my studies of using sensor data from regular smartphones to accurately measure walking patterns, in order to monitoring health status for cardiopulmonary patients. Fifty five pulmonary patients were participated in the study. The sensor data for their walk test and free walk are collected and stored by a novel designed Android smartphone application. Different machine learning techniques are applied and compared to predict gait speed, pulmonary function and oxygen saturation. The result shows that walking patterns are highly correlated with health status. The trained models can predict health status accurately for each patient. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. The ultimate goal is that patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Qian Cheng, accepted the attached license on 2017-03-16 at 15:59.","The student, Qian Cheng, submitted this Dissertation for approval on 2017-03-16 at 16:07.","This Dissertation was approved for publication on 2017-03-29 at 09:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10598 on 2017-08-10 at 13:38:11","Made available in DSpace on 2017-08-10T19:14:34Z (GMT). No. of bitstreams: 2 CHENG-DISSERTATION-2017.pdf: 1714931 bytes, checksum: 167023c22efed9d4a097f17012154ddd (MD5) LICENSE.txt: 4207 bytes, checksum: ebf383d6b93138bfa976cd6360f608aa (MD5) Previous issue date: 2017-03-29"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Predictive modeling of health status using motion analysis from mobile phones"]}]}],"canonical_facts":{"dc:contributor":["Schatz, Bruce R.","Han, Jiawei","Smaragdis, Paris","Konig, Christian"],"dc:creator":["Cheng, Qian"],"dc:date":["2017-08-10T19:14:34Z","2017-03-29","2017-05"],"dc:description":["It is unknown what physiological functions can be monitored at clinical quality with a normal smartphone, which is ubiquitous. There are standard measures like walk speed, pulmonary function and oxygen saturation variation for health status of cardiopulmonary patients. The dissertation is to summarize my studies of using sensor data from regular smartphones to accurately measure walking patterns, in order to monitoring health status for cardiopulmonary patients. Fifty five pulmonary patients were participated in the study. The sensor data for their walk test and free walk are collected and stored by a novel designed Android smartphone application. Different machine learning techniques are applied and compared to predict gait speed, pulmonary function and oxygen saturation. The result shows that walking patterns are highly correlated with health status. The trained models can predict health status accurately for each patient. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. The ultimate goal is that patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Qian Cheng, accepted the attached license on 2017-03-16 at 15:59.","The student, Qian Cheng, submitted this Dissertation for approval on 2017-03-16 at 16:07.","This Dissertation was approved for publication on 2017-03-29 at 09:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10598 on 2017-08-10 at 13:38:11","Made available in DSpace on 2017-08-10T19:14:34Z (GMT). No. of bitstreams: 2 CHENG-DISSERTATION-2017.pdf: 1714931 bytes, checksum: 167023c22efed9d4a097f17012154ddd (MD5) LICENSE.txt: 4207 bytes, checksum: ebf383d6b93138bfa976cd6360f608aa (MD5) Previous issue date: 2017-03-29"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/97278"],"dc:language":["en"],"dc:rights":["Copyright 2017 Qian Cheng"],"dc:subject":["Mobile health","Smartphone application","Medical information retrieval","Chronic disease"],"dc:title":["Predictive modeling of health status using motion analysis from mobile phones"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:32Z"}