With the prevalence of computing, many workers today are confined to desk within an office. By sitting in these positions for long periods of time, workers are prone to develop one of many musculoskeletal disorders (MSDs), such as carpal tunnel syndrome. In order to prevent MSDs in the long term, workers must employ good sitting habits. One promising method to ensure good workplace posture is through camera monitoring. To date, camera systems have been used in determining posture in a clean environment. However, an occluded and cluttered background, which is typical in an office setting, imposes a great challenge for a computer vision system to detect desired objects. In this thesis, we design and propose components that assess good posture using information gathered from a Microsoft Kinect camera. To do so, we generate a data set of posture captures to test and train, applying crowd-sourced voting to determine ratings for a subset of these captures. Leveraging this data set, we apply machine learning to develop a classification tool. Finally, we explore and compare the usage of depth information in conjunction with a traditional RGB sensor array and present novel implementations of a wrist locating method.
","abstract_html":"<p>With the prevalence of computing, many workers today are confined to desk within an office. By sitting in these positions for long periods of time, workers are prone to develop one of many musculoskeletal disorders (MSDs), such as carpal tunnel syndrome. In order to prevent MSDs in the long term, workers must employ good sitting habits. One promising method to ensure good workplace posture is through camera monitoring. To date, camera systems have been used in determining posture in a clean environment. However, an occluded and cluttered background, which is typical in an office setting, imposes a great challenge for a computer vision system to detect desired objects. In this thesis, we design and propose components that assess good posture using information gathered from a Microsoft Kinect camera. To do so, we generate a data set of posture captures to test and train, applying crowd-sourced voting to determine ratings for a subset of these captures. Leveraging this data set, we apply machine learning to develop a classification tool. Finally, we explore and compare the usage of depth information in conjunction with a traditional RGB sensor array and present novel implementations of a wrist locating method.</p>","abstract_has_math":false,"creators":["Crussell, Matthew"],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Jane Zhang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-04-01T07:00:00Z","date_published":"2017-04-01T07:00:00Z","updated_at":"2026-07-24T01:31:49Z","subjects":["Other Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2017.26"],"render_values":[{"text":"10.15368/theses.2017.26","href":"https://doi.org/10.15368/theses.2017.26","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1710","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jane Zhang"]},{"key":"dc:creator","label":"Author","values":["Crussell, Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-04-15T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Other Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1710","10.15368/theses.2017.26"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["With the prevalence of computing, many workers today are confined to desk within an office. By sitting in these positions for long periods of time, workers are prone to develop one of many musculoskeletal disorders (MSDs), such as carpal tunnel syndrome. In order to prevent MSDs in the long term, workers must employ good sitting habits. One promising method to ensure good workplace posture is through camera monitoring. To date, camera systems have been used in determining posture in a clean environment. However, an occluded and cluttered background, which is typical in an office setting, imposes a great challenge for a computer vision system to detect desired objects. In this thesis, we design and propose components that assess good posture using information gathered from a Microsoft Kinect camera. To do so, we generate a data set of posture captures to test and train, applying crowd-sourced voting to determine ratings for a subset of these captures. Leveraging this data set, we apply machine learning to develop a classification tool. Finally, we explore and compare the usage of depth information in conjunction with a traditional RGB sensor array and present novel implementations of a wrist locating method.
"]},{"key":"dc:title","label":"Title","values":["Workplace Posture Assessment and Biofeedback with Kinect"]}]}],"canonical_facts":{"dc:contributor":["Jane Zhang"],"dc:creator":["Crussell, Matthew"],"dc:date.available":["2017-04-15T07:00:00Z"],"dc:description.abstract":["With the prevalence of computing, many workers today are confined to desk within an office. By sitting in these positions for long periods of time, workers are prone to develop one of many musculoskeletal disorders (MSDs), such as carpal tunnel syndrome. In order to prevent MSDs in the long term, workers must employ good sitting habits. One promising method to ensure good workplace posture is through camera monitoring. To date, camera systems have been used in determining posture in a clean environment. However, an occluded and cluttered background, which is typical in an office setting, imposes a great challenge for a computer vision system to detect desired objects. In this thesis, we design and propose components that assess good posture using information gathered from a Microsoft Kinect camera. To do so, we generate a data set of posture captures to test and train, applying crowd-sourced voting to determine ratings for a subset of these captures. Leveraging this data set, we apply machine learning to develop a classification tool. Finally, we explore and compare the usage of depth information in conjunction with a traditional RGB sensor array and present novel implementations of a wrist locating method.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1710","10.15368/theses.2017.26"],"dc:subject":["Other Electrical and Computer Engineering"],"dc:title":["Workplace Posture Assessment and Biofeedback with Kinect"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:31:49Z"}