The inclusion of rich sensors on modern smartphones has changed mobile phones from simple communication devices to powerful human-centric sensing platforms. Similar trends are influencing other personal gadgets such as the tablets, cameras, and wearable devices like the Google glass. Together, these sensors can provide
a high-resolution view of the user's context, ranging from simple information like locations and activities, to high-level inferences about the users' intention, behavior, and social interactions. Understanding such context can help solving existing system-side
challenges and eventually enable a new world of real-life applications.
In this thesis, we propose to learn human behavior via multi-modal sensing. The intuition is that human behaviors leave footprints on different sensing dimensions - visual, acoustic, motion and in cyber space. By collaboratively analyzing these footprints, the system can obtain valuable insights about the user. We show that the
analysis results can lead to a series of applications including capturing life-logging videos, tagging user-generated photos and enabling new ways for human-object interactions. Moreover, the same intuition may potentially be applied to enhance existing
system-side functionalities - offloading, prefetching and compression.
","abstract_html":"<p>The inclusion of rich sensors on modern smartphones has changed mobile phones from simple communication devices to powerful human-centric sensing platforms. Similar trends are influencing other personal gadgets such as the tablets, cameras, and wearable devices like the Google glass. Together, these sensors can provide</p><p>a high-resolution view of the user's context, ranging from simple information like locations and activities, to high-level inferences about the users' intention, behavior, and social interactions. Understanding such context can help solving existing system-side</p><p>challenges and eventually enable a new world of real-life applications. </p><p>In this thesis, we propose to learn human behavior via multi-modal sensing. The intuition is that human behaviors leave footprints on different sensing dimensions - visual, acoustic, motion and in cyber space. By collaboratively analyzing these footprints, the system can obtain valuable insights about the user. We show that the</p><p>analysis results can lead to a series of applications including capturing life-logging videos, tagging user-generated photos and enabling new ways for human-object interactions. Moreover, the same intuition may potentially be applied to enhance existing</p><p>system-side functionalities - offloading, prefetching and compression.</p>","abstract_has_math":false,"creators":["Bao, Xuan"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Roy Choudhury, Romit"],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-24T02:07:08Z","subjects":["Computer science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10161/8052","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Roy Choudhury, Romit"]},{"key":"dc:creator","label":"Author","values":["Bao, Xuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-11-14T19:14:34Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2013-11-14T19:14:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2013"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10161/8052"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The inclusion of rich sensors on modern smartphones has changed mobile phones from simple communication devices to powerful human-centric sensing platforms. Similar trends are influencing other personal gadgets such as the tablets, cameras, and wearable devices like the Google glass. Together, these sensors can provide
a high-resolution view of the user's context, ranging from simple information like locations and activities, to high-level inferences about the users' intention, behavior, and social interactions. Understanding such context can help solving existing system-side
challenges and eventually enable a new world of real-life applications.
In this thesis, we propose to learn human behavior via multi-modal sensing. The intuition is that human behaviors leave footprints on different sensing dimensions - visual, acoustic, motion and in cyber space. By collaboratively analyzing these footprints, the system can obtain valuable insights about the user. We show that the
analysis results can lead to a series of applications including capturing life-logging videos, tagging user-generated photos and enabling new ways for human-object interactions. Moreover, the same intuition may potentially be applied to enhance existing
system-side functionalities - offloading, prefetching and compression.
"]},{"key":"dc:title","label":"Title","values":["Enabling Context-Awareness in Mobile Systems via Multi-Modal Sensing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Roy Choudhury, Romit"],"dc:creator":["Bao, Xuan"],"dc:date.accessioned":["2013-11-14T19:14:34Z"],"dc:date.available":["2013-11-14T19:14:34Z"],"dc:date.issued":["2013"],"dc:description.abstract":["The inclusion of rich sensors on modern smartphones has changed mobile phones from simple communication devices to powerful human-centric sensing platforms. Similar trends are influencing other personal gadgets such as the tablets, cameras, and wearable devices like the Google glass. Together, these sensors can provide
a high-resolution view of the user's context, ranging from simple information like locations and activities, to high-level inferences about the users' intention, behavior, and social interactions. Understanding such context can help solving existing system-side
challenges and eventually enable a new world of real-life applications.
In this thesis, we propose to learn human behavior via multi-modal sensing. The intuition is that human behaviors leave footprints on different sensing dimensions - visual, acoustic, motion and in cyber space. By collaboratively analyzing these footprints, the system can obtain valuable insights about the user. We show that the
analysis results can lead to a series of applications including capturing life-logging videos, tagging user-generated photos and enabling new ways for human-object interactions. Moreover, the same intuition may potentially be applied to enhance existing
system-side functionalities - offloading, prefetching and compression.
"],"dc:identifier.uri":["https://hdl.handle.net/10161/8052"],"dc:subject":["Computer science"],"dc:title":["Enabling Context-Awareness in Mobile Systems via Multi-Modal Sensing"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T02:07:08Z"}