{"id":159227,"date":"2010-09-01T00:00:00","date_gmt":"2010-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/user-experiences-with-activity-based-navigation-on-mobile-devices\/"},"modified":"2018-10-16T21:40:47","modified_gmt":"2018-10-17T04:40:47","slug":"user-experiences-with-activity-based-navigation-on-mobile-devices","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/user-experiences-with-activity-based-navigation-on-mobile-devices\/","title":{"rendered":"User Experiences with Activity-Based Navigation on Mobile Devices"},"content":{"rendered":"

We introduce activity-based navigation, which uses human activities derived from sensor data to help people navigate, in particular to retrace a \u2015trail\u2016 previously taken by that person or another person. Such trails may include step counts, walking up\/down stairs or taking elevators, compass directions, and photos taken along a user\u2018s path, in addition to absolute positioning (GPS and maps) when available. To explore the user experience of activity-based navigation, we built Greenfield, a mobile device interface for finding a car. We conducted a ten participant user study comparing users\u2018 ability to find cars across three different presentations of activity-based information as well as verbal instructions. Our results show that activity-based navigation can be used for car finding and suggest its promise more generally for supporting navigation tasks. We present lessons for future activity-based navigation interfaces, and motivate further work in this space, particularly in the area of robust activity inference.<\/p>\n","protected":false},"excerpt":{"rendered":"

We introduce activity-based navigation, which uses human activities derived from sensor data to help people navigate, in particular to retrace a \u2015trail\u2016 previously taken by that person or another person. Such trails may include step counts, walking up\/down stairs or taking elevators, compass directions, and photos taken along a user\u2018s path, in addition to absolute […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13552,13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-159227","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-hardware-devices","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"Association for Computing 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