Automated In-Home Assistive Monitoring with Privacy-Enhanced Video

A privacy-enhanced video obscures the appearance of a person in the video. We consider four privacy enhancements: person blurred, person silhouetted, person covered with a bounding-oval, and person covered by a bounding-box. We demonstrate that privacy-enhanced video can be as accurate as raw video for eight in-home assistive monitoring goals: energy expenditure estimation, in room too long, leave but not return at night, arisen in morning, not arisen in morning, in region too long, abnormally inactive during day, and fall detection. Each monitoring goal´s solution was trained using one actor and tested using two different actors. The privacy enhancements of silhouette, bounding-oval, and bounding-box, did not degrade achievement of the eight assistive monitoring goals. Raw video had a fidelity of 0.994 for the goal of energy expenditure estimation, while silhouette had 0.995, bounding-oval had 0.994, and bounding-box had 0.997. The fall detection algorithm yielded the same sensitivity of 0.91 and specificity of 0.92 for raw and bounding-oval video, while silhouette had a sensitivity of 0.91 and specificity of 0.75, and bounding-box had a sensitivity of 0.82 and specificity of 0.92. The other 6 goals yielded perfect sensitivity and specificity for raw and privacy-enhanced video, with the exception of blur video´s sensitivity of 0.5 in region too long.