Name
Title | ||
|---|---|---|
Privacy perception and fall detection accuracy for in-home video assistive monitoring with privacy enhancements |
Abstract | ||
|---|---|---|
Video of in-home activity provides valuable information for assistive monitoring but raises privacy concerns. Raw video can be privacy-enhanced by obscuring the appearance of a person. We consider five privacy enhancements: blur, silhouette, oval, box, and trailing-arrows. We investigate whether a privacy enhancement exists that provides sufficient perceived privacy while enabling accurate fall detection by humans. We recorded 23 1-minute videos involving normal household activities, falling, and lying on the floor after an earlier fall, and created versions of each video for each privacy setting. We conducted an experiment with 376 undergraduate, non-engineering student participants to measure perceived privacy protection and the participant's fall detection accuracy for each privacy setting. Results indicate that the oval provides sufficient perceived privacy for 88% of participants while still supporting fall detection accuracy of 89%, and that the common privacy enhancements blur and silhouette were perceived to provide insufficient privacy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2384556.2384557 | SIGHIT Record |
Keywords | Field | DocType |
common privacy enhancements blur,insufficient privacy,fall detection accuracy,accurate fall detection,earlier fall,in-home video assistive monitoring,privacy enhancement,1-minute video,privacy concern,privacy setting,privacy perception,privacy protection,video,smart home,privacy | Internet privacy,Computer security,Silhouette,Lying,Home automation,Engineering,Perception,Privacy software | Journal |
Volume | Issue | Citations |
2 | 2 | 10 |
PageRank | References | Authors |
0.90 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex Edgcomb | 1 | 37 | 6.13 |
| Frank Vahid | 2 | 2688 | 218.00 |