BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Pacific BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210816T010356Z UID:3D908AE1-8DF1-4363-BA3B-6A2BA6B6E5E4 DTSTART;TZID=US/Pacific:20210701T180000 DTEND;TZID=US/Pacific:20210701T193000 DESCRIPTION:Abstract:\nWith the increasing number of vehicles and traffic a ccidents\, driving safety has become an important factor that affects our daily lives. As the primary cause of driving accidents\, driving fatigue c ould be prevented by a sensing and alarm system built in the vehicle. In t his talk\, we propose to exploit radio frequency identification (RFID) tag s as low-cost wearable sensors for driving fatigue detection. Unlike tradi tional video camera based solutions\, this approach does not require light ing in the driving environment and can effectively protect the privacy of users. We first present the NodTrack system\, which sense the driver’s n odding movements using commodity RFID. To accurately extract nodding featu res\, we propose an effective approach to mitigate the environment noise\, the interference caused by surrounding movements\, and the cumulative err or caused by the frequency hopping offset in FCC-compliant RFID systems. A long short-term memory (LSTM) autoencoder is utilized to detect nodding m ovements using calibrated data. The second part of the talk presents an RF ID based respiration monitoring system for driving environments. Since bre athing rate is a key indicator of drowsy state\, respiration monitoring in the noisy driving environment is useful for developing an effective drivi ng fatigue detection system. The system estimates the respiration rate of a driver based on phase values sampled from multiple RFID tags attached to the seat belt\, exploiting tag diversity to combat the strong noise in th e driving environment. The proposed systems are implemented with commodity RFID devices. Their accurate and robust performances are demonstrated wit h extensive experiments conducted in a moving car. The highly accurate det ection performances of the proposed systems are validated by extensive exp eriments in various real driving scenarios.\n\nSpeaker: Prof. SHIWEN MAO\, Earle C. Williams Eminent Scholar Chair\, and Director of the Wireless En gineering Research and Education Center at Auburn University\, a Fellow of the IEEE\, Distinguished Lecturer of IEEE Communications Society and Dist inguished Speaker of IEEE Vehicular Technology Society. See more detailed bio below.\n\nSpeaker(s): Prof. Shiwen Mao\, \n\nSan Deigo\, California\, United States\, Virtual: https://events.vtools.ieee.org/m/274072 LOCATION:San Deigo\, California\, United States\, Virtual: https://events.v tools.ieee.org/m/274072 ORGANIZER:maliangp@yahoo.com SEQUENCE:5 SUMMARY:RFID based Driving Fatigue Detection URL;VALUE=URI:https://events.vtools.ieee.org/m/274072 X-ALT-DESC:Description: <br /><p><strong>Abstract:</strong>&nbsp\;<br />Wit h the increasing number of vehicles and traffic accidents\, driving safety has become an important factor that affects our daily lives. As the prima ry cause of driving accidents\, driving fatigue could be prevented by a se nsing and alarm system built in the vehicle. In this talk\, we propose to exploit radio frequency identification (RFID) tags as low-cost wearable se nsors for driving fatigue detection. Unlike traditional video camera based solutions\, this approach does not require lighting in the driving enviro nment and can effectively protect the privacy of users. We first present t he NodTrack system\, which sense the driver&rsquo\;s nodding movements usi ng commodity RFID. To accurately extract nodding features\, we propose an effective approach to mitigate the environment noise\, the interference ca used by surrounding movements\, and the cumulative error caused by the fre quency hopping offset in FCC-compliant RFID systems. A long short-term mem ory (LSTM) autoencoder is utilized to detect nodding movements using calib rated data. The second part of the talk presents an RFID based respiration monitoring system for driving environments. Since breathing rate is a key indicator of drowsy state\, respiration monitoring in the noisy driving e nvironment is useful for developing an effective driving fatigue detection system. The system estimates the respiration rate of a driver based on ph ase values sampled from multiple RFID tags attached to the seat belt\, exp loiting tag diversity to combat the strong noise in the driving environmen t. The proposed systems are implemented with commodity RFID devices. Their accurate and robust performances are demonstrated with extensive experime nts conducted in a moving car. The highly accurate detection performances of the proposed systems are validated by extensive experiments in various real driving scenarios.</p>\n<p><strong>Speaker: Prof. SHIWEN MAO</strong> \, Earle C. Williams Eminent Scholar Chair\, and Director of the Wireless Engineering Research and Education Center at Auburn University\, a Fellow of the IEEE\, Distinguished Lecturer of IEEE Communications Society and Di stinguished Speaker of IEEE Vehicular Technology Society. See more detaile d bio below.</p> END:VEVENT END:VCALENDAR