TY - GEN
T1 - The Tesserae project
T2 - 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019
AU - Mattingly, Stephen M.
AU - Gregg, Julie M.
AU - Audia, Pino
AU - Bayraktaroglu, Ayse Elvan
AU - Campbell, Andrew T.
AU - Chawla, Nitesh V.
AU - Swain, Vedant Das
AU - De Choudhury, Munmun
AU - D'Mello, Sidney K.
AU - Dey, Anind K.
AU - Gao, Ge
AU - Jagannath, Krithika
AU - Jiang, Kaifeng
AU - Lin, Suwen
AU - Liu, Qiang
AU - Mark, Gloria
AU - Martinez, Gonzalo J.
AU - Masaba, Kizito
AU - Mirjafari, Shayan
AU - Moskal, Edward
AU - Mulukutla, Raghu
AU - Nies, Kari
AU - Reddy, Manikanta D.
AU - Robles-Granda, Pablo
AU - Saha, Koustuv
AU - Sirigiri, Anusha
AU - Striegel, Aaron
N1 - Funding Information:
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA Contract No. 2017-17042800007. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s). ACM
PY - 2019/5/2
Y1 - 2019/5/2
N2 - The Tesserae project investigates how a suite of sensors can measure workplace performance (e.g., organizational citizenship behavior), psychological traits (e.g., personality, affect), and physical characteristics (e.g., sleep, activity) over one year. We enrolled 757 information workers across the U.S. and measure heart rate, physical activity, sleep, social context, and other aspects through smartwatches, a phone agent, beacons, and social media. We report challenges that we faced with enrollment, privacy, and incentive structures while setting up such a long-term multimodal large-scale sensor study. We discuss the tradeoffs of remote versus in-person enrollment, and showed that directly paid, in-person enrolled participants are more compliant overall compared to remotely-enrolled participants. We find that providing detailed information regarding privacy concerns up-front is highly beneficial. We believe that our experiences can benefit other large sensor projects as this field grows.
AB - The Tesserae project investigates how a suite of sensors can measure workplace performance (e.g., organizational citizenship behavior), psychological traits (e.g., personality, affect), and physical characteristics (e.g., sleep, activity) over one year. We enrolled 757 information workers across the U.S. and measure heart rate, physical activity, sleep, social context, and other aspects through smartwatches, a phone agent, beacons, and social media. We report challenges that we faced with enrollment, privacy, and incentive structures while setting up such a long-term multimodal large-scale sensor study. We discuss the tradeoffs of remote versus in-person enrollment, and showed that directly paid, in-person enrolled participants are more compliant overall compared to remotely-enrolled participants. We find that providing detailed information regarding privacy concerns up-front is highly beneficial. We believe that our experiences can benefit other large sensor projects as this field grows.
KW - Phone agent
KW - Privacy
KW - Sensors
KW - Smartwatches
KW - Social media
KW - Stress
UR - http://www.scopus.com/inward/record.url?scp=85067275122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067275122&partnerID=8YFLogxK
U2 - 10.1145/3290607.3299041
DO - 10.1145/3290607.3299041
M3 - Conference contribution
AN - SCOPUS:85067275122
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 4 May 2019 through 9 May 2019
ER -