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As we develop computing platforms for augmented reality (AR) head-mounted display (HMDs) technologies for social or workplace environments, understanding how users interact with notifications in immersive environments has become crucial. We researched effectiveness and user preferences of different interaction modalities for notifications, along with two types of notification display methods. In our study, participants were immersed in a simulated cooking environment using an AR-HMD, where they had to fulfill customer orders. During the cooking process, participants received notifications related to customer orders and ingredient updates. They were given three interaction modes for those notifications: voice commands, eye gaze and dwell, and hand gestures. To manage multiple notifications at once, we also researched two different notification list displays, one attached to the user’s hand and one in the world. Results indicate that participants preferred using their hands to interact with notifications and having the list of notifications attached to their hands. Voice and gaze interaction was perceived as having lower usability than touch
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Nurture: Notifying Users at the Right Time Using Reinforcement Learning
User interaction is an essential part of many mobile devices such as smartphones and wrist bands. Only by interacting with the user can these devices deliver services, enable proper configurations, and learn user preferences. Push notifications are the primary method used to attract user attention in modern devices. However, these notifications can be ineffective and even irritating if they prompt the user at an inappropriate time. The discontent is exacerbated by the large number of applications that target limited user attention. We propose a reinforcement learning-based personalization technique, called Nurture, which automatically identifies the appropriate time to send notifications for a given user context. Through simulations with the crowd-sourcing platform Amazon Mechanical Turk, we show that our approach successfully learns user preferences and significantly improves the rate of notification responses.
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- Award ID(s):
- 1640813
- PAR ID:
- 10111008
- Date Published:
- Journal Name:
- Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
- Page Range / eLocation ID:
- 1194 to 1201
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3267305.3274107
