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TypeOut: Just-in-Time Self-Affirmation for Reducing Phone Use

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Smartphone overuse is related to a variety of issues such as lack of sleep and anxiety. We explore the application of Self-Affirmation Theory on smartphone overuse intervention in a just-in-time manner. We present TypeOut, a just-in-time intervention technique that integrates two components: an in-situ typing-based unlock process to improve user engagement, and self-affirmation-based typing content to enhance effectiveness. We hypothesize that the integration of typing and self-affirmation content can better reduce smartphone overuse. We conducted a 10-week within-subject field experiment (N=54) and compared TypeOut against two baselines: one only showing the self-affirmation content (a common notification-based intervention), and one only requiring typing non-semantic content (a state-of-the-art method). TypeOut reduces app usage by over 50%, and both app opening frequency and usage duration by over 25%, all significantly outperforming baselines. TypeOut can potentially be used in other domains where an intervention may benefit from integrating self-affirmation exercises with an engaging just-in-time mechanism.

Typeout: Leveraging just-in-time self-affirmation for smartphone overuse reduction. Xuhai Xu, Tianyuan Zou, Xiao Han, Yanzhang Li, Ruolin Wang, Tianyi Yuan, Yuntao Wang, Yuanchun Shi, Jennifer Mankoff,and Anind K. Dey. 2022. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM, New York, NY, USA.

Interaction via Wireless Earbuds

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Xuhai Xu, Haitian Shi, Xin Yi, Wenjia Liu, Yukang Yan, Yuanchun Shi, Alex Mariakakis, Jennifer Mankoff, Anind K. Dey:
EarBuddy: Enabling On-Face Interaction via Wireless Earbuds. CHI 2020: 1-14

Past research regarding on-body interaction typically requires custom sensors, limiting their scalability and generalizability. We propose EarBuddy, a real-time system that leverages the microphone in commercial wireless earbuds to detect tapping and sliding gestures near the face and ears. We develop a design space to generate 27 valid gestures and conducted a user study (N=16) to select the eight gestures that were optimal for both human preference and microphone detectability. We collected a dataset on those eight gestures (N=20) and trained deep learning models for gesture detection and classification. Our optimized classifier achieved an accuracy of 95.3%. Finally, we conducted a user study (N=12) to evaluate EarBuddy’s usability. Our results show that EarBuddy can facilitate novel interaction and that users feel very positively about the system. EarBuddy provides a new eyes-free, socially acceptable input method that is compatible with commercial wireless earbuds and has the potential for scalability and generalizability

  • A user is tapping on their cheek. The tapping is sensed by a microphone in the earphone.
  • Plot of Test Accuracy versus number of left-out user's additional samples
  • Two barplots from the EarBuddy user study comparing EarBuddy, Table and Pocket.
  • Gesture design of EarBuddy.

HulaMove: Waist Interaction

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Xuhai Xu, Jiahao Li, Tianyi Yuan, Liang He, Xin Liu, Yukang Yan, Yuntao Wang, Yuanchun Shi, Jennifer Mankoff, Anind K. Dey:
HulaMove: Using Commodity IMU for Waist Interaction. CHI 2021: 503:1-503:16

We present HulaMove, a novel interaction technique that leverages the movement of the waist as a new eyes-free and hands-free input method for both the physical world and the virtual world. We first conducted a user study (N=12) to understand users’ ability to control their waist. We found that users could easily discriminate eight shifting directions and two rotating orientations, and quickly confirm actions by returning to the original position (quick return). We developed a design space with eight gestures for waist interaction based on the results and implemented an IMU-based real-time system. Using a hierarchical machine learning model, our system could recognize waist gestures at an accuracy of 97.5%. Finally, we conducted a second user study (N=12) for usability testing in both real-world scenarios and virtual reality settings. Our usability study indicated that HulaMove significantly reduced interaction time by 41.8% compared to a touch screen method, and greatly improved users’ sense of presence in the virtual world. This novel technique provides an additional input method when users’ eyes or hands are busy, accelerates users’ daily operations, and augments their immersive experience in the virtual world.

The Limits of Expert Text Entry Speed

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Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect is a powerful way to reduce the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies of autocorrect uncovered an unexplored side-effect: participants’ aversion to typing errors despite autocorrect. We present the first computational model of typing on keyboards with autocorrect, which enables precise study of expert typists’ aversion to typing errors on such keyboards. Unlike empirical typing studies that last days, our model evaluates the effects of typists’ aversion to typing errors for any autocorrect accuracy in seconds. We show that typists’ aversion to typing errors adds a self-imposed limit on upper bound typing speeds, which decreases the value of highly accurate autocorrect. Our findings motivate future designs of keyboards with autocorrect that reduce typists’ aversion to typing errors to increase typing speeds.

The Limits of Expert Text Entry Speed on Mobile Keyboards with Autocorrect Nikola Banovic, Ticha Sethapakdi, Yasasvi Hari, Anind K. Dey, Jennifer Mankoff. Mobile HCI 2019.

A picture of a samsung phone. The screen says: Block 2. Trial 6 of 10. this camera takes nice photographs. The user has begun typing with errors: "this camera tankes l" Error correction offers 'tankes' 'tankers' and 'takes' and a soft keyboard is shown before that.

An example mobile device with a soft keyboard: A) text entry area, which in our study contained study progress, the current phrase to transcribe, and an area for transcribed characters, B) automatically suggested words, and C) a miniQWERTY soft keyboard with autocorrect.

A bar plat showing typing speed (WPM, y axis) against acuracy (0 to 1). The bars start at 32 WPM (for 0 accuracy) and go up to approx 32 (for accuracy of 1).
Our model estimated expected mean typing speeds (lines) for different levels of typing error rate aversion (e) compared to mean empirical typing speed with automatic correction and suggestion (bar plot) in WPM across Accuracy. Error bars represent 95% confidence intervals.
4 bar plats showing error rate in uncorrected, corrected, autocorrected, and manual corrected conditions. Error rates for uncorrected are (approximately) 0 to 0.05 as accuracy increases; error rates for corrected are .10 to .005 for corrected condition as accuracy goes from 0 to 1. Error rates are 0 to about .1 for uncorrected as accuracy goes from 0 to 1. Error rates are variable but all below 0.05 for manual as accuracy goes from 0 to 1
Median empirical error rates across Accuracy in session 3 with automated correction and suggestion. Error bars represent minimum and maximum error rate values, and dots represent outliers

Clench Interaction: Biting As Input

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Shows human faced diagraming where the clench sensor should be placed between the teeth; the settings for correctly sensing clench, and the hardware platform used.
Clench Interaction: Novel Biting Input Techniques
Xuhai Xu, Chun Yu, Anind K. Dey, Jennifer Mankoff
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19)
People eat every day and biting is one of the most fundamental and natural actions that they perform on a daily basis. Existing work has explored tooth click location and jaw movement as input techniques, however clenching has the potential to add control to this input channel. We propose clench interaction that leverages clenching as an actively controlled physiological signal that can facilitate interactions. We conducted a user study to investigate users’ ability to control their clench force. We found that users can easily discriminate three force levels, and that they can quickly confirm actions by unclenching (quick release). We developed a design space for clench interaction based on the results and investigated the usability of the clench interface. Participants preferred the clench over baselines and indicated a willingness to use clench-based interactions. This novel technique can provide an additional input method in cases where users’ eyes or hands are busy, augment immersive experiences such as virtual/augmented reality, and assist individuals with disabilities.

Probabilistic Input

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Increasingly natural, sensed, and touch-based input is being integrated into devices. Along the way, both custom and more general solutions have been developed for dealing with the uncertainty that is associated with these forms of input. However, it is difficult to provide dynamic, flexible, and continuous feedback about uncertainty using traditional interactive infrastructure. Our contribution is a general architecture with the goal of providing support for continual feedback about uncertainty.

Our architecture tracks multiple interfaces – one for each plausible and differentiable sequence of input that the user may have intended. This paper presents a method for reducing the number of alternative interfaces and fusing possible interfaces into a single interface that both communicates uncertainty and allows for disambiguation.

Rather than tracking a single interface state (as is currently done in most UI toolkits), we keep track of several possible interfaces. Each possible interface represents a state that the interface might be in. The likelihood of each possible interface is updated based on user inputs and our knowledge of user behavior. Feedback to the user is rendered by first reducing the set of possible interfaces to a representative set, then fusing interface alternatives into a single interface, which is then rendered.


Julia Schwarz, Jennifer Mankoff, Scott E. Hudson:
An Architecture for Generating Interactive Feedback in Probabilistic User Interfaces. CHI 2015: 2545-2554

Julia Schwarz, Jennifer Mankoff, Scott E. Hudson:
Monte carlo methods for managing interactive state, action and feedback under uncertainty. UIST 2011: 235-244

Julia Schwarz, Scott E. Hudson, Jennifer Mankoff, Andrew D. Wilson:
A framework for robust and flexible handling of inputs with uncertainty. UIST 2010: 47-56

Replacing ‘Wave to Engage’ with ‘Intent to Interact’

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Schwarz, J., Marais, C., Leyvand, T., Hudson, S., Mankoff, J. Combining Body Pose, Gaze and Motion to Determine Intention to Interact in Vision-Based Interfaces. In Proceedings of the 32nd Annual SIGCHI Conference on Human Factors in Computing Systems (Toronto, Canada, April 26 – May 1, 2014). CHI ’14. ACM, New York, NY.

 paper  | video summary  | slides

Vision-based interfaces, such as those made popular by the
Microsoft Kinect, suffer from the Midas Touch problem:
every user motion can be interpreted as an interaction. In
response, we developed an algorithm that combines facial
features, body pose and motion to approximate a user’s
intention to interact with the system. We show how this can
be used to determine when to pay attention to a user’s actions and when to ignore them. To demonstrate the value of
our approach, we present results from a 30-person lab study
conducted to compare four engagement algorithms in single
and multi-user scenarios. We found that combining intention to interact with a “raise an open hand in front of you”
gesture yielded the best results. The latter approach offers a
12% improvement in accuracy and a 20% reduction in time
to engage over a baseline “wave to engage” gesture currently used on the Xbox 360