Activity sensing in the wild: a field trial of ubifit garden

By Sunny Consolvo, David W. Mcdonald, Tammy R Toscos, Mike Y. Chen, Jon E. Froehlich, Beverly L. Harrison, Predrag V. Klasnja, Anthony LaMarca, Louis Legrand, R. Libby and 2 more. International Conference on Human Factors in Computing Systems, 2008

1,328 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6ca8fa3ba89ed69128c7f6f03e07f3aa42446596

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Human-Computer Interaction

Citations in the corpus

A Survey of Online Activity Recognition Using Mobile Phones (2015, background): “For example, the authors in [70] use a dedicated activity recognition device for sensing, but use a mobile phone as a feedback device.”
Window Size Impact in Human Activity Recognition (2014, background): “Promoting healthier lifestyles (e.g., encouraging exercising [2,3]), preventing unhealthy habits (e.g., tobacco use or unwholesome food [4,5]), detecting anomalous behaviors (e.g., fall detection [6–8]) or tracking conditions (e.g., mobility worsening due to aging or illnesses [9]) are different…”
Persuasive technology for health and wellness: State-of-the-art and emerging trends (2018, neutral): “6.”
Use and Adoption Challenges of Wearable Activity Trackers (2015, background): “(P8, male, low) Consolvo et al. (2008) detailed the following primary types of perceived errors made by physical activity trackers: make an error in the start time, make an error in the duration, confuse an activity it was trained to infer with another it was trained to infer, confuse an activity…”
Why we use and abandon smart devices (2015, background): “may not have had specific physical disabilities or conditions but were recruited for their interest in increasing physical activity, such as participants in trials of UbiFit Garden, an activity tracker integrated with a mobile phone that provides users with a glanceable symbolic representation of go”
Mobile Phone Sensing Systems: A Survey (2013, background): “Security and privacy of mobile phone sensing systems is still in its infancy.”
Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles (2015, background): “…for different ends, e.g., advertising (Kaptein and Eckles, 2012), promoting healthy or pro-social behaviors (Lambert, 2001; Morris and Guilak, 2009; Consolvo et al., 2008, 2009), and reducing energy consumption (see, e.g., Svane, 2007; Midden et al., 2008; Bang et al., 2006; Dillahunt et al.,…”
Bewell: A smartphone application to monitor, model and promote wellbeing (2011, background): “, stress [5], diet [6], physical activity [7]) and consider only one or two types of behavior.”
Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations (2010, background): “Based on the consumed calorie, the system can provide recommendation and advices when they fail to complete enough exercise and also build systems to encourage people to conduct more activities [4, 5, 3].”
Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity (2012, background): “Users of UbiFit, a mobile phone applica tion to motivate physical activity, set their own primary and alternate weekly physical goals, then chose to work toward their primary or alternate goal each week [3,6].”
An Actor-Critic Contextual Bandit Algorithm for Personalized Interventions using Mobile Devices (2014, background): “The focuses of other studies include physical activity [5, 6], obesity/weight management [7], etc.”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “ities such as running, walking or cycling [69].”
The design of eco-feedback technology (2010, background): “Previous research in persuasive health technology has shown that even providing an asterisk after the completion of a behavior is enough to elicit a positive response [9].”
Using mobile phones to determine transportation modes (2010, methodology): “Related work in this space can be grouped based on the types of systems used to implement the algorithms: commercial devices, custom hardware, and mobile phones [Consolvo et al. 2008b].”
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research (2009, methodology): “Also, in addition to using GPS and GSM features [29, 30, 26], future activity classification techniques could leverage additional on-board sensor types (e.”
Adherence to a Smartphone Application for Weight Loss Compared to Website and Paper Diary: Pilot Randomized Controlled Trial (2013, background): “Another app, UbiFit, was developed to promote change in physical activity [19].”
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions (2017, background): “…increasing popularity and have been used to support health behavior change in a variety of domains including physical activity (King et al. (2013); Consolvo et al. (2008)), eating disorders (Bauer et al. (2010)), drug abuse (Scott and Dennis (2009)), alcohol use (Witkiewitz et al. (2014);…”