Using a Live-In Laboratory for Ubiquitous Computing Research

Intille, Stephen; Larson, Kent; Tapia, Emmanuel Munguia; Beaudin, Jennifer; Kaushik, Pallavi; Nawyn, Jason; Rockinson, Randy

doi:10.1007/11748625_22

Cited by 265 publications

(147 citation statements)

References 8 publications

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“…A few of the more known datasets are: the PlaceLab dataset, focusing on ambient and object sensing [13]; Van Kasteren's dataset [14] with particularly long recordings (month-long) but with fewer sensors, and the Darmstadt routine dataset used for unsupervised activity pattern discovery [15], that is a long recording from body activity collected by the Porcupine system [16]. The TUM Kitchen data set was recorded for video-based activity recognition [17].…”

Section: B Datasets For Activity Recognitionmentioning

confidence: 99%

Collecting complex activity datasets in highly rich networked sensor environments

Roggen

Calatroni

Rossi

et al. 2010

2010 Seventh International Conference on Networked Sensing Systems (INSS)

587

359

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Abstract-We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.

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Section: B Datasets For Activity Recognitionmentioning

confidence: 99%

Collecting complex activity datasets in highly rich networked sensor environments

Roggen

Calatroni

Rossi

et al. 2010

2010 Seventh International Conference on Networked Sensing Systems (INSS)

587

359

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“…While the evaluation of interactive systems has a long tradition and a variety of available methodologies, for example, usability testing (Dumas and Redish 1994), experience sampling method (Consolvo and Walker 2003), and living laboratories (Intille et al 2006), some problems emerge when what is to be evaluated is a social application. According to Kim, Jeong, and Lee (2010), social applications are usually classified as social networking applications, that is, website or smartphone applications that allow users to stay connected with other people in online communities; and social media applications, that is, website or smartphone applications that allow people to create, upload, post, tag, comment, and share user-created content (UCC).…”

Section: Evaluating Social Applicationsmentioning

confidence: 99%

Using game mechanics for field evaluation of prototype social applications: a novel methodology

Rapp

Cena

Gena

et al. 2015

Behaviour & Information Technology

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This paper describes a novel methodology to evaluate a social media application in its formative phase of design. Taking advantage of the experiences developed in the Alternate Reality Games, we propose to insert game mechanics in the test setting of a formative evaluation of a prototypical social system. As a use case, we present the evaluation of WantEat, a prototypical social mobile application in the gastronomical domain. The evaluation highlighted how the gamification of a field trial can yield good results when evaluating social applications in prototypical status. From a methodological point of view, gamifying a field trial overcomes the cold start problem, caused by the absence of active communities, which can prevent the participation of users and therefore the collection of reliable data. Our experience showed that the gamification of a field evaluation is feasible and can likely increase the quantity of both browsing actions and social actions performed by users. Based on these results, we then are able to provide a set of guidelines to gamify the evaluation session of an interactive system.

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“…For example, the sociometer [Choudhury and Pentland 2003] is a wearable sensor package, which is used to monitor face-to-face interactions and social dynamics. PlaceLab [Larson and Intille ] is an example of a "living lab", where hundreds of sensors are built into objects and the home environment (as opposed to wearable sensors) for various research purposes including activity recognition [Intille et al 2006,Logan et al 2007,Tapia et al 2004. Similar work has been done in an office space in which hundreds of motion sensors were used to study the social interactions and behaviors of approximately 100 subjects [Wren et al 2007].…”

Section: Related Workmentioning

confidence: 99%

Discovering routines from large-scale human locations using probabilistic topic models

Farrahi

Gática-Pérez

2011

ACM Trans. Intell. Syst. Technol.

195

109

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In this work we discover the daily location-driven routines which are contained in a massive reallife human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16 month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include "going to work late", "going home early", "working non-stop" and "having no reception (phone off)" at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as "going to work early" or "turning off the mobile (or having no reception) in the evenings". We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.

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Using a Live-In Laboratory for Ubiquitous Computing Research

Cited by 265 publications

References 8 publications

Collecting complex activity datasets in highly rich networked sensor environments

Collecting complex activity datasets in highly rich networked sensor environments

Using game mechanics for field evaluation of prototype social applications: a novel methodology

Discovering routines from large-scale human locations using probabilistic topic models

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