Sharing real-time biometric data across social networks

Curmi, Franco; Ferrario, Maria Angela; Whittle, Jon

doi:10.1145/2598510.2598515

Cited by 21 publications

(27 citation statements)

References 17 publications

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“…Since this work was a first deployment of its kind in a research setting that includes very personal data sharing such as heart rate, we felt that it would be appropriate to use anonymity in this case. This decision was also supported by earlier interviews conducted with experts in this area [10]. Although future deployments of the system will not anonymise athletes for reasons specified earlier, the anonymisation of athletes in this context had research benefits, namely, that the cheering decisions (as perceived by the athletes) were based on athletic performance.…”

Section: Future Workmentioning

confidence: 83%

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Crowdsourcing Synchronous Spectator Support

Curmi

Ferrario

Whittle

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Many studies have shown that crowd-support, such as cheering during sport events, can have a positive impact on athletes' performance. However, up until recently this support was only possible if the supporters and the athletes were geographically co-located. Can cheering be done remotely and would this be effective? In this paper we investigate the effect and possibilities of live remote cheering on co-located athletes and online supporting crowds that have a weak social tie and no social tie with the athlete. We recruit 140 online spectators and 5 athletes for an ad-hoc 5km road race. Results indicate that crowds socially closer to the athletes are significantly more engaged in the support. The athletes were excited by live remote cheering from friendsourced spectators and cheering from unknown crowdsourced participants indicating that remote friends and outsourced spectators could be an important source of support.

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Section: Future Workmentioning

confidence: 83%

“…The data broadcast system was implemented using BioShare [10]. BioShare is an open source application that was designed for broadcasting data during day-to-day activities through a smartphone app and a web portal for visualizing the broadcasted data in real-time.…”

Section: Data Sharing Infrastructurementioning

confidence: 99%

Crowdsourcing Synchronous Spectator Support

Curmi

Ferrario

Whittle

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many systems have been built to examine physical activity [11,35,38,56], bioinformatics data such as heart rate and ECG data [15,16,40,49], location [6,27,54], ecologically friendly activities [22,37], and music [20,50]. These domains are tracked for a variety of reasons, including to make healthier decisions or track health factors [8,38,41,55] or simply to know thyself [19,32,33].…”

Section: Data Domainmentioning

confidence: 99%

From "nobody cares" to "way to go!"

Epstein

Jacobson

Bales

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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Many research applications and popular commercial applications include features for sharing personally collected data with others in social awareness streams. Prior work has identified several barriers to use as well as discrepancies between designer goals and how these features are used in practice. We develop a framework for designing and evaluating these features based on an extensive review of prior literature. We demonstrate the value of this framework by analyzing physical activity sharing on Twitter, coding 4,771 tweets and their responses and gathering 444 reactions from 97 potential tweet recipients, learning that specific user-generated content leads to more responses and is better received by the post audience. We conclude by extending our findings to other sharing problems and discussing the value of our design framework.

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“…Commercial accelerometer driven products like the fitbit or the Nike+ FuelBand do not provide access to the raw data collected by the device [3]. The missing API of commercial devices and the need for additional data like the orientation of the sensor required to build a custom prototype.…”

Section: Climbing Sensor Hardwarementioning

confidence: 99%

ClimbSense

Kosmalla

Daiber

Krüger

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Today, sports and activity trackers are ubiquitous. Especially runners and cyclists have a variety of possibilities to record and analyze their workouts. In contrast, climbing did not find much attention in consumer electronics and human-computer interaction. If quantified data similar to cycling or running data were available for climbing, several applications would be possible, ranging from simple training diaries to virtual coaches or usage analytics for gym operators.This paper introduces a system that automatically recognizes climbed routes using wrist-worn inertia measurement units (IMUs). This is achieved by extracting features of a recorded ascent and use them as training data for the recognition system. To verify the recognition system, cross-validation methods were applied to a set of ascent recordings that were assessed during a user study with eight climbers in a local climbing gym. The evaluation resulted in a high recognition rate, thus proving that our approach is possible and operational.

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Sharing real-time biometric data across social networks

Cited by 21 publications

References 17 publications

Crowdsourcing Synchronous Spectator Support

Crowdsourcing Synchronous Spectator Support

From "nobody cares" to "way to go!"

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