Author Archives: Cecilia Bergman

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When heart rate becomes the goal of practice. Will professional sports lead the way in self – tracking?

Even the City bikes in Helsinki give you a report of the trip, which is also added to your personal use history along with the pick-up and return stations.

Even the City bikes in Helsinki give you a report of the trip, which is also added to your personal use history along with the pick-up and return stations.

Monitoring our performance with wearable devices has become popular despite the lack of scientific evidence on its benefits to our health. GPS tracking, for instance, is no longer an activity of goal-oriented athletes alone as anyone from a casual paddler to a committed bicycle commuter can easily record routes with a sports watch or smartphone application. Provided that the privacy of individuals is guaranteed, prospects for re-using the data for societal good are plentiful. Many of us would find novel value-added services enabling for example smarter route planning [1] or optimization of a city’s cycling network and its maintenance [2] very welcome.

As part of my doctoral work, I aim to understand the representativeness of crowd-sourced movement data. In terms of cycling, activity tracking data is typically expected to be biased towards faster recreational riders, which has raised questions regarding its value in the urban planning context. Other biases may not be as obvious, but can still be at least as problematic. One example is participation inequality by which we refer to the uneven distribution of recorded tracks between users. This is a typical characteristic of online communities based on volunteered collaborations, e.g. Wikipedia, OpenStreetMap, in which a small active minority of users is responsible of a large share of the contributions.

Our findings suggest that individual cyclists, particularly commuters who are repeatedly tracking their route between home and work, may have a greater, potentially biasing, effect on the spatial and temporal cycling patterns derived from public mobile sports tracking application data than mass events and other group journeys [2,3]. Regarding privacy, biases induced by individual-level phenomena brings us to an important decision: even though from privacy perspective it would seem justifiable to remove all user identifiers from the data, in terms of data usability, it is necessary to be able to associate tracks belonging to the same user via pseudo-IDs [2,3]. Whether this is possible or not, will affect routing [1] and other analyses, and ultimately determine the value of the data in building sustainable smart cities.

It is likely that privacy and data ownership issues will only proliferate in the future once biometric tracking devices move into the consumer sector and are plugged in for data fusion. In professional sports, wearables, and specifically biometric trackers, are already a highly controversial topic. The battle is tough for example in the NBA, where Lauri Markkanen, who played himself into the hearts of all Finns in Eurobasket 2017, has impressed also the Chicago Bulls fans.

The advantages of biometric data may seem so obvious, considering their benefits in recovery and avoiding injuries, that we forget the potential – not necessarily so desirable – effects in the longer term. This is where the players’ union has stepped in, reminding that such data can also be used against the player. In a recent article in the Atlantic – “The Upcoming Privacy Battle Over Wearables in the NBA” – Jeremy Venook described the current agreement [4]:

[…] use in practice is strictly voluntary […]. Any team requesting a player wear one must explain, in writing, what’s being tracked and how the team will use the information […] Most importantly, the agreement says that ‘data collected from a Wearable worn at the request of a team may be used for player health and performance purposes and Team on-court tactical and strategic purposes only. The data may not be considered, used, discussed or referenced for any other purpose such as in negotiations regarding a future Player Contract or other Player Contract transaction,’ under penalty of a $250,000 fine.”

Obviously, collecting data for one purpose and later using it for some complete other context may be problematic. In the context of professional sports this could have serious economic consequences for the athletes. Although data collection is voluntary, it is rare (or impossible) for any player to refuse it. Therefore it is important that everything be transparent and controlled by the player, Venook explains. In reality, the ownership of biometric data is currently very fuzzy [5], and the lack of transparency and trust can lead to cheating from the player’s side, for example by faking explosive movement or tying the sleep monitor around a pillow [6].

How can we increase the usability of movement tracking data in a privacy-respective manner in order to build smarter and more sustainable cities? How will the battles in professional sports affect future decisions on the use of biometric tracking regarding consumer-level devices? Can the NBA’s players’ union become “a radical potential of the big data era”? [7]

In my opinion, these worlds are tightly connected. After all, we are all humans. Yes, even Lauri Markkanen (2.13 m, born in Vantaa).
References

[1] Bergman, C., & Oksanen, J. (2016). Conflation of OpenStreetMap and Mobile Sports Tracking Data for Automatic Bicycle Routing. Transactions in GIS, 20(6), 848-868. Available at: https://doi.org/10.1111/tgis.12192

[2] Oksanen, J., Bergman, C., Sainio, J., & Westerholm, J. (2015). Methods for deriving and calibrating privacy-preserving heat maps from mobile sports tracking application data. Journal of Transport Geography, 48, 135–144. Available at: https://doi.org/10.1016/j.jtrangeo.2015.09.001

[3] Bergman C., & Oksanen J. (2016). Estimating the Biasing Effect of Behavioural Patterns on Mobile Fitness App Data by Density-Based Clustering. In Sarjakoski T, Santos MY, Sarjakoski LT (Eds.). Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography, Springer International Publishing, pp. 199–218. Available at: https://www.researchgate.net/publication/303182768_Estimating_the_Biasing_Effect_of_Behavioural_Patterns_on_Mobile_Fitness_App_Data_by_Density-Based_Clustering

[4] Venook, J. (2017). The Upcoming Privacy Battle Over Wearables in the NBA. The Atlantic. https://www.theatlantic.com/business/archive/2017/04/biometric-tracking-sports/522222/

[5] Karkazis, K., & Fishman, J. R. (2017). Tracking U.S. Professional Athletes: The Ethics of Biometric Technologies. The American Journal of Bioethics, 17(1), 45–60. https://doi.org/10.1080/15265161.2016.1251633

[6] LHN Presents: SXsports “1984 Meets Moneyball: Who Owns Player Data” https://vimeo.com/160000924

[7] Crawford, K. (2014). The Anxieties of Big Data. The New Inquiry. https://thenewinquiry.com/the-anxieties-of-big-data/

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