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An Explorative Evaluation of using Smartwatches to track athletes in marathon events
1  Institute of Pervasive Computing, Johannes Kepler University JKU
Academic Editor: Jean-marc Laheurte

https://doi.org/10.3390/ECSA-12-26553 (registering DOI)
Abstract:

Accurate and continuous tracking of athletes is essential to meet the infotainment demands and health and safety requirements of major marathon events. However, the current ability to track individual athletes or groups at mass sporting events is severely limited by the weight, size and cost of the equipment required. In marathons, Radio Frequency Identification (RFID) technology is typically used for timing, but can only provide accurate tracking at widely spaced intervals, relying on heuristic and interpolation algorithms to estimate runners’ positions between measurement points. Alternative IOT solutions, such as Low Power Wide Area Network (LWPAN), have limitations in terms of range and require dedicated infrastructure and regulation. Instead, we analysed the potential use of smartwatches as accurate and continuous tracking devices for athletes, assessing battery consumption during tracking and standby drain, achievable GPS tracking accuracy and the update rate of data transfer from the device in urban environments. The 4G LTE battery drain is different from non-urban areas. Analysis of standby usage is necessary as devices need to conserve power for tracking. We programmed an application that allowed us to control the modalities of acquisition and transmission intervals, integrating advanced logging and statistics at runtime, and evaluated the achievable results in major marathon events. Our empirical evaluation at the Frankfurt, Athens and Vienna marathons with three different types of smartwatch tracking platforms showed the validity of this approach, while respecting some necessary limitations of the tracking settings. Median battery drain was 5.3%/hr in standby before race start (σ 1.5) and 16.5%/hr in tracking mode (σ 3.29), with an actual update rate varying between 19-57s on Wear OS devices. The average GPS offset to the track was 4.5 m (σ 8.7). Future work will focus on integrating these consumer devices with existing time and tracking infrastructure.

Keywords: Event Monitoring; Athlete Tracking; Smartwatch Energy Consumption

 
 
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