Petrella Davide, Ellenberger Lynn, Gilgien Matthias
Department of Movement Sciences, KU Leuven, Leuven, Belgium.
Swiss Council for Accident Prevention BFU, Bern, Switzerland.
PLoS One. 2025 Aug 13;20(8):e0327896. doi: 10.1371/journal.pone.0327896. eCollection 2025.
Smartphones with integrated global navigation satellite system (GNSS) functionality are increasingly used in various apps beyond communication, including positioning, navigation, and tracking. This study explores the potential of smartphone GNSS data to improve ski slope safety through motion data analysis. Apps such as iSKI, Skitude, Slopes, and Strava measure speeds, distances, and altitude differences, generating valuable data on skiers' movements. These data help ski resorts in planning and accident prevention by identifying high-risk areas based on movement patterns. We compared the accuracy of position and speed data from four apps across four smartphone models (two Android and two iOS) against a differential GNSS (dGNSS) reference system. Data were collected at two ski resorts during the winter of 2022/23, with smartphones recording at 1 Hz and dGNSS at 50 Hz. Analysis focused on downhill runs, excluding initial recording phases and vertical position data. Accuracy was assessed by calculating the Euclidean distance between the time-synchronized smartphone data and dGNSS reference data. High-end smartphones provided more accurate position data, with an average error of approximately 4 m, compared to 6 m for low-end models. Speed data were reliable across all devices, with an average error <1.9 km/h. However, accuracy diminished with increasing speeds and varied based on location-specific environmental factors. Thus, although smartphone position data can evaluate non-exact position-dependent parameters, such as slope utilization and user density, more precise systems, such as dGNSS, are necessary for exact position-dependent evaluations. Speed data derived from cleaned position data are reliable for estimating skier speeds, and data from different apps can be combined if consistent calculation methods are used. Future advances in smartphone technology are expected to enhance data accuracy. Recommendations include using smartphone data in open terrain for better accuracy and exercising caution when interpreting absolute position data for accident prevention or other context-specific analyses.
具备集成全球导航卫星系统（GNSS）功能的智能手机越来越多地应用于通信以外的各种应用程序，包括定位、导航和跟踪。本研究通过运动数据分析探索智能手机GNSS数据在提高滑雪坡安全性方面的潜力。诸如iSKI、Skitude、Slopes和Strava等应用程序可测量速度、距离和海拔差异，生成有关滑雪者运动的有价值数据。这些数据有助于滑雪场通过根据运动模式识别高风险区域来进行规划和事故预防。我们将四款智能手机型号（两款安卓和两款iOS）上四款应用程序的位置和速度数据的准确性与差分GNSS（dGNSS）参考系统进行了比较。在2022/23年冬季，在两个滑雪场收集了数据，智能手机以1Hz的频率记录，dGNSS以50Hz的频率记录。分析集中在下坡滑行，不包括初始记录阶段和垂直位置数据。通过计算时间同步的智能手机数据与dGNSS参考数据之间的欧几里得距离来评估准确性。高端智能手机提供的位置数据更准确，平均误差约为4米，而低端型号为6米。所有设备的速度数据都很可靠，平均误差<1.9公里/小时。然而，准确性随着速度的增加而降低，并因特定位置的环境因素而有所不同。因此，尽管智能手机位置数据可以评估非精确位置相关参数，如坡度利用率和用户密度，但对于精确的位置相关评估，需要更精确的系统，如dGNSS。从清理后的位置数据得出的速度数据对于估计滑雪者速度是可靠的，如果使用一致的计算方法，不同应用程序的数据可以合并。预计智能手机技术的未来进步将提高数据准确性。建议包括在开阔地形中使用智能手机数据以获得更高的准确性，以及在解释用于事故预防或其他特定上下文分析的绝对位置数据时要谨慎。
