{"id":30998,"date":"2024-08-01T13:21:45","date_gmt":"2024-08-01T13:21:45","guid":{"rendered":"https:\/\/soccerment.com\/?p=30998"},"modified":"2024-08-07T10:16:24","modified_gmt":"2024-08-07T10:16:24","slug":"improvements-on-gps-acceleration-accuracy","status":"publish","type":"post","link":"https:\/\/soccerment.com\/it\/improvements-on-gps-acceleration-accuracy\/","title":{"rendered":"Improvements on GPS Acceleration Accuracy"},"content":{"rendered":"

\ud83c\uddee\ud83c\uddf9 La versione italiana dell’articolo \u00e8 disponibile in fondo.<\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

At Soccerment, our commitment to enhancing sports technology continues to drive us toward greater precision in athlete performance tracking. Building on our previous work that significantly improved GPS speed accuracy<\/a>, we now turn our focus to refining the accuracy of acceleration metrics. This recent phase of research aimed to develop and filter acceleration metrics, comparing the results with traditional GPS vests to ensure precise data collection.<\/strong><\/p>\n\n\n\n

\n\n\n\n

Background and Motivation<\/strong><\/h4>\n\n\n\n

In our prior study, we explored the refinement of GPS speed calculations<\/a> using XSEED<\/a> smart shinguards compared to industry-standard GPS vests. Our findings highlighted the critical role of accurate speed measurements in developing other performance metrics, such as acceleration and deceleration. Given the success in enhancing speed accuracy, it became evident that similar improvements were necessary for acceleration metrics to provide a holistic view of player dynamics. Accurate acceleration data is essential for understanding explosive movements and for designing training programs tailored to individual needs.<\/p>\n\n\n\n

The development of accurate acceleration metrics is the final crucial component needed to develop comprehensive metabolic power and energy-related metrics. These metrics are among the most important for clubs to analyze player performance. This need was underscored in previous meetings with various clubs, including insights from Antonio Fierro, the performance coach of Club Milano<\/a>, who emphasized the importance of these metrics.<\/p>\n\n\n\n

The research in this field was initially focused on the total distance covered, assuming that this was related to the overall energy expenditure of the player. Later, the distance covered in different speed categories was addressed, with the assumption that higher speed correlated with greater intensity. However, this approach failed to account for the demands of changing speed, such as greater energy consumption for accelerations and greater muscular load for decelerations. Therefore, the validity and reliability of the acceleration data obtained from GPS has become crucial.<\/p>\n\n\n\n

Methodology <\/strong><\/h4>\n\n\n\n

To address the need for accurate acceleration metrics, we developed a series of advanced filtering techniques and metrics. Our research was conducted using two different settings: a controlled environment that mimicked actual football actions and real-world match conditions.<\/p>\n\n\n\n

We developed our acceleration signal from the filtered speed signal by preserving the peaks but smoothing out everything else, using a specifically designed algorithm proprietary to Soccerment, Milan, Italy. We then assessed the quality of the acceleration signal by comparing the metrics of acceleration (such as the number of accelerations and decelerations) defined by common thresholds (e.g., 2 m\/s\u00b2 to detect an acceleration or deceleration event) with those obtained by the GPS vest.<\/p>\n\n\n\n

Results<\/strong><\/h4>\n\n\n\n

Our chosen acceleration filtering technique demonstrated a high level of accuracy in detecting acceleration events. Specifically:<\/p>\n\n\n\n