Düking Peter, Hotho Andreas, Holmberg Hans-Christer, Fuss Franz Konstantin, Sperlich Billy
Integrative and Experimental Training Science, Department of Sports Science, Institute for Sport Sciences, Julius-Maximilians University Würzburg Würzburg, Germany.
Data Mining and Information Retrieval Group, Computer Science VI, Artificial Intelligence and Applied Computer Science, Julius-Maximilians University Würzburg Würzburg, Germany.
Front Physiol. 2016 Mar 9;7:71. doi: 10.3389/fphys.2016.00071. eCollection 2016.
Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.
运动员每天都会调整训练,以优化表现,并避免疲劳、过度训练以及对健康产生其他不良影响。为了优化训练负荷,每位运动员都必须考虑自身的客观和主观特征,并且越来越多的可穿戴技术(可穿戴设备)能方便地监测各种参数。因此,帮助运动员确定哪些参数最为重要,以及哪些可穿戴设备能最有效地监测这些参数就显得很重要。在此,我们讨论可用于非侵入性监测与运动员训练和健康相关的各种参数的可穿戴技术。基于这些考虑,我们给出未来的发展方向。此外,我们建议组合使用几种可穿戴设备,这样对于获取所有相关参数最为有效,对运动员的干扰最小,同时能优化表现并促进健康。