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Sport-related concussions (SRC) are characterized by impaired autonomic control. Heart rate variability (HRV) offers easily obtainable diagnostic approaches to SRC-associated dysautonomia, but studies investigating HRV during sleep, a crucial time for post-traumatic cerebral regeneration, are relatively sparse. The aim of this study was to assess nocturnal HRV in athletes during their return to sports (RTS) after SRC in their home environment using wireless wrist sensors (E4, Empatica, Milan, Italy) and to explore possible relations with clinical concussion-associated sleep symptoms. Eighteen SRC athletes wore a wrist sensor obtaining photoplethysmographic data at night during RTS as well as one night after full clinical recovery post RTS (>3 weeks). Nocturnal heart rate and parasympathetic activity of HRV (RMSSD) were calculated and compared using the Mann–Whitney U Test to values of eighteen; matched by sex, age, sport, and expertise, control athletes underwent the identical protocol. During RTS, nocturnal RMSSD of SRC athletes (Mdn = 77.74 ms) showed a trend compared to controls (Mdn = 95.68 ms, p = 0.021, r = −0.382, p adjusted using false discovery rate = 0.126) and positively correlated to “drowsiness” (r = 0.523, p = 0.023, p adjusted = 0.046). Post RTS, no differences in RMSSD between groups were detected. The presented findings in nocturnal cardiac parasympathetic activity during nights of RTS in SRC athletes might be a result of concussion, although its relation to recovery still needs to be elucidated. Utilization of wireless sensors and wearable technologies in home-based settings offer a possibility to obtain helpful objective data in the management of SRC.

In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player’s feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario.