No abstract

The purpose of the present work was to compare daily variations of heart rate variability (HRV) parameters between controlled breathing (CB) and spontaneous breathing (SB) sessions during a longitudinal follow-up of athletes. HRV measurements were performed daily on 10 healthy male runners for 21 consecutive days. The signals were recorded during two successive randomised 5-minutes sessions. One session was performed in CB and the other in SB. The results showed significant differences between the two respiration methods in the temporal, nonlinear and frequency domains. However, significant correlations were observed between CB and SB (higher than 0.70 for RMSSD and SD1), demonstrating that during a longitudinal follow-up, these markers provide the same HRV variations regardless of breathing pattern. By contrast, independent day-to-day variations were observed with HF and LF/HF frequency markers, indicating no significant relationship between SB and CB data over time. Therefore, we consider that SB and CB may be used for HRV longitudinal follow-ups only for temporal and nonlinear markers. Indeed, the same daily increases and decreases were observed whatever the breathing method employed. Conversely, frequency markers did not provide the same variations between SB and CB and we propose that these indicators are not reliable enough to be used for day-to-day HRV monitoring.

Recent laboratory studies have suggested that heart rate variability (HRV) may be an appropriate criterion for training load (TL) quantification. The aim of this study was to validate a novel HRV index that may be used to assess TL in field conditions. Eleven well-trained long-distance male runners performed four exercises of different duration and intensity. TL was evaluated using Foster and Banister methods. In addition, HRV measurements were performed 5 minutes before exercise and 5 and 30 minutes after exercise. We calculated HRV index (TLHRV) based on the ratio between HRV decrease during exercise and HRV increase during recovery. HRV decrease during exercise was strongly correlated with exercise intensity (R = -0.70; p < 0.01) but not with exercise duration or training volume. TLHRV index was correlated with Foster (R = 0.61; p = 0.01) and Banister (R = 0.57; p = 0.01) methods. This study confirms that HRV changes during exercise and recovery phase are affected by both intensity and physiological impact of the exercise. Since the TLHRV formula takes into account the disturbance and the return to homeostatic balance induced by exercise, this new method provides an objective and rational TL index. However, some simplification of the protocol measurement could be envisaged for field use.

Rapid force production is critical to improve performance and prevent injuries. However, changes in rate of force/torque development caused by the repetition of maximal contractions have received little attention. The aim of this study was to determine the relative influence of rate of torque development (RTD) and peak torque (Tpeak) on the overall performance (i.e. mean torque, Tmean) decrease during repeated maximal contractions and to investigate the contribution of contractile and neural mechanisms to the alteration of the various mechanical variables. Eleven well-trained men performed 20 sets of 6-s isokinetic maximal knee extensions at 240°·s-1, beginning every 30 seconds. RTD, Tpeak and Tmean as well as the Rate of EMG Rise (RER), peak EMG (EMGpeak) and mean EMG (EMGmean) of the vastus lateralis were monitored for each contraction. A wavelet transform was also performed on raw EMG signal for instant mean frequency (ifmean) calculation. A neuromuscular testing procedure was carried out before and immediately after the fatiguing protocol including evoked RTD (eRTD) and maximal evoked torque (eTpeak) induced by high frequency doublet (100 Hz). Tmean decrease was correlated to RTD and Tpeak decrease (R²=0.62; p<0.001; respectively β=0.62 and β=0.19). RER, eRTD and initial ifmean (0-225 ms) decreased after 20 sets (respectively -21.1±14.1, -25±13%, and ~20%). RTD decrease was correlated to RER decrease (R²=0.36; p<0.05). The eTpeak decreased significantly after 20 sets (24±5%; p<0.05) contrary to EMGpeak (-3.2±19.5 %; p=0.71). Our results show that reductions of RTD explained part of the alterations of the overall performance during repeated moderate velocity maximal exercise. The reductions of RTD were associated to an impairment of the ability of the central nervous system to maximally activate the muscle in the first milliseconds of the contraction.

The purpose of this study was to measure the influence of breathing frequency (BF) on heart rate variability (HRV) and specifically on the Low Frequency/High Frequency (LF/HF) ratio in athletes. Fifteen male athletes were subjected to HRV measurements under six randomised breathing conditions: spontaneous breathing frequency (SBF) and five others at controlled breathing frequencies (CBF) (0.20; 0.175; 0.15; 0.125 and 0.10 Hz). The subjects were divided in two groups: the first group included athletes with SBF <0.15 Hz (infSBF) and the second athletes with SBF higher than 0.15 Hz (supSBF). Fatigue and training load were evaluated using a validated questionnaire. There was no difference between the two groups for the fatigue questionnaire and training load. However, the LF/HF ratio during SBF was higher in infSBF than in supSBF (6.82 ± 4.55 vs. 0.72 ± 0.52; p<0.001). The SBF and LF/HF ratio were significantly correlated (R=-0.69; p=0.004). For the five CBF, no differences were found between groups; however, LF/HF ratios were very significantly different between sessions at 0.20; 0.175; 0.15 Hz and 0.125; 0.10 Hz. In this study, BF was the main modulator of the LF/HF ratio in both controlled breathing and spontaneous breathing. Although, none of the subjects of the infSBF group were overtrained, during SBF they all presented LF/HF ratios higher than four commonly interpreted as an overtraining syndrome. During each CBF, all athletes presented spectral energy mainly concentrated around their BF. Consequently, spectral energy was located either in LF or in HF band. These results demonstrate that the LF/HF ratio is unreliable for studying athletes presenting SBF close to 0.15 Hz leading to misclassification in fatigue.

The registration of a preoperative 3D model, reconstructed for example from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation: they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the nontrivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery. Methods. Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end-to-end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector. Results. Evaluation shows that the proposed detector has a similar false negative rate to existing methods but substantially reduces both false positive rate and response thickness. Finally, we ran a user-study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 minutes and 53 seconds in surgeon time without sacrificing registration accuracy. Conclusions. We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state of the art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible.

The proliferation of smartphones is creating new opportunities to monitor and interact with human subjects in free-living conditions since smartphones are familiar to large segments of the population and facilitate data collection, transmission and analysis. From accelerometry data collected by smartphones, the present work aims to estimate time spent in different activity categories and the energy expenditure in free-living conditions. Our research encompasses the definition of an energy-saving function (Pred) considering four physical categories of activities (still, light, moderate and vigorous), their duration and metabolic cost (MET). To create an efficient discrimination function, the method consists of classifying accelerometry-transformed signals into categories and of associating each category with corresponding Metabolic Equivalent Tasks. The performance of the Pred function was compared with two previously published functions (f(η,d)aedes,f(η,d)nrjsi), and with two dedicated sensors (Armband® and Actiheart®) in free-living conditions over a 12-h monitoring period using 30 volunteers. Compared to the two previous functions, Pred was the only one able to provide estimations of time spent in each activity category. In relative value, all the activity categories were evaluated similarly to those given by Armband®. Compared to Actiheart®, the function underestimated still activities by 10.1% and overestimated light- and moderate-intensity activities by 7.9% and 4.2%, respectively. The total energy expenditure error produced by Pred compared to Armband® was lower than those given by the two previous functions (5.7% vs. 14.1% and 17.0%). Pred provides the user with an accurate physical activity feedback which should help self-monitoring in free-living conditions.

Physical inactivity and sedentary behaviors are on the rise worldwide and contribute to the current overweight and obesity scourge. The loss of healthy life style benchmarks and the lack of the need to move make it necessary to provide feedback about physical and sedentary activities in order to promote active ways of life. The aim of this study was to develop a specific function adapted to overweight and obese people to identify four physical activity (PA) categories and to estimate the associated total energy expenditure (TEE). This function used accelerometry data collected from a smartphone to evaluate activity intensity and length, and TEE. The performance of the proposed function was estimated according to two references (Armband® and FitmatePro®) under controlled conditions (CC) for a 1.5-h scenario, and to the Armband® device in free-living conditions (FLC) over a 12-h monitoring period. The experiments were carried out with overweight and obese volunteers: 13 in CC and 27 in FLC. The evaluation differences in time spent in each category were lower than 7% in CC and 6% in FLC, in comparison to the Armband® and FitmatePro® references. The TEE mean gap in absolute value between the function and the two references was 9.3% and 11.5% in CC, and 8.5% according to Armband® in FLC.