The aim of the present study was to compare heart rate variability (HRV) at rest and during exercise using a temporal series obtained with the Polar S810i monitor and a signal from a LYNX ® signal conditioner (BIO EMG 1000 model) with a channel configured for the acquisition of ECG signals. Fifteen healthy subjects aged 20.9 ± 1.4 years were analyzed. The subjects remained at rest for 20 min and performed exercise for another 20 min with the workload selected to achieve 60% of submaximal heart rate. RR series were obtained for each individual with a Polar S810i instrument and with an ECG analyzed with a biological signal conditioner. The HRV indices (rMSSD, pNN50, LFnu, HFnu, and LF/HF) were calculated after signal processing and analysis. The unpaired Student t-test and intraclass correlation coefficient were used for data analysis. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV at rest and during exercise

The Polar® RS800G3™ rate monitor was released in the market to replace the Polar® S810i™, and few studies have assessed that the RR series obtained by this equipment is reliable for analysis of heart rate variability (HRV). We compared HRV indexes among the devices Polar® RS800G3™, Polar® S810i™ and eletrocardiogram (ECG) to know whether the series of Polar® RS800G3™ are as reliable as those devices already validated. We analysed data from 30 healthy young adults, male, with an average age of 20·66 ± 1·40 years, which had captured the heart rate beat to beat in the three devices simultaneously with spontaneously breathing, first in the supine position and subsequently sit both for 30 min. The obtained series of RR intervals was used to calculate the indexes of HRV in the time domain (SDNN and RMSSD) and in the frequency domain (LF, HF and LF/HF). There were no significant differences in HRV indexes calculated from series obtained by the three devices, regardless of the position analysed, and a high correlation coefficient was observed. The results suggest that the Polar® RS800G3™ is able to capture series of RR intervals for analysis of HRV indexes as reliable as those obtained by ECG and Polar® S810i™.

CRD42016047602.

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The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL 2 DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.