ObjectivesWearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts.MethodsMIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage.ResultsWearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness.ConclusionsWearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization.

The first aim of this study was to compare an ambulatory five-lead ECG system with the commercially available breast belt measuring devices; Polar S810i and Suunto t6, in terms of R-R interval measures and heart rate variability (HRV) indices. The second aim was to compare different HRV spectral analysis methods. Nineteen young males (aged between 22 and 31 years, median 24 years) underwent simultaneous R-R interval recordings with the three instruments during supine and sitting rest, moderate dynamic, and moderate to vigorous static exercise of the upper and lower limb. For each subject, 17 R-R interval series of 3-min length were extracted from the whole recordings and then analyzed in frequency domain using (1) a fast Fourier transform (FFT), (2) an autoregressive model (AR), (3) a Welch periodogram (WP) and (4) a continuous wavelet transform (CWT). Intra-class correlation coefficients (ICC) and Bland-Altman limits of agreement (LoA) method served as criteria for measurement agreement. Regarding the R-R interval recordings, ICC (lower ICC 95% confidence interval >0.99) as well as LoA (maximum LoA: -15.1 to 14.3 ms for ECG vs. Polar) showed an excellent agreement between all devices. Therefore, the three instruments may be used interchangeably in recording and interpolation of R-R intervals. ICCs for HRV frequency parameters were also high, but in most cases LoA analysis revealed unacceptable discrepancies between the instruments. The agreement among the different frequency transform methods can be taken for granted when analyzing the normalized power in low and high frequency ranges; however, not when analyzing the absolute values.

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Our results reveal that higher perceived stress in the operating room is associated with increased intraoperative HR and decreased HRV at night. Non-stressed surgeons show greater relaxation during sleep compared to their stressed colleagues.

Stoll R, Urban-Klein B, Roggendorf MJ, JablonskiMomeni A, Strauch K, Frankenberger R. Effectiveness of four electronic apex locators to determine distance from the apical foramen. International Endodontic Journal, 43, 808-817, 2010.Aim To evaluate the accuracy of four electronic apex locators (EAL) in the apical region (0-3 mm short of the foramen) and to compare the precision of the readings on the display with the real position of the file in the root canal. Methodology Twenty single-rooted extracted teeth with round root canals were used. The canal orifices were preflared, and the length to the major foramen was determined visually under a microscope. Canals were enlarged, so that a size 15 file fitted well inside the canal. Teeth were mounted in acrylic test tubes filled with physiologic saline solution. Electronic length was determined in the region between the major foramen and 3 mm short of it in 0.5 mm steps with the Dentaport ZX, Root ZX mini, Elements Diagnostic Unit and Apex Locator and Raypex 5 using files of size 10 and size 15. The data were analysed using linear regression between true length and EAL reading, Bland-Altman plots and nonparametric tests at a significance level of alpha = 0.05. ResultsThe major foramen was detected by all EALs. With a measurement file positioned directly at the major foramen, meter readings were equivalent to a position 0.01-0.38 mm away. For the Dentaport ZX, a better accuracy using the size 15 file for the area 0-1.5 mm short of the apex was found. The differences in measurements between the two files were smaller for the other EALs. In linear regression, a good linearity for Dentaport ZX and Root ZX mini and moderate linearity for Elements Diagnostic Unit and Apex Locator and Raypex 5 were found. The slope of the measurement curve was too low (0.37-0.57) for the Raypex 5 and almost optimal for the Dentaport ZX (1.01-1.05). The Root ZX mini and the Elements Obturation Unit produced lower slope values and especially the Elements Obturation Unit revealed much higher SDs at the different measurement levels. Conclusion Amongst the four EALs, the Dentaport ZX and Root ZX mini had the best agreement between true lengths and meter readings. For the Raypex 5, an interpretation of the colour-coded zones as distance to the foramen cannot be recommended and might lead to erroneous interpretations.