**6.1 Waveform of the FBG sensor signal**

In this section, the blood pressure is calculated from the waveform of the FBG sensor signal. As shown in Section 3, the FBG sensor signal is measured representing the pressure of the blood flow that causes a change in the diameter of the blood vessel. Pulsation is a distortion that causes an arterial distortion on the skin surface. Therefore, information on blood pressure is considered to be present in the FBG sensor signal from which a distortion is measured.

A signal measured with a general photoelectric pulse wave sensor is a volume pulse wave signal indicating the volume of blood. A signal obtained by second derivative of the volume pulse wave signal is an acceleration plethysmogram. The basic shape of acceleration plethysmogram includes five peaks [18]. The A-wave to the E-wave are called initial systolic positive wave, initial contraction negative wave, mid-systole re-elevation wave, post-contraction descent wave, and expansion initial positive wave, respectively. Therefore, an acceleration pulse wave contains information on systole and diastole of the heart. The first derivative signal of the

**51**

*Vital Sign Measurement Using FBG Sensor for New Wearable Sensor Development*

**6.2 Calculation method of blood pressure from the FBG sensor signal**

When blood pressure was calculated from FBG sensor signal, PLS regression analysis, which is a widely known multivariate analysis among others, was used. The PLS regression analysis can construct calibration curves from the explanatory and objective variables. At this time, it is a feature to construct a calibration curve on the premise that an explanatory variable and an objective variable contain errors. The explanatory variable is the FBG sensor signal waveform, and the objective variable is the blood pressure measured simultaneously by the electronic sphygmomanometer. The FBG sensor signal is processed through the following steps [19].

1.The FBG sensor signal is processed with a band pass filter of 0.5–5 Hz.

3.The signal is divided for each detected peak.

rate fluctuations caused by respiratory sinus arrhythmia.

**6.3 Experimental result of calculating the blood pressure**

4.The divided signals are averaged.

2.The 'A' peak ('A' wave) is detected from the signal waveform of the band pass

5.At the vertical axis of the averaged signal, the first point (peak of 'A' wave) is normalized to "1", and the lowest point (peak of 'B' wave) is set to "0".

6.The horizontal axis of all FBG sensor signal waveforms processed up to the 5th term is cut out in the shortest time, and the length of the horizontal axis is unified.

The step 1 of the signal processing a range for covering a signal with a pulse rate of 30–300 times/min. In step 4 of the signal processing, considering that the measurement time of the electronic sphygmomanometer is ~30 s, the average is calculated for the pulse wave signals measured within that time. The step 5 of signal processing is to cancel the vertical axis fluctuations caused by pressure while installing the FBG sensor in humans. The step 6 of signal processing is to cancel the pulse

The FBG sensor signal processed through the aforementioned signal processing steps is used as an explanatory variable, the blood pressure value of the electronic sphygmomanometer measured simultaneously is used as a target variable, and a calibration curve is constructed by PLS regression analysis. The newly measured FBG sensor signal is substituted into this calibration curve to calculate the blood pressure.

This experiment was performed on three subjects. A schematic of the experimental blood pressure measurement is shown in **Figure 9** [19]. The posture of the subject was supine, and the FBG sensor was installed at the pulsation point of the radial artery of the right wrist. The reference blood pressure value (objective

FBG sensor is similar to the acceleration pulse wave in shape [13]. Since an FBG sensor signal indicates the displacement of the Bragg wavelength due to strain, time change of the volume pulse wave is measured. Therefore, the first derivative signal of the FBG sensor is similar to the second derivative signal of the volume pulse wave signal. Furthermore, since the first derivative waveform of an FBG sensor signal includes information on the systole and diastole of the heart, the blood pressure can

*DOI: http://dx.doi.org/10.5772/intechopen.84186*

be calculated from this waveform.

filter.

*Vital Sign Measurement Using FBG Sensor for New Wearable Sensor Development DOI: http://dx.doi.org/10.5772/intechopen.84186*

FBG sensor is similar to the acceleration pulse wave in shape [13]. Since an FBG sensor signal indicates the displacement of the Bragg wavelength due to strain, time change of the volume pulse wave is measured. Therefore, the first derivative signal of the FBG sensor is similar to the second derivative signal of the volume pulse wave signal. Furthermore, since the first derivative waveform of an FBG sensor signal includes information on the systole and diastole of the heart, the blood pressure can be calculated from this waveform.


**Author details**

Romel E. Guzmán-Alvarez1 \* and José G. Márquez-Ramos2

1 Institute of Food Science and Technology, Science Faculty, Central University of Venezuela, Caracas, Venezuela

2 School of Nutrition and Dietetic, Medicine Faculty, University Central of Venezuelan, Caracas, Venezuela

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**5. Discussion**

This study focuses on salinity as a single quality measure of irrigation water. However, in water-scarce areas, TWW has become a significant water source that renders salinity but one of many water quality measures. Compared to freshwater irrigation, the reuse of TWW in agricultural applications can harm agricultural production [39], degrade output qualities [40], and threaten the environment [41]. In response, TWW irrigation incentivizes stricter TWW quality standards [42] and attracts the development of new agricultural production technologies [43] and wastewater treatment methods [44]. Moreover, the supply of TWW is more stable than that of natural freshwater [45], and TWW contains nutritional elements that can partially replace fertilizers [46]. These processes have the potential to alter the use of irrigation water sources as well as the damage caused by salinity. To comprehend this, suppose that new regulations impose strict micropollutant standards that can only be met by the desalination of a large fraction of the generated TWW; in this case, the damage caused by salinity would become smaller and less sensitive to salinity changes. This implies that future economic studies of agricultural and water management should account for the interrelations across multiple water-quality measures.

**Supplementary Materials:** All the data used in this research have been incorporated into the MYWAS-VALUE model, which is available at: https://zenodo.org/record/3702053#.Xx1jpSgzZPZ (accessed on 3 January 2022).

**Author Contributions:** Conceptualization, Y.S., A.R., I.F. and I.K.; data curation, Y.S. and A.R.; funding acquisition, I.F. and I.K.; investigation, Y.S. and I.K.; methodology, Y.S., A.R., I.F. and I.K.; project administration, I.K.; software, Y.S. and A.R.; writing—original draft, Y.S.; writing—review and editing, A.R., I.F. and I.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was partially funded by the Israeli Ministry of Agriculture and Rural Development (grant 12-09-0017), the Israeli Water Authority (grant<PHONE_NUMBER>), the Israeli Ministry of Energy (grant 222-99-271), and the Center for Agricultural Economics Research at the Hebrew University of Jerusalem.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank the Editor and two anonymous reviewers for valuable comments. Thanks go to Yael Kachel from the Israeli Ministry of Agriculture and Rural Development for the data provisions.

**Conflicts of Interest:** The authors declare no conflict of interest.


*Article* **Hip Fractures and Visual Impairment: Is There a Cause–Consequence Mechanism?**

**Gianluca Testa 1,\*, Sara De Salvo 1, Silvia Boscaglia 2, Marco Montemagno 1, Antonio Longo 2, Andrea Russo 2, Giuseppe Sessa 1 and Vito Pavone 1**

**Abstract:** Background: Numerous studies have pointed out how visual impairment relates to falls in the elderly, causing dangerous consequences, such as fractures. The proximal femur fracture is one of the most frequent fracture types related to poor vision. This study investigates the link between fall-related hip fractures and visual impairment. Methods: The present is an observational monocentric case–control study. We collected the ophthalmologic anamnesis and measured the visual acuity of 88 subjects with femur neck fracture (case group), comparing it with 101 adults without fractures and a recent fall history. Results: The results showed no statistical difference between the two groups regarding visual acuity, with a *p*-value of 0.08 for the right eye and 0.13 for the left one. One of the major ophthalmologic morbidities found was cataracts, present in 48% of the control group and 30% of the case group. Conclusions: The data obtained suggest that visual impairment might not be crucial in determining falls in the elderly.

**Keywords:** aged; cataracts; elderly; falls; femur fractures; hip fractures; sight defects; vision; visual acuity; visual impairment


**Extended Dependence of the Hydrological Regime on the Land Cover Change in the Three-North Region of China: An Evaluation under Future Climate Conditions**

**Yi Yao 1,2, Xianhong Xie 1,2,\*, Shanshan Meng 1,2, Bowen Zhu 1,2, Kang Zhang 1,2 and Yibing Wang 1,2**

Received: 27 November 2018; Accepted: 28 December 2018; Published: 4 January 2019

**Abstract:** The hydrological regime in arid and semi-arid regions is quite sensitive to climate and land cover changes (LCC). The Three-North region (TNR) in China experiences diverse climate conditions, from arid to humid zones. In this region, substantial LCC has occurred over the past decades due to ecological restoration programs and urban expansion. At a regional scale, the hydrological effects of LCC have been demonstrated to be less observable than the effects of climate change, but it is unclear whether or not the effects of LCC may be intensified by future climate conditions. In this study, we employed remote sensing datasets and a macro-scale hydrological modeling to identify the dependence of the future hydrological regime of the TNR on past LCC. The hydrological effects over the period from 2020–2099 were evaluated based on a Representative Concentration Pathway climate scenario. The results indicated that the forest area increased in the northwest (11,691 km2) and the north (69 km2) of China but declined in the northeast (30,042 km2) over the past three decades. Moreover, the urban area has expanded by 1.3% in the TNR. Under the future climate condition, the hydrological regime will be influenced significantly by LCC. Those changes from 1986 to 2015 may alter the future hydrological cycle mainly by promoting runoff (3.24 mm/year) and decreasing evapotranspiration (3.23 mm/year) over the whole region. The spatial distribution of the effects may be extremely uneven: the effects in humid areas would be stronger than those in other areas. Besides, with rising temperatures and precipitation from 2020 to 2099, the LCC may heighten the risk of dryland expansion and flooding more than climate change alone. Despite uncertainties in the datasets and methods, the regional-scale hydrological model provides new insights into the extended impacts of ecological restoration and urbanization on the hydrological regime of the TNR.

**Keywords:** hydrological cycle; Three-North region; climate change; land cover change; Variable Infiltration Capacity (VIC) model; evapotranspiration; runoff; soil moisture


*2.1.5. Industrial horticultural wastes*

There are lots of food processing industries. These industries use some hazardous materials for food processing such as coloring agents, dyes, by-products such as banana peels, coconut husks, and other extraneous bioactive compounds phenols, flavonoids, flavanols, anthocyanins always evolved in the processing industries as by-products (**Table 2**). Heavy and rapid disposal is required for this kind of wastes. If it is not disposed with the time, then it will appear as a biggest threat for human survival and environmental balance. Higher emissions of pollutant make the environment more vulnerable. So, without utilizing these types of compounds it can be delectable enough to make our environment polluted.


**3.5.1 Crosses between** *D. mulleri* **and** *D. mojavensis*

The cross between *D. mulleri* and *D. mojavensis* showed asymmetric isolation, with many descendents only in the direction of *D. mulleri* females and *D. mojavensis* males. The reciprocal cross did not produce offspring despite the presence of courtship among couples and eggs in the plate. The hybrid larvae were analyzed in 10% PAGE and showed threeband patterns for EST-4 and EST-5 (Figure 4). For both enzymes, the slower band corresponded to the enzyme encoded by *D. mulleri* and the faster to the enzyme encoded by *D. mojavensis*. The intermediate band represented a hybrid enzyme, indicating that EST-4 and EST-5 are dimeric in both species.

For EST-4, the hybrid intermediate bands were located closer to the band encoded by *D. mulleri*, which could be a result of differences in the I.P. values of theses enzymes (Table 3). The same was not observed for EST-5, as this enzyme has nearly the same value for both of these species.