**4. Heart rate monitoring**

Among vital signals, a heart rate (HR) is an important index for understanding and diagnosing human's health condition. Especially, heart rate variability includes much information on health condition, for example, symptoms of cardiac disease, and conditions of autonomic nerve system (Kitney, 1980; Kobayashi, 1999). HR is measured in medical checkups and clinical diagnosis by electrocardiograph (ECG) as the gold standard. Besides the medical field, continuous monitoring of HRs during daily life activities is also strongly required because HR depends on activity intensity and monitoring HR might bring its information. Considering with usage in daily life, the monitoring should be realized without burden of human side. Regarding the burden, noninvasive, low intrusive, and unconscious sensing should be desired. In this section, an HR monitoring on bed by using an air pressure sensor (APS) is proposed for considering the unconscious and low cost biomedical sensing.

Because an APS is low cost and has high sensitivity, it could realize non invasive, non intrusive, and unconscious sensing on a bed with low cost. However, it brings too much information other than heart rate. The signal analysis such as filtering noise is required to realize its stable performance of HR estimation.

#### **4.1 Analysis domain**

There are two main analysis directions for measuring HRs from sensory signals; i.e., frequency domain analysis and time one. As frequency domain analysis, short term fast Fourier transformation (SFFT) is commonly used. SFFT is capable of monitoring global variability of target waveform. Because FFT assumes constant frequency, it does not extract microscopic variability. As time domain analysis, there have been several methods for HR measurement; i.e., peak detection, pattern matching, etc. Especially, pattern matching based on autocorrelation is commonly used to estimate HR variability from signals obtained via ECG monitor. Because of its capability of sensing HR variability, it is recommended for extraction and sensing of microscopic variability of HR. As HR monitoring in daily life requires the microscopic variability, time domain analysis is much more suitable for the usage.

#### **4.2 Causal analysis in biomedical sensing**

Regarding the transparency of biomedical sensing, causal analysis is a powerful tool since the causality is easy to be visualized, and makes the measurement principle clear.

There have been many practical studies on causal analysis. For instance, Thang et al. proposed a medical diagnosis support system based on oriental diagnosis knowledge (Thang, 2006). In their approach, the causality among some subject's symptoms and their diagnostic outcome is described by using RBF neural network. Nakajima et al. proposed a

Smart Health Management Technology 73

The basic idea of measuring HR monitoring is to extract heartbeats from pressure change of back in lying posture. The sensory signal superimposes not only heartbeat but also body movement and respiration. We need to extract the signal related to HR from the sensory source signal. In response to the requirement, causal analysis among air pressure and HR is

Firstly, the causality of heartbeat *HB*, body movement *BMV*, respiration *RSP*, and air pressure *APS* can be designed as the waveform analysis part as illustrated in Fig.11. Then,

variables Wave analysis Objective variables

1000

0

could be extracted from *HB* signal, HR variability could be calculated

which is the time differences of R waves in the same manner as ECG.

*R* 

extraction from pressure change, the pressure change involves not only heartbeat

but also respiration and body movement. Because of the nature of the signals, it could be

peak detection method. In this study, fuzzy logic is employed to formulate the knowledge

**Step 1.** Firstly, full-wave rectification is applied to *APS*, and the pre-processed signal is

processed signal *<sup>i</sup> x* . These fuzzy rules are described in the following.

 *RR* 

*HR*

by autocorrelation function and

is applied to the pre-

0 5 Time[sec]

Amplitude[a.u.]

No-Activity Activity

Time

**4.3.2 Heart rate estimation and causal analysis** 

Sensory

employed to analysis and design the extraction method.

*APS*

Waveform analysis

*RSP*

*a HB*

*BMV*

difficult to determine the precise position of R-waves *R*

Fig. 11. Causality of heart rate estimation by using an air pressure sensor

**Step 2.** Then, the fuzzy logic based on the knowledge about *RR*

Fig. 10. Principle of the measurement by an air pressure sensor

Amplitude[V]

once R wave points *R*

from R-R interval *RR*

As for *R*

about heartbeat.

determined as *<sup>i</sup> x* .

Pressure change

generic health management framework named Health Management Technology which is applied to not only human being but also manufacturing process, energy consumption management, and so forth (Nakajima, 2008a). Hata et al. suggested a concept named Human Health Care System of Systems which focus on health management, medical diagnosis, and surgical support (Hata, 2009). In the concept, the human health management technology is discussed from viewpoint of system of systems engineering. Marutschke et al. suggested that the causal analysis based on human-machine collaboration realizes transparent system model (Marutschke, 2009). From a viewpoint of theoretical development, lots of causal analysis theories have been proposed. Bayesian network describes statistical causality among phenomena observed from certain managed systems, and the statistical causality provides inference and reasoning functions (Pearl, 2001). Graphical model visualizes causality among components in complex systems (Miyagawa, 1991). Fuzzy logic helps intuitive representation of causality which is experts' implicit knowledge (Zadeh, 1996).

Through the discussions above, this section describes a transparent and accurate HR monitoring technology by employing an air pressure sensor and causal analysis among air pressure transit and HR.

### **4.3 Measurement principle and system architecture**

### **4.3.1 System design**

An HR monitoring equipment on a bed is implemented by using air pressure sensor (Hata, 2007). The equipment is not only capable of easy setup and application, but also unconscious and low intrusive. And the measurement principle is designed by employing causal analysis among air pressure and HR, and the cause-effect structure based on the designed causality is formed by using fuzzy logic (Zadeh, 1996; Tsuchiya, 2007; Tsuchiya, 2008).

Fig.9 shows the HR monitoring equipment. The human's body pressure is obtained via air pressure sensor, and the pressure is quantified into 1024 level (10bit) at 100 Hz by A/D converter. As a result, the HR transit is estimated from the quantified pressure. Fig.10 illustrates the principle of the measurement by using an air pressure sensor.

Fig. 9. Heart-rate monitoring equipment in sleep

generic health management framework named Health Management Technology which is applied to not only human being but also manufacturing process, energy consumption management, and so forth (Nakajima, 2008a). Hata et al. suggested a concept named Human Health Care System of Systems which focus on health management, medical diagnosis, and surgical support (Hata, 2009). In the concept, the human health management technology is discussed from viewpoint of system of systems engineering. Marutschke et al. suggested that the causal analysis based on human-machine collaboration realizes transparent system model (Marutschke, 2009). From a viewpoint of theoretical development, lots of causal analysis theories have been proposed. Bayesian network describes statistical causality among phenomena observed from certain managed systems, and the statistical causality provides inference and reasoning functions (Pearl, 2001). Graphical model visualizes causality among components in complex systems (Miyagawa, 1991). Fuzzy logic helps intuitive representation of causality which is experts'

Through the discussions above, this section describes a transparent and accurate HR monitoring technology by employing an air pressure sensor and causal analysis among air

An HR monitoring equipment on a bed is implemented by using air pressure sensor (Hata, 2007). The equipment is not only capable of easy setup and application, but also unconscious and low intrusive. And the measurement principle is designed by employing causal analysis among air pressure and HR, and the cause-effect structure based on the designed causality is formed by using fuzzy logic (Zadeh, 1996; Tsuchiya, 2007;

Fig.9 shows the HR monitoring equipment. The human's body pressure is obtained via air pressure sensor, and the pressure is quantified into 1024 level (10bit) at 100 Hz by A/D converter. As a result, the HR transit is estimated from the quantified pressure. Fig.10

**Control device**

**Personal Computer**

illustrates the principle of the measurement by using an air pressure sensor.

implicit knowledge (Zadeh, 1996).

**4.3 Measurement principle and system architecture** 

**Air pressure sensor**

Fig. 9. Heart-rate monitoring equipment in sleep

pressure transit and HR.

**4.3.1 System design** 

Tsuchiya, 2008).

Fig. 10. Principle of the measurement by an air pressure sensor

#### **4.3.2 Heart rate estimation and causal analysis**

The basic idea of measuring HR monitoring is to extract heartbeats from pressure change of back in lying posture. The sensory signal superimposes not only heartbeat but also body movement and respiration. We need to extract the signal related to HR from the sensory source signal. In response to the requirement, causal analysis among air pressure and HR is employed to analysis and design the extraction method.

Firstly, the causality of heartbeat *HB*, body movement *BMV*, respiration *RSP*, and air pressure *APS* can be designed as the waveform analysis part as illustrated in Fig.11. Then, once R wave points *R* could be extracted from *HB* signal, HR variability could be calculated from R-R interval *RR* which is the time differences of R waves in the same manner as ECG.

Fig. 11. Causality of heart rate estimation by using an air pressure sensor

As for *R* extraction from pressure change, the pressure change involves not only heartbeat but also respiration and body movement. Because of the nature of the signals, it could be difficult to determine the precise position of R-waves *R* by autocorrelation function and peak detection method. In this study, fuzzy logic is employed to formulate the knowledge about heartbeat.


Smart Health Management Technology 75

In this experiment, the developed HR monitoring is compared with conventional and typical method that is based on autocorrelation functions. Table 3 shows the profile of each subject, and their correlations between HR changes obtained from the ECG and those obtained from the HR monitoring equipment. The results indicate that the proposed method achieved higher performance for all of the subjects. In particular, the correlation to ECG for

**Attribute Correlation coefficient** 

**A 23 Male 175 76 0.973 0.703 B 23 Male 171 68 0.807 0.389 C 23 Male 165 50 0.754 0.621 D 25 Male 171 56 0.872 0.699 E 22 Male 180 92 0.972 0.658 F 22 Male 172 55 0.844 0.677 G 23 Male 170 62 0.737 0.346 Mean 23 - 172 65.6 0.851 0.585** 

Figure 13 shows an example of HR monitoring result for subject E. In the figure, the virtual axis is R-R interval (heartbeat interval), the horizontal axis is heartbeat count, the black line is R-R interval estimated by the proposed method, and the gray line is the one obtained by ECG. According the result around 200 beats, the proposed HR monitoring technology

estimates the correct R-R intervals even if the significant change is occurred.

Fig. 13. An example of HR (R-R interval) monitoring result by using proposed method

In this chapter, the Smart Health Management Technology is proposed with introductions to its applications. The notion of the technology centers causality to realize the transparency and

**Age [yrs] Gender Height [cm] Weight [kg] Proposed AC function** 

*<sup>i</sup>* is determined as heartbeat *HB* as formulated in equation (17).

*Amp* and

*Int* and the location with

Int(*i*) (17)

*<sup>i</sup>* is calculated by multiplying

 = Amp(*i*) \* 

**Step 3.** Finally,

**Subject** 

maximum

**4.4 Experimental results** 

the subject A and E is over 0.97.

Table 3. Experimental result on 7 males on a bed

**5. Summaries and conclusions** 

Knowledge 1 : The large pressure change is caused by heartbeat. Knowledge 2 : Heartbeat interval does not change significantly.

According to the knowledge on heartbeat characteristics, the fuzzy rules are denoted in the following.

Rule 1 : IF *<sup>i</sup> x* is HIGH, THEN the degree of heartbeat point *Amp* is HIGH. Rule 2 : IF *ti* is CLOSE to *T* ,THEN the degree of heartbeat point *Int* is HIGH.

where *Amp* is the membership function of Rule 1, *<sup>i</sup> x* is pre-processed pressure change, *it* is the sampling point of obtained pressure change, *T* is the average of heartbeat intervals that calculated by using previous ten heartbeats, and *Int* is the membership function of Rule 2. Then, the membership functions respond to the fuzzy rules are illustrated in Fig.12(a) and 12(b), and formulae are equations (12)–(14) and (15), (16).

Fig. 12. Membership functions

$$
\mu\_{Amp}(i) = \begin{cases} 0 & \text{if } \quad \mathbf{x}\_{\ i} < \mathbf{x}\_{\min} \\ \frac{\mathbf{x}\_{i} - \mathbf{x}\_{\min}}{\mathbf{x}\_{\max} - \mathbf{x}\_{\min}} & \text{if } \quad \mathbf{x}\_{\min} \le \mathbf{x}\_{i} \le \mathbf{x}\_{\max} \\ 1 & \text{if } \quad \mathbf{x}\_{i} > \mathbf{x}\_{\max} \end{cases} \tag{12}
$$

$$\mathbf{x}\_{\text{min}} \equiv \min(\mathbf{x}\_{\text{AlFS}}) \tag{13}$$

$$\mathbf{x}\_{\text{max}} \equiv \mathbf{max}(\mathbf{x}\_{\text{AFS}}) \tag{14}$$

$$\mu\_{Int}(i) = \exp\left(\frac{-(t\_i - \overline{T})^2}{2\sigma^2}\right) \tag{15}$$

*T* / 3 (16) **Step 3.** Finally, *<sup>i</sup>* is calculated by multiplying *Amp* and *Int* and the location with maximum *<sup>i</sup>* is determined as heartbeat *HB* as formulated in equation (17).

$$
\mu\_{\rm i} = \mu\_{\rm Amp}(\text{i}) \, \* \, \mu\_{\rm Int}(\text{i}) \tag{17}
$$

#### **4.4 Experimental results**

74 Health Management – Different Approaches and Solutions

Knowledge 1 : The large pressure change is caused by heartbeat. Knowledge 2 : Heartbeat interval does not change significantly.

According to the knowledge on heartbeat characteristics, the fuzzy rules are denoted in the

 *Amp* is the membership function of Rule 1, *<sup>i</sup> x* is pre-processed pressure change, *it* is the sampling point of obtained pressure change, *T* is the average of heartbeat intervals

Rule 2. Then, the membership functions respond to the fuzzy rules are illustrated in

(a) Heartbeat amplitude (b) Heartbeat interval

min

*x x i x <sup>x</sup> <sup>x</sup>*

 *x*min=min(*x*APS) (13)

 *x*max=max(*x*APS) (14)

( ) ( ) exp <sup>2</sup> *i*

 

*Amp i*

0 if

max min

1 if

( ) if

*x x*

*i*

*Int*

*i*

min

*i*

*x x*

*i*

*x x*

2 2

*t T*

max

min max

*Int* is the membership function of

*Amp* is HIGH.

*Int* is HIGH.

(12)

(15)

*T* / 3 (16)

following.

where

Fig. 12. Membership functions

that calculated by using previous ten heartbeats, and

Fig.12(a) and 12(b), and formulae are equations (12)–(14) and (15), (16).

Rule 1 : IF *<sup>i</sup> x* is HIGH, THEN the degree of heartbeat point

Rule 2 : IF *ti* is CLOSE to *T* ,THEN the degree of heartbeat point

In this experiment, the developed HR monitoring is compared with conventional and typical method that is based on autocorrelation functions. Table 3 shows the profile of each subject, and their correlations between HR changes obtained from the ECG and those obtained from the HR monitoring equipment. The results indicate that the proposed method achieved higher performance for all of the subjects. In particular, the correlation to ECG for the subject A and E is over 0.97.


Table 3. Experimental result on 7 males on a bed

Figure 13 shows an example of HR monitoring result for subject E. In the figure, the virtual axis is R-R interval (heartbeat interval), the horizontal axis is heartbeat count, the black line is R-R interval estimated by the proposed method, and the gray line is the one obtained by ECG. According the result around 200 beats, the proposed HR monitoring technology estimates the correct R-R intervals even if the significant change is occurred.

Fig. 13. An example of HR (R-R interval) monitoring result by using proposed method

#### **5. Summaries and conclusions**

In this chapter, the Smart Health Management Technology is proposed with introductions to its applications. The notion of the technology centers causality to realize the transparency and

Smart Health Management Technology 77

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#### **6. References**


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According to the notion of SHMT, Fig. 4 illustrates the causality from multivariate time series data. The applications introduced here provide mainly the measurement functionality. In the future, the accumulation of sensory data will realize the notion of

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**5** 

**Association of Intimate Partner** 

Mosiur Rahman and Golam Mostofa

*University of Rajshahi, Rajshahi* 

*Bangladesh* 

**Physical and Sexual Violence with Childhood Morbidity in Bangladesh** 

*Department of Population Science and Human Resource Development,* 

Although Bangladesh is on track to achieve Millennium Development Goal 4 (MDG4: reduce child mortality, approximately less than 50 per 1000 live births by 2015) (International Center for Diarrheal Disease Research, Bangladesh [ICDDR, B], 2007), child mortality rate still remains very high in this country. In Bangladesh, the mortality rate of under-five children was 65 per 1000 live births in 2007 and diarrhea (20%), acute respiratory infections (ARI) (18%) accounted for 38 % of the under-five deaths (United Nations International Children's Emergency Fund [UNICEF], 2010). Fever, is another symptom of acute infections and malaria among children in Bangladesh and contributes to high levels of malnutrition and mortality (National Institute of Population Research and Training

Although clinical (Haque et al., 2003), nutritional (Daniel et al., 2008; Tomkins, Dunn, & Hayes, 1989), household environmental (Gasana et al., 2002; Cairncross et al., 2010) and socio-demographic (Barros et al., 2010; Rayhan, Khan, & Shahidullah, 2007) risk factors of ARI, diarrhea, and fever are well documented, research has only begun to investigate the influence of other aspects of the social environment. Intimate partner violence (IPV) is defined as the range of sexually, psychologically, and physically coercive acts used against women by current or former male intimate partners (World Health Organization [WHO], 1997). Intimate partner violence is considered to be one of the psychosocial factors that might influence child morbidity status (Campbell 2002). It can affect child morbidity status through psychological stress of the child, resulting from observing IPV; stress in turn can exert an effect on immune reactivity and link to increase vulnerability to illness (Friedman & David, 2002). Besides, IPV can affect child health outcome through direct violence, injury, and mistreatment of children from fathers who abuse their female partners (Herrenkohl et al., 2008; Christian et al., 1997), or through physical or psychological maternal health outcomes such as stress and depression, suicidal thoughts and infectious diseases including HIV/AIDS (Ellsberg et al., 2008; Sutherland, Bybee, & Sullivan, 1998; Coker et al., 2002; Silverman et al., 2007; Silverman et al., 2008) or through diminishing mother's autonomy, social isolation, and lack of control over financial resources (Ellsberg et al., 2008; Smith &

Martin, 1995; Forte et al., 1996), that can prevent proper care of the child.

**1. Introduction** 

[NIPORT], 2009; Rayhan, Khan, & Shahidullah, 2007).

basics and experimental findings, *International Journal of Obesity,* Vol. 25, No. 4, 502- 511 (2001)

