**3. A wellness wear system**

A wellness wear system is an integrated system consisting of wellness wear, biosensors, hardware, and software. One of its advantages is easy aquisition of vital signs at any time and anywhere, which provide important basic data for monitoring health status. In this respect, advanced sensor technology is recommended to ensure the accuracy of the signals. The development of digital or conductive yarns also plays a crucial role in the formation of a body area network (BAN) within the clothes. These special yarns enable wired transmission of the data over the clothes. Further, hardware is used for digital signal processing (DSP), wired and wireless communication, and integration of multiple biosensors. However, another important technology is software.

In particular, the success of wellness wear systems depends highly on the quality of the medical information extracted from that data by the software and the medical content that is provided. In this section, we present a nanofiber technique–based wellness wear system that we are developing as an example of wellness wear. This system has a particular emphasis on software applications, including a fundamental service framework as an infrastructure and an application called Calorie Tracker, which runs on Android-based smartphones and enables weight loss and exercise management (Kim et al., 2011).

#### **3.1 An overview**

Here, we introduce a wellness wear system that we are currently developing. A primitive but fundamental scenario that we assume is that the user wears smart clothes with noninvasive and comfortable biosensors that typically measure ECG, respiration, body

The Zephyr BioHarness technology is well-known because it was used in connection with the rescue operation of 33 trapped miners in northern Chile's San Jose mine (Zephyr Technology, 2010). It has also been adopted by NASA for use by astronauts in training. The BioHarness, a chest strap with sensors and wireless technology, monitors and transmits the wearer's vital signs, such as ECG data, HR, breathing rate, skin temperature, posture, activity, acclerometry, blood pressure, and pulse oximetry. Vital signs are transmitted with ISM or Bluetooth for remote monitoring anywhere in the world. The software platform

A wellness wear system is an integrated system consisting of wellness wear, biosensors, hardware, and software. One of its advantages is easy aquisition of vital signs at any time and anywhere, which provide important basic data for monitoring health status. In this respect, advanced sensor technology is recommended to ensure the accuracy of the signals. The development of digital or conductive yarns also plays a crucial role in the formation of a body area network (BAN) within the clothes. These special yarns enable wired transmission of the data over the clothes. Further, hardware is used for digital signal processing (DSP), wired and wireless communication, and integration of multiple biosensors. However,

In particular, the success of wellness wear systems depends highly on the quality of the medical information extracted from that data by the software and the medical content that is provided. In this section, we present a nanofiber technique–based wellness wear system that we are developing as an example of wellness wear. This system has a particular emphasis on software applications, including a fundamental service framework as an infrastructure and an application called Calorie Tracker, which runs on Android-based smartphones and

Here, we introduce a wellness wear system that we are currently developing. A primitive but fundamental scenario that we assume is that the user wears smart clothes with noninvasive and comfortable biosensors that typically measure ECG, respiration, body

OmniSense is used to display the BioHarness data.

Fig. 8. The Zephyr BioHarness technology

another important technology is software.

**3.1 An overview** 

enables weight loss and exercise management (Kim et al., 2011).

**3. A wellness wear system** 

temperature, and the like. While the subject is sitting, sleeping, walking, running, exercising, or performing any other activity, biosignals are recorded and transmitted to the terminal and/or the server. Smart phones can be both terminals that transmit the data and devices that enable the application software to run. The software system analyzes the signals and other general data such as age and gender, and provides the user with a relevant medical recommendation. Figure 9 illustrates the scenario described.

Fig. 9. Use scenario of a wellness wear system

Using calorie tracking as an example, here, we describe four of the important elements required for the development of the wellness wear system that we are implementing: biosensors, digital yarns, a software framework, and communication and medical services.

#### **3.2 Biosensors**

When we develop biosensors that are attachable to wellness wear, two crucial factors are accuracy of the obtained signals and user comfort. However, a tradeoff is typically required between them. Generally, the more accurate wearable biosensors are, the less comfortable they are, and *vice versa*. Thus, a future goal concerns how to overcome this. Comfort is a particularly important element. Because biosensors are adhered to smart clothes and touch the skin, they should be small, comfortable, and noninvasive. This is one of the reasons for the requirement for nanofiber. In fact, nanofiber is comfortable even when sensors are attached to smart clothes. For example, the physiological sensor belt (PSB), which detects the breath and pulse (Kim et al., 2009), is poromeric and protected from electromagnetic waves. It is much more comfortable than ordinary hospital devices that sense vital signs.

Several typical vital signs can be detected by biosensors attached to wellness wear. As we saw in the previous section, the ECG is one biosignal that most smart medical clothes aim to detect. It is ideal to detect life-threatening diseases early and to monitor patients and manage health through wireless communications (Taylor and Sharif, 2006). HR variability (HRV) extracted from ECG is also an important biosignal that helps in diagnosing heartrelated illnesses and can check the efficiency of exercise and even stress. Respiration

Health Care with Wellness Wear 51

Applications to provide medical service content are more complex and larger to build than many realize. This is because issues such as standardization, interoperability, reusability, reduction of maintenance cost, and readability must be addressed, which requires a sustainable and flexible infrastructure before application development. For this purpose, we have developed a framework that underlies all wellness wear application systems (Kim et

The primary idea is that vital signs from the wellness clothes are transformed into a metamodel-based abstract tree on which health-care services are defined through Object Constraint Language (OCL) (OMG-OCL, 2009) with the help of medical specialists and engineers. This idea implies that each service must be clearly defined and that integrated management is much easier when a repository of biosignal data is constructed. It also helps to make additions, deletions, and updates of services convenient. In particular, because the framework expresses the biosignal data as an HL7 (HL7, 2009) metamodel. based on MetaObject Facility (MOF), the M3 level, defined by the Object Management Group (OMG), standardization of the data and interoperability and integration between the biosignal data.

Fig. 11. Framework for a software solution in the wellness wear system

Once the biosignal data obtained from sensors are accurate and stable, what is then important is how to support proper health management services, such as stress management, food control, an exercise plan, and general health management. When these services are perceived as useful by different stakeholders, the whole system, including other technologies such as the digital garments, DSP, and biosensors, will eventually succeed. In this respect, software and related medical services are primary elements for the success of

We consider that the software solution has four critical areas. First, algorithms and data mining techniques to process and analyze the given biosignals are required. Noise detection and filtering must be very basic techniques, and analysis of the biosignals, including both single and multiple biosignals, is important. In particular, multiple-signal analysis is a

**3.4.1 Framework** 

al., 2009).

is achieved.

**3.4.2 Software solution** 

the wellness wear system.

monitoring is also required to monitor walking and detect sleep apnea. Recently, fabricbased respiration sensors for wellness wear have been developed, and the quality of the signal has improved (Catrysse et al., 2004). Accelerometers have become popular to evaluate the amount of exercise. The number of steps taken can be identified by accelerometers. Together with GPS, one can determine the distance walked or run. For the elderly, accelerometers can support fall detection. They are also used to monitor chronic obstructive pulmonary disease patients (Mathie et al., 2004). Additionally, researchers are making efforts to develop wellness wear–based biosensors to obtain skin temperature, SpO2, and blood pressure.

#### **3.3 Digital yarns**

To produce wellness wear, several technologies are needed, such as sewing, knitting, and embroidering technologies. More importantly, however, digital yarns that require highly advanced technologies play an important role in the transmission of biosignal data. In fact, conductive or digital yarns that transmit data within the clothes form a BAN. Dr. Gi-Soo Chung at the Korea Institute of Industrial Technology (KITECH) developed a digital yarn in which the major material is a copper alloy (Chung et al., 2006; Chung, 2007). His digital yarn is divided into two parts: a core and an outer part. As seen in Figure 10, its core consists of 7 microwires and a special resin. Its outer part is covered with dyed normal yarn. Electric resistance of the digital yarn is fairly low, at 7.5 Ω/m, compared with previously developed conductive yarns (Bekaert, 2011; Linz et al., 2005; TEXTILE, 2005).

Fig. 10. A digital yarn

A digital garment is made with both ordinary yarns and digital bands for data transmission. Here, a digital band is a set of 10 to 30 digital yarns that are used as a communication line. The transmission speed of the yarn is very high (approximately 80 Mbps). This indicates that it could transmit an 800-MB movie file within approximately 1.5 min (Chung and Kim, 2011).

#### **3.4 Software: Framework and software solution**

Software aspects consist of a framework and a software solution. The framework is a foundation on which software applications are built, and the software solution concerns everything related to the software that is implemented on the framework.

#### **3.4.1 Framework**

50 Health Management – Different Approaches and Solutions

monitoring is also required to monitor walking and detect sleep apnea. Recently, fabricbased respiration sensors for wellness wear have been developed, and the quality of the signal has improved (Catrysse et al., 2004). Accelerometers have become popular to evaluate the amount of exercise. The number of steps taken can be identified by accelerometers. Together with GPS, one can determine the distance walked or run. For the elderly, accelerometers can support fall detection. They are also used to monitor chronic obstructive pulmonary disease patients (Mathie et al., 2004). Additionally, researchers are making efforts to develop wellness wear–based biosensors to obtain skin temperature, SpO2, and

To produce wellness wear, several technologies are needed, such as sewing, knitting, and embroidering technologies. More importantly, however, digital yarns that require highly advanced technologies play an important role in the transmission of biosignal data. In fact, conductive or digital yarns that transmit data within the clothes form a BAN. Dr. Gi-Soo Chung at the Korea Institute of Industrial Technology (KITECH) developed a digital yarn in which the major material is a copper alloy (Chung et al., 2006; Chung, 2007). His digital yarn is divided into two parts: a core and an outer part. As seen in Figure 10, its core consists of 7 microwires and a special resin. Its outer part is covered with dyed normal yarn. Electric resistance of the digital yarn is fairly low, at 7.5 Ω/m, compared with previously developed

A digital garment is made with both ordinary yarns and digital bands for data transmission. Here, a digital band is a set of 10 to 30 digital yarns that are used as a communication line. The transmission speed of the yarn is very high (approximately 80 Mbps). This indicates that it could transmit an 800-MB movie file within approximately 1.5 min (Chung and Kim,

Software aspects consist of a framework and a software solution. The framework is a foundation on which software applications are built, and the software solution concerns

everything related to the software that is implemented on the framework.

conductive yarns (Bekaert, 2011; Linz et al., 2005; TEXTILE, 2005).

blood pressure.

**3.3 Digital yarns** 

Fig. 10. A digital yarn

**3.4 Software: Framework and software solution** 

2011).

Applications to provide medical service content are more complex and larger to build than many realize. This is because issues such as standardization, interoperability, reusability, reduction of maintenance cost, and readability must be addressed, which requires a sustainable and flexible infrastructure before application development. For this purpose, we have developed a framework that underlies all wellness wear application systems (Kim et al., 2009).

The primary idea is that vital signs from the wellness clothes are transformed into a metamodel-based abstract tree on which health-care services are defined through Object Constraint Language (OCL) (OMG-OCL, 2009) with the help of medical specialists and engineers. This idea implies that each service must be clearly defined and that integrated management is much easier when a repository of biosignal data is constructed. It also helps to make additions, deletions, and updates of services convenient. In particular, because the framework expresses the biosignal data as an HL7 (HL7, 2009) metamodel. based on MetaObject Facility (MOF), the M3 level, defined by the Object Management Group (OMG), standardization of the data and interoperability and integration between the biosignal data. is achieved.

Fig. 11. Framework for a software solution in the wellness wear system

#### **3.4.2 Software solution**

Once the biosignal data obtained from sensors are accurate and stable, what is then important is how to support proper health management services, such as stress management, food control, an exercise plan, and general health management. When these services are perceived as useful by different stakeholders, the whole system, including other technologies such as the digital garments, DSP, and biosensors, will eventually succeed. In this respect, software and related medical services are primary elements for the success of the wellness wear system.

We consider that the software solution has four critical areas. First, algorithms and data mining techniques to process and analyze the given biosignals are required. Noise detection and filtering must be very basic techniques, and analysis of the biosignals, including both single and multiple biosignals, is important. In particular, multiple-signal analysis is a

Health Care with Wellness Wear 53

in most developed countries. Obesity not only affects one's physical appearance, but also leads to a number of weight-related diseases, such as insulin resistance syndrome, cardiovascular disease, and type 2 diabetes. It is also leads to various chronic diseases. Generally, obesity is treated with food control and exercise. The calorie tracker was designed to evaluate the amount of exercise, show the number of calories burned during the

The choice of Android as an operating system was made primarily because of its multitasking performance, which is important for real-time continuous transmission of ECG data. If multitasking is not supported, ECG data transmission will suddenly cease when another application begins running or when the user initiates or receives a telephone call. The smartphone is both a device that handles client programs and a terminal that exchanges data with a server. The ECG data acquired from the wellness wear are transferred to the smartphone using Bluetooth, and the system on the smartphone transmits the data to the

The calorie tracker is best understood in terms of its four phases of use. First, it acquires ECG data from the wellness wear. More specifically, ECG data are obtained while the user is performing an activity such as running, walking, or sitting for a certain period of time; for example, 5 min. Figure 13 shows an ECG signal obtained from wellness wear. The user also provides the calorie tracker with other general data, such as the user's weight, height, age, gender, and more. Additionally, the user must provide information regarding whether

exercise, and recommend simple exercise plans.

**3.5.2 Usage scenario** 

server using the XML form that conforms to the HL7 standard.

he/she is at rest or active, is taking medications, or is diabetic.

Fig. 13. Screenshot of the user interface of the calorie tracker

relatively new and important research field that involves understanding stress and the amount of exercise by analyzing the relationship between ECG and respiration. Second, development of health-care service content by medical specialists is key. Their roles are to plan and organize health-care service content using their expertise and cooperating with engineers. They will also develop health indices in the context of wellness wear systems, such as a wellness index and HR variation index. Third, both wired and wireless communication are required for the wellness wear system. For example, Bluetooth or ZigBee is used for short-distance wireless communication of vital signs. Another important task is to accept the international standard ISO/IEEE 11073 communication model and add its related communication protocols for processing vital signs information in different medical devices. A standardized protocol is not urgently needed in the near future; however, many experts anticipate that communication between personal health devices (PHDs) will be standardized eventually. It is also meaningful to follow a communication standard. Finally, an application service system provides health-care services. It is the central system that integrates various modules, including the biosignal analysis module, service components, and communication. Next, we present a software program called Calorie Tracker, which works together with the wellness wear system.

Fig. 12. Software solution in the wellness wear system

#### **3.5 Calorie tracker: A medical service example**

There must be a large number of potential medical services supported by a wellness wear system. We have developed a software application involving a calorie tracker (Kim et al., 2011) and herein describe how wellness wear is used with the software for health care.

#### **3.5.1 Why calorie tracker?**

The calorie tracker, which runs on an Android-based smartphone, analyzes HRV extracted from the ECG data obtained by wellness wear and provides a weight-loss program. The calorie tracker is useful in managing obesity. As we know, obesity reduction is a top priority

relatively new and important research field that involves understanding stress and the amount of exercise by analyzing the relationship between ECG and respiration. Second, development of health-care service content by medical specialists is key. Their roles are to plan and organize health-care service content using their expertise and cooperating with engineers. They will also develop health indices in the context of wellness wear systems, such as a wellness index and HR variation index. Third, both wired and wireless communication are required for the wellness wear system. For example, Bluetooth or ZigBee is used for short-distance wireless communication of vital signs. Another important task is to accept the international standard ISO/IEEE 11073 communication model and add its related communication protocols for processing vital signs information in different medical devices. A standardized protocol is not urgently needed in the near future; however, many experts anticipate that communication between personal health devices (PHDs) will be standardized eventually. It is also meaningful to follow a communication standard. Finally, an application service system provides health-care services. It is the central system that integrates various modules, including the biosignal analysis module, service components, and communication. Next, we present a software program called

Calorie Tracker, which works together with the wellness wear system.

Fig. 12. Software solution in the wellness wear system

There must be a large number of potential medical services supported by a wellness wear system. We have developed a software application involving a calorie tracker (Kim et al., 2011) and herein describe how wellness wear is used with the software for health care.

The calorie tracker, which runs on an Android-based smartphone, analyzes HRV extracted from the ECG data obtained by wellness wear and provides a weight-loss program. The calorie tracker is useful in managing obesity. As we know, obesity reduction is a top priority

**3.5 Calorie tracker: A medical service example** 

**3.5.1 Why calorie tracker?** 

in most developed countries. Obesity not only affects one's physical appearance, but also leads to a number of weight-related diseases, such as insulin resistance syndrome, cardiovascular disease, and type 2 diabetes. It is also leads to various chronic diseases. Generally, obesity is treated with food control and exercise. The calorie tracker was designed to evaluate the amount of exercise, show the number of calories burned during the exercise, and recommend simple exercise plans.

The choice of Android as an operating system was made primarily because of its multitasking performance, which is important for real-time continuous transmission of ECG data. If multitasking is not supported, ECG data transmission will suddenly cease when another application begins running or when the user initiates or receives a telephone call. The smartphone is both a device that handles client programs and a terminal that exchanges data with a server. The ECG data acquired from the wellness wear are transferred to the smartphone using Bluetooth, and the system on the smartphone transmits the data to the server using the XML form that conforms to the HL7 standard.

#### **3.5.2 Usage scenario**

The calorie tracker is best understood in terms of its four phases of use. First, it acquires ECG data from the wellness wear. More specifically, ECG data are obtained while the user is performing an activity such as running, walking, or sitting for a certain period of time; for example, 5 min. Figure 13 shows an ECG signal obtained from wellness wear. The user also provides the calorie tracker with other general data, such as the user's weight, height, age, gender, and more. Additionally, the user must provide information regarding whether he/she is at rest or active, is taking medications, or is diabetic.

Fig. 13. Screenshot of the user interface of the calorie tracker

Health Care with Wellness Wear 55

Fig. 15. The calorie tracker's evaluation and recommendation

Bekaert (a firm in Belgium), (2011). April, 2011. http://www.swicofil.com/bekintex.

This work is funded by the Korean Ministry of Knowledge Economy (#10033321 and

Axisa, F., Schmitt, P. M., Gehin, C., Delhomme, G., McAdams, E. and Dittmar, A. (2005).

Catrysse M., Puers R., Hertleer C., Van Langenhove L., van Egmond H., and Matthys D.

*in Biomedicine*, vol. 9, no. 3, September, 2005, pp. 325-336.

*Sensors and Actuators. A: Physical.* 114(2-3), pp. 302-311.

Flexible Technologies and Smart Clothing for Citizen Medicine, Home Healthcare, and Disease Prevention, *IEEE Transactions on Information Technology* 

(2004). Towards the integration of textile sensors in a wireless monitoring suit.

**5. Acknowledgment** 

#10033443).

**6. References** 

Second, the system measures HRV from the ECG data. ECGs consist of waves of P-Q-R-S-T, with R peaks being the highest of the five. Figure 14 illustrates an ideal ECG signal, showing adjacent R peaks and the R-R interval. Here, the HRV indicates the variablity of intervals between R waves, which are called R-R intervals. Thus, HRV is a time series of intervals between successive R peaks in the ECG. HRV has relevance for physical, emotional, and mental functions. The variability in HR is an adaptive quality in a healthy body. Generally, HRV is useful for evaluating functions of the autonomous nervous system, whereas ECG is better for the diagnosis of various heart-related diseases. Well-known HRV indices include the normalized beat-to-beat interval (NN), the standard deviation of those intervals (SDNN), and the percentage of the interbeat intervals differing from neighboring intervals by 50 ms or more.

Fig. 14. R peaks and R-R intervals on an ECG

Third, the system calculates the number of calories burned (energy expenditure) and the body mass index using the HRV and other general data. Energy expenditure is crucial to the determination of how much and how intense exercise should be for each person. To determine calorie consumption, the maximal oxygen consumption per min (%VO2max) is obtained using HRV measurements, including the average resting HRV (HRVR) and the maximal HRV (HRVM), according to the user's age (Lubell and Marks, 1986). Next, the number of calories burned during exercise or other activities for a given period of time is calculated from %VO2max and the user's weight.

Finally, the system offers the user a weight-loss program. For example, it may state, "You need light exercise for two hours a day to reduce weight by 1 kg in a month." Based on the HRV data recorded during a certain period of the user's activity (e.g., 5 min), the system determines the intensity of exercise related to the user. It then makes a recommendation for an appropriate exercise level to help the user reduce weight according to his or her weight loss goals (Fig. 15).

#### **4. Conclusions**

Wellness wear systems are expected to be used for health care in the near future. In this chapter, we described wellness wear systems in general and introduced in detail a wellness wear system that we are currently developing. In doing so, we have demonstrated the potential for health care using wellness wear. Additionally, we presented a weight loss program called Calorie Tracker, which works together with wellness wear, as an example of a medical service. Although a stable market for wellness wear has not yet developed, we believe that a healthy life with wellness wear will eventually be realized as certain technological problems are resolved.

Second, the system measures HRV from the ECG data. ECGs consist of waves of P-Q-R-S-T, with R peaks being the highest of the five. Figure 14 illustrates an ideal ECG signal, showing adjacent R peaks and the R-R interval. Here, the HRV indicates the variablity of intervals between R waves, which are called R-R intervals. Thus, HRV is a time series of intervals between successive R peaks in the ECG. HRV has relevance for physical, emotional, and mental functions. The variability in HR is an adaptive quality in a healthy body. Generally, HRV is useful for evaluating functions of the autonomous nervous system, whereas ECG is better for the diagnosis of various heart-related diseases. Well-known HRV indices include the normalized beat-to-beat interval (NN), the standard deviation of those intervals (SDNN), and the percentage of the interbeat intervals differing from neighboring intervals

Third, the system calculates the number of calories burned (energy expenditure) and the body mass index using the HRV and other general data. Energy expenditure is crucial to the determination of how much and how intense exercise should be for each person. To determine calorie consumption, the maximal oxygen consumption per min (%VO2max) is obtained using HRV measurements, including the average resting HRV (HRVR) and the maximal HRV (HRVM), according to the user's age (Lubell and Marks, 1986). Next, the number of calories burned during exercise or other activities for a given period of time is

Finally, the system offers the user a weight-loss program. For example, it may state, "You need light exercise for two hours a day to reduce weight by 1 kg in a month." Based on the HRV data recorded during a certain period of the user's activity (e.g., 5 min), the system determines the intensity of exercise related to the user. It then makes a recommendation for an appropriate exercise level to help the user reduce weight according to his or her weight

Wellness wear systems are expected to be used for health care in the near future. In this chapter, we described wellness wear systems in general and introduced in detail a wellness wear system that we are currently developing. In doing so, we have demonstrated the potential for health care using wellness wear. Additionally, we presented a weight loss program called Calorie Tracker, which works together with wellness wear, as an example of a medical service. Although a stable market for wellness wear has not yet developed, we believe that a healthy life with wellness wear will eventually be realized as certain

by 50 ms or more.

loss goals (Fig. 15).

**4. Conclusions** 

technological problems are resolved.

Fig. 14. R peaks and R-R intervals on an ECG

calculated from %VO2max and the user's weight.

Fig. 15. The calorie tracker's evaluation and recommendation

#### **5. Acknowledgment**

This work is funded by the Korean Ministry of Knowledge Economy (#10033321 and #10033443).

#### **6. References**

Axisa, F., Schmitt, P. M., Gehin, C., Delhomme, G., McAdams, E. and Dittmar, A. (2005). Flexible Technologies and Smart Clothing for Citizen Medicine, Home Healthcare, and Disease Prevention, *IEEE Transactions on Information Technology in Biomedicine*, vol. 9, no. 3, September, 2005, pp. 325-336.

Bekaert (a firm in Belgium), (2011). April, 2011.

http://www.swicofil.com/bekintex.

Catrysse M., Puers R., Hertleer C., Van Langenhove L., van Egmond H., and Matthys D. (2004). Towards the integration of textile sensors in a wireless monitoring suit. *Sensors and Actuators. A: Physical.* 114(2-3), pp. 302-311.

Health Care with Wellness Wear 57

Luprano, J. et al. (2006). Combination of Body Sensor Networks and On-Body Signal

Luprano, J. et al. (2007). New Generation of Smart Sensors for Biochemical and

Lymberis, A. & Olsson S. (2003). Intelligent Biomedical Clothing for Personal Health

Mathie M. J., Coster A. C., Lovell N. H., and Celler B. G. (2004). Accelerometry: providing

McCann, J., Hurford, R. And Martin, A. (2005). A Design Process for the Development of

Noury, N. et al. (2004). A Smart Cloth for Ambulatory Telemonitoring of Physiological

Pandian, P.S. et al. (2008). Smart Vest: Wearable Multi-parameter Remote Physiological

Pantelopoulos, A. & Bourbakis, N.G. (2010). A Survey on Wearable Sensor-Based Systems

Paradiso, R. et al. (2008). Remote Health Monitoring with Wearable Non-Invasive

Park, S. & Jayaraman S. (2010). Smart Textile-based Wearable Biomedical Systems: A

Saranummi, N. (2002). Information Technology in Biomedicine, *IEEE Trans. Biomed. Eng.*,

Taylor S. A. and Sharif H. (2006). Wearable Patient Monitoring Application (ECG) using Wireless Sensor Networks. *Proc IEEE Eng Med Biol Soc.* pp. 5977-5980.

*International Symposium on Wearable Computers (ISWC'05)*.

160, ISBN 0-7803-8453-9, Odawara, Japan, June 28-29, 2004. OMG-OCL, (2009). "Object Constraint Language Specification, Version 2.0", http://www.omg.org/technology/documents/formal/ocl.htm

7695-2547-4, Cambridge, Massachusetts, USA, April 3-5, 2006.

Greece, June, 2007.

466-477, ISSN 1350-4533.

1-12, ISSN 1094-6977.

vol. 49, no. 12, pp. 1385–1386.

2008.

7771.

movement. *Physiol. Meas.* 25(2), pp. 1-20.

5627.

Processing Algorithms: the practical case of MyHeart project, *International Workshop on Wearable and Implantable Body Sensor Networks*, pp. 76-79, ISBN 0-

Bioelectrical Applications, *Proceedings of 2007 pHealth Conference*, Chalkidiki,

and Disease Mangement : State of the Art and Future Vision, *Telemediciane Journal and e-Health*, Vol. 9, No. 4, (December 2003), pp. 379-386, ISSN 1530-

an integrated, practical method for long-term, ambulatory monitoring of human

Innovative Smart Clothing that Addresses End-User Needs from Technical, Functional, Aesthetic and Cultureal View points. *Proceedings of the IEEE* 

Parameters and Activity: the VTAMN Project, *Proceedings of 6th International Workshop on Enterprise Networking and Computing in Healthcare Industry*, pp. 155-

Monitoring System, *Medical engineering & Physics*, Vol. 30, No. 4, (May 2008), pp.

for Health Monitoring and Prognosis, *IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews*, Vol. 40, No. 1, (January 2010), pp.

Mobile System: the HealthWear Project, *Proceedings of IEEE 30th Annual International Conference on Engineering in Medicine and Biology Society*, pp. 1699-1702, ISBN 1557-170X, Vancouver, British Columbia, Canada, August 20-24,

Transition Plan for Research to Reality, *IEEE Transactions on Information Technology in Biomedicine*, Vol. 14, No. 1, (January 2010), pp. 86-92, ISSN 1089-


Chung, G. S., An, J. S., Lee, D. H. and Hwang, C. S. (2006). A Study on the Digital Yarn for

Chung, G. S. (2007). Wearable Computer for Protective Clothing, *8th European Seminar on* 

Chung, G. S. and Kim, H. C. (2011). Smart Clothes Are Neew Interactive Devices. *HCI* 

Di Rienzo, M. et al. (2005). MagIC System: A New Textile-based Wearable Device for

Grossman, P. (2004). The LifeShirt: A Multi-Function Ambulatory System Monitoring

*Technology and Informatics*, Vol. 108, pp. 133-141, ISBN 978-1-58603-449-8. Habetha, J. (2006). The MyHeart Project - Fighting Cardiovascular Diseases by Prevention

Heilman, K.J. & Porges, S.W. (2007). Accuracy of the Lifeshirt® (Vivometrics) in the

Kim, H. –C., Chung, G. –S., and Kim. T. –W. (2009). A Framework for Health Management

Kim, H., Kim, T., M. Joo, S. Yi, C. Yoo, K. Lee, J. Kim, and G. Chung (2011). Design of a

Kim, K. J., Chang, Y. M., Yoon, S. and Kim, H. J. (2009). A Novel Piezoelectric PVDF Film-

Lauter, L. (2003). Personal Health Care in Philips: Status and Ambition, *Proceedings of 25th*

Linz, T., Kallmayer, C., Aschenbrenner, R. and Reichl, H. (2005). Embroidering Electrical

Lubell, M. and Marks, S. (1986) Health Fitness Monitor. U.S. Patent 4,566,461, Jan 28,

1-4244-0032-5, New York, USA, August 30-September 3, 2006.

*2009*. Decmber 16-18, Sydney, Australia, 2009, pp. 70-73.

*Science*. Special Issue, Vol. 5, No. 2, pp. 70-73. To appear.

Monitoring System, *Integrated Ferroelectrics, 107*, pp. 53-68.

*and Textiles*, 2006, pp. 207~210.

*International 2011*, To appear.

September, 2005.

pp. 300-305. HL7, Inc. (2009). What is HL7?,

2003, pp. 3748.

1986.

http://www.hl7.org/about/hl7about.htm

October 19-21, 2005, Osaka, Japan.

*Personal Protective Equipment*, 2007.03

the High Speed Data Communication, *The 2nd International Conference on Clothing* 

Biological Signal Monitoring. Applicability in Daily Life and Clinical Setting, *Proceedings of IEEE 27th Annual International Conference on Engineering in Medicine and Biology Society*, pp. 7167-7169, ISBN 0-7803-8741-4, Shanghai, China,

Health, Disease, and Medical Intervention in the Real World, *Studies in Health* 

and Early Diagnosis, *Proceedings of IEEE 28th Annual International Conference on Engineering in Medicine and Biology Society*, Vol. Supplement, pp. 6746-6749, ISBN

Detection of Cardiac Rhythms, *Biological Psychology*, Vol. 75, No. 3,(April 2007),

Services in Nanofiber Technique-based Wellness Wear Systems, *IEEE Healthcom* 

Calorie Tracker Utilizing Heart Rate Variability Obtained by a Nanofiber Technique-based Wellness Wear System, *Applied Mathematics and Information* 

based Physiological Sensing Belt for a Complementary Respiration and Heartbeat

*Annual International Conference on IEEE-EMBS*, Cancun, Mexico, September. 17-21,

Interconnects with Conductive Yarn for the Integration of Flexible Electronic Modules into Fabric, *IEEE International Symposium on Wearable Computing*,


**4** 

*Japan* 

Hiroshi Nakajima *Omron Corporation* 

**Smart Health Management Technology** 

The notion of health management technology (HMT) is simple but powerful because it employs the ideas of cyclical evolution and synergetic integration of devices and services based on causality and human machine collaboration (Nakajima, 2008a). It can be applied for the different entities of human beings, artifacts, and nature environment as following discussions. An essential and simple observation and understanding of our world reveals that it can be considered as comprising humans, artifacts, and nature environments shown in Fig.1. Even though the values of the entities are given by humans, they have obviously different directions. Examples of such values are comfort and safety for humans, efficiency and effectiveness for artifacts, and environmental enhancement for nature. The problem is that these values come into conflict with each other. Examples of conflicts in a factory are as follows. Productivity related to efficiency and effectiveness is the most important value in manufacturing lines. However, the focus only on productivity will increase the emission of carbon dioxide and other contaminants that will negatively influence to the sustainability of nature environment, and the safety and comfort of human operators in the manufacturing line. Thus, the conflicts of values among the respective entities cause serious problems in important areas such as the environment, agriculture and food, security and safety, and human health these days. In this sense, harmonization among them should be realized with keeping good health condition of each entity for realizing a desired next society. Although this vision might look grandiloquent, health management of each entity is considered as important activity as

Because of recent development of information and communication technology (ICT), sensory networks have been pervading various fields such as home security, healthcare, condition-based maintenance for manufacturing equipment, and environment monitoring.

HMT is designed for providing basic four kinds of functions by centering causality. The functions are measurement, recognition, estimation, and evolution. The functions provide the solution of cyclical evolvement based on causality which abstractly illustrates conditions of target systems and is used as problem solving knowledge, which is composed of feature attributes extracted from sensory data and intermediate characteristics. In this sense, causality could evolve and be updated according to sophistication of sensing and control mechanisms. Because the nature of causality is transparent to humans, the structure can be easily improved through human-machine collaboration. This feature of the technology is quite important because the integration of human knowledge and sensory data will bring a

They require the suitable integration of both sensing devices and valuable services.

**1. Introduction** 

steady steps toward the bright future.

"TEXTILE WIRE version 03.01-e", (2005). ELEKTRO-FEINDRHAT-AG, Switzerland.

Zephyr-Technology (2010). Case Study: Zephyr Provides Physiological Monitoring of Chilean Miners During San Jose Mine Rescue Operation. http://www.zephyr-technology.com/wp-content/uploads/2010/10/Case-

Study-Chilean-Miner-Rescue-Operation.pdf
