//Logo Image
PDF Version

作者:劉育瑋 (2014-07-17);推薦:徐業良(2015-12-11)


Chapter 5. Extending the bed-centered telehealth system

This chapter describes the extension of the bed-centered telehealth system. By adding sensors into the bed-centered telehealth system, the system could provide more valuable information of the elderly people in daily lives for remote caregivers.

5.1    Integrating sensors of activities of daily living (ADL)

The ageing process of the elderly results in declined functional status and decreased mobility, which affects their level of self-care and independence. Functional status can be defined as the ability to perform activities necessary to ensure well-being, and it can be assessed by examining the ability to carry out various activities of daily living (ADL) [Heikkinen, 1998]. Limitations in basic ADL and instrumental ADL (IADL) are strongly associated with less mobility [Shimada et al., 2010]. Katz et al. identified 6 basic ADL items associated with one’s living independence: bathing, dressing, toileting, transfer, continence and feeding [Katz et al., 1970; 1976]. Physical activity is regarded as any bodily movement produced by skeletal muscles and results in energy expenditure [Caspersen et al., 1985].

With rapid advances in sensors and telecommunication technologies, human physical activities can be monitored by means of sensors. Celler et al. [1994] proposed the concept of implementing sensor networks in a home environment to monitor functional status of the elderly living alone. For measuring ADL, passive infrared sensors (PIR) and switches are commonly used to detect the occupancy of human in space (home) and location transfer. Electricity sensors can be used to detect the use of home appliances [Franco et al., 2008]. Motion sensors can also be used to measure human activities.

5.1.1        The ADL sensor introduction and power-save design

Yang et al. [2010] developed an activity monitoring system for mobility and functional ability telemonitoring for elderly people living at home. This system is also integrated with the Decentralized Home Telehealth System (DHTS) for home use. The sensors are including the temperature, humidity, Passive infrared (PIR) and CT (current transformer) sensing functions. It is distributed in several locations of interests in a home environment for detecting home ADL of the elderly. Figure 3-1 shows the ADL sensors.

Figure 5-1. The ADL sensors (Right hand side: temperature, humidity and passive infrared sensor; left hand side: current transformer sensor)

In this study, these ADL sensors are integrated with the bed-centered telehealth system for mobility and functional ability tele-monitoring of elderly people. The ADL sensor equipped with a microchip, Zigbee transmission module and sensing components powered by AC adapter or battery (+4.8V). For extending the life of battery, a power-saving circuit and sleep-mode procedure is designed for the Microchip of ADL sensors. The main target is to decrease the meaningless energy consumption by the sensor. As shown in Figure 5-2, the ADL sensor will send the sensor ID number to the bedside data processor (BDP) when it is started. When the PIR sensor is in motionless state for more than 1 minute, the microchip of the sensor will go into sleep mode, and the power of Zigbee module is also shut off (Pin4 is low); if the PIR sensor is activated, the Zigbee module will be turn on (Pin4 is High) first, and the sensing data will be transmitted to the BDP by UART (Universal Asynchronous Receiver/Transmitter). In this power saving design, the static working current of ADL sensor is lower than 1mA, and the dynamic working current is 67mA. The estimated battery life is about 2,400 hours (100 days) if the ADL sensor is powered by battery in 2,400mAH.

Figure 5-2. The design of power-saving circuit and sleep-mode procedure for ADL sensor

5.1.2        Integrating the ADL sensor with bed-centered telehealth system

Figure 5-3 shows the system structure of the extended BCTS. The BCTS structure is extended based on the original WhizPAD system. The sensing data collected will be transmitted to the BDP of BCTS though Zigbee wireless data transmission. The temperature and humility data will be transmitted to BDP every 1 min, and the PIR and CT sensing data will be transmitted to BDP if the sensor is triggered. All the ADL sensing data will be stored in the SD card for data storage and management. The remote caregivers can also access the BDP to browse the ADL monitoring data and receive abnormal event alert from mobile devices.

Figure 5-3. The system structure of extended bed centered telehealth system

By typing a series of IP address and the specific string of the ADL sensor on the web page, the real-time data and historical record can be displayed. Figure 5-4 shows the result of real-time monitoring data from ADL sensors displaying on the web page when the BDP was accessed. The user interface of ADL sensors is integrated with the WhizPAD App. The interface displays real time monitoring data of temperature, humidity, body activities of specific location and appliance use situation, as shown in Figure 5-5. The real time data refreshes every 6 second, and the total count of the real time data of body activities and appliance usage will be displayed every 10 minutes. The historical data can also be shown in graphic. Figure 5-6 shows the historical data of TV usage all day. The sensor title can be edited by the users in this user interface according to the location of ADL sensors.

Figure 5-4. The result of real-time monitoring data from ADL sensors displaying on the web page

Figure 5-5. The real time monitoring data of ADL sensors displaying on the user interface of App.

Figure 5-6. The historical data of TV usage all day

Using simple, low-cost and less diverse sensors modality, the passive infrared (PIR) and the current transformer (CT) were adopted in the home ADL sensors. The PIRs can detect active movement related to mobility and the CTs can detect the usage of electrical home appliances of particular interest associated with daily activities. Based on the bed-centered telehealth system, the home ADL monitoring system was installed in a real home environment for long-term monitoring the ADLs of an elder person living alone. The subject was not aware of the system operation and thus this system did not interfere with the daily livings of the elderly subject. The subject did not report any discomfort brought by the system. The ZigBee WSN technology offers reliable ADL data delivery and it also simplifies complex instrument setup in a home environment. Compounding the above advantages, this system can be suitable for home use.

5.2    Integrating the motion sensing carpet, WhizCarpet

Age-related loss of strength contributes to impaired mobility and increases the risk of falls. Approximately 28-35% of people aged of 65 and over fall each year increasing to 32-42% for those over 70 years of age [World Health Organization, 2008]. Twenty to 30 percent of those who fall suffer injuries that reduce mobility and independence and increase the risk of premature death. In addition, depression, fear of falling, and other psychological problems are common consequences of repeated falls [De Ruyter et al., 2007].

Monitoring of activities and fall events of the elderly people is the one of important technologies developed for ambient assisted living (AAL). Many sensing technologies been developed for monitoring activities and fall events for home users, including the accelerometry-based wearable sensor, RFID sensing technology, passive infrared (PIR) sensors, and camera application in image and voice recognition etc. Figure 5-7 shows the accelerometry-based wearable device. By detecting the change of acceleration, the wearable device is used to detect activities and fall event of users [Klingbeil et al., 2008; Yang et al., 2009]. Kaushik et al. proposed a method which setups PIR sensors in home environment for monitoring the activities and revealing changing trends in their health status [Kaushik et al., 2006]. Petrushin et al. [2006] presented the multiple camera indoor surveillance project, which is devoted to using multiple cameras, agent-based technology and knowledge-based techniques to identify and track people and summarize their activities.

Figure 5-7. The accelerometry-based wearable device [Klingbeil et al., 2008]

Some researches integrated sensing components into floor for long-term monitoring with minimal disturbance or discomfort in these years. Liau et al. [2007] proposed the sensory floor integrating load cell units for determining inhabitants' locations and tracking their movements. Morgado et al. [2012] presented the sensory floor equipped resistive pressure sensors for detecting footsteps of users at home, as shown in Figure 5-8. Through a careful consideration of user preferences, the sensing carpet is developed for easy installation and use for home users. Savio et al. [2007] presented an innovative approach of embedding electronics in textiles and interweaving them inside a carpet for footstep tracking. This sensor is terminated to the I/O of a 16-bit microcontroller that has 4 UART’s. A regular mesh network connecting to computer is formed by interconnecting the UART’s of 120 such constructions with a separation gap of 5 cm between the sensors. Each sensor has own number and X/Y-coordinates. When the sensor is activated, the location of sensor can be display on the computer interface to show the user location at home, as shown in Figure 5-9.

Figure 5-8. The sensory floor equipped resistive pressure sensors[…]

Figure 5-9. The smart carpet for footstep tracking[…]

5.2.1        The development of motion sensing carpet, WhizCarpet

In collaboration with the same manufacturer, SEDA Company, a motion sensing carpet named WhizCarpet is developed for tele-monitoring of mobility level, indoor locations and fall events in an unobtrusive way for older adults in the home environment [Chang et al., 2014]. The motion sensing carpet, WhizCarpet, is developing for mobility, fall event and localization tele-monitoring of elderly people. In this study, WhizCarpet is integrated with bed-centered telehealth system for pilot application.

WhizCarpet is composed of 30cm×30cm “puzzle floor mat” modular units, which can be assembled freely into any size and shape according to the setup of the home environment, as shown in Figure 5-10. Instead of adding sensing components to the carpet, the puzzle floor mat unit itself is designed into a motion sensor. The working principle is similar to that of a membrane switch. Once a WhizCarpet unit is under pressure, the top and bottom layers make contact with each other. Different pressure will create different contact quality and therefore generates different resistance. Figure 5-11 shows the relationship between applied pressure and output voltage of WhizCarpet.


Figure 5-10. The motion sensing carpet, WhizCarpet

Figure 5-11. The relationship between applied pressure and output voltage of WhizCarpet

The unit of WhizCarpet imbeds a microprocessor as the brain for data collection, analysis and transmission. When the unit is under pressure, the unit will transmit the sensing data to the master of WhizCarpet units. The master of WhizCarpet units connects to a microprocessor (Arduino Mega 2560) and Zigbee data transmission module for data communication. The sensing data could be transmitted, analyzed and stored in bedside data processor of WhizPAD for remote application. The two main features of WhizCarpet are described below:

Ÿ   Data communication

Using the I2C (Inter - Integrated Circuit) as the data communication method, the units of WhizCarpet can share the data among them. A unit has to be the master to collect the data from units as slaves, and can also request to the slaves. Figure 5-12 shows the I2C data communication. I²C uses only two bidirectional open-drain lines, Serial Data Line (SDA) and Serial Clock Line (SCL), pulled up with resistors. The I²C bus speed is the 100 kbit/s.

Figure 5-12. The I²C data communication

Figure 5-13 shows the I²C bus connection of WhizCarpet units. Each unit uses the same power from a DC adapter (5V/2A). The red unit is the master in whole system, and blue units are slaves. When the unit connects each together, the I²C Bus (yellow line) of WhizCarpet can be built. The number and location of WhizCarpet units can be adjusted according to the setup of the home environment

Figure 5-13. The I²C bus connection of WhizCarpet units

Ÿ   Auto-mapping algorithm

The auto-mapping algorithm is developed for no-specific connection of WhizCarpet units according to the Breadth-First Search [Thomas, et al. 2001] and First In - First Out [Robert L. Kruse & Alexander J. Ryba. 1998] algorithms.

Figure 5-13 shows the description of Breadth-First Search algorithm. Node 1 is the first node to search if any neighbor node exists. As Node 2 and 3 are found, they replace Node 1 to search new nodes. Node 2 does not find any new node, so the search process of Node 2 ends; Node 3 found Node 4, so the Node 4 turns to find the new one. The search process stops as there no new node appears, and the map of all nodes can be found.

A WhizCarpet unit represents a node and has a connector on the each side. Applying Breadth-First Search in auto-mapping, the procedure starts from the master unit and follows the search sequence to search new units. The search sequence is from the upside, right side, downside, to left side. The new unit found will be asked to search for the next unit. The process will be repeated until there is no new unit found. Therefore, all units and their corresponding locations can be found. Figure 5-14 shows the mapping process of WhizCarpet units. The unit has its own number written in microprocessor. Starting from the unit 1 (master), the unit 4 is the first unit found, and unit 7 is the second unit found. According to the “First In - First Out” algorithm, unit 4 is used to find the next units, then unit 7 is used next. In the second round, unit 4 can find unit 6 and 9, and unit 7 can find unit 2. This process repeats until the last unit is found. Finally, these units found will form an array to represent the numbers and locations of units. A zero in the array means there is no existing unit. The size of the array is not limited, and depends on the number of units.

Figure 5-13. The description of Breadth-First Search algorithm

Figure 5-14. The mapping process of WhizCarpet units

5.2.2        Integrating the WhizCarpet with bed-centered telehealth system

Figure 5-3 shows the BCTS structure integrating with WhizCarpet, which is the same with integrating ADL sensors. By integrating the Zigbee data transmission module, WhizCarpet system can be integrated with the bedside data processor of WhizPAD for the tele-monitoring of localization, mobility and fall event. The sensing data will be transmitted to the BDP as WhizCarpet is activated. Real-time and historical data will be stored in the SD card for data storage and management. Remote caregivers can also access the BDP to browse the monitoring data of WhizCarpet and receive abnormal event alarm by mobile device.

As the power of WhizCarpet system is turned on, the system will start the mapping processing automatically. By typing a series of IP address and the specific string of WhizCarpet system on the web page, the result of mapping can be display on the web page of BDP as shown in Figure 5-15; Figure 5-16(a) shows the result of units under no applied pressure on web page. Figure 5-16(b) shows the result of pressure distribution when the units are under applied pressure on web page.

Figure 5-15. The result of mapping process on web page

Figure 5-16 (a). The result of units under no applied pressure on web page (b). The result of pressure distribution when the units are under applied pressure on web page

Figure 5-17 shows the mapping result and activated units of WhizCarpet on the user interface of the App. If there is applied pressure on units, units on interface will become red; if there is no applied pressure, units are blue. The interface will displays the units which is under pressure, and refresh every 2 seconds. In this pilot development, the fall event is determined according to the applied pressure distribution on WhizCarpet units. When the light pressure (< 0.4 kg/cm2) extends on large, continuous areas (> 3 units), the fall event is determined. Figure 5-18 shows the real-time monitoring of mobility and fall event on App.


Figure 5-17. Activated units of WhizCarpet on App

Figure 5-18. The real-time monitoring of mobility and fall event on App

In this pilot application, WhizCarpet is a puzzle floor mat which is capable of motion sensing. Considering user acceptance, WhizCarpet is unobtrusive, easy to use, low cost, and be a natural part of the home environment. Integrating with the bed-centered system, WhizCarpet has greater potential to facilitate long-term monitoring of mobility monitoring and fall event. WhizCarpet is now in commercialization process.


Heikkinen, R. L., 1998. The role of physical activity in healthy ageing. World Health Organization- Ageing and Helth Programme.

Shimada, H., Ishizaki, T., Kato, M., Morimoto, A., Tamate, A., Uchiyama, Y., Yasumura, S., 2010. “How often and how far do frail elderly people need to go outdoors to maintain functional capacity?” Archives of Gerontology and Geriatrics, Vol. 50, pp. 140-146.

Katz, S., Akpom, C., 1976. “A measure of primary sociobiological functions”, International Journal of Health Service, vol. 6, no. 3, pp. 493-508.

Katz, S., Downs, T. D., Cash, H. R., Grotz, R. C., 1970. “Progress in development of the index of ADL”, The Gerontologist, pp. 20-30.

Caspersen, C.J., Powell, K.E., Christenson, G.M., 1985. “Physical activity, exercise and physical fitness: Definitions and distinctions for health-related research,” Public Health Rep, Vol. 110, pp. 126-131.

Celler B.G., Lovell, B.G., Earnshaw W. A., Ilsar E. D., Betbeder-Matibet L., 1994. “Development of an integrated system for remote monitoring of health status of the elderly at home,” Biomedical Engineering, Application, Basis, Communications, Vol. 6:6, pp. 919-926.

Franco, G.C., Gallay, F., Berenguer, M., Mourrain, C., Couturier, P., 2008. “Non-invasive monitoring of activities of daily living of elderly people at home- a pilot study of the usage of domestic appliances,” Journal of Telemedicine and Telecare, Vol. 14, pp.231-235.

Yang, C. C., Hsu, Y. L., 2012. “ Journal of Clinical Gerontology and Geriatrics, Vol. 3, No. 2, pp. 97-104.

World Health Organization, 2008. WHO Global Report on Falls Prevention in Older Age”.

De Ruyter, B., Pelgrim, E., 2007. Ambient Assisted Living research in CareLab,” ACM Interactions, Vol. 14.

Kaushik, A.R., Celler B.G., 2006. Characterization of Passive Infrared Sensors For Monitoring Occupancy Pattern,” in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

V. A. Petrushin, G. Wei, A. V. Gershman., 2006. Multiple-camera people localization in an indoor environment,” Knowledge and Information Systems, vol. 10, no. 2, pp. 229-241.

Liau, W.H., Wu C.L., Fu L.C., 2008. Inhabitants Tracking System in a Cluttered Home Environment Via Floor Load Sensors,” Automation Science and Engineering, IEEE Transactions on Volume 5, pp. 10-20.

Morgado, J.M.A., Konig, A., 2012. Low-power concept and prototype of distributed resistive pressure sensor array for smart floor and surfaces in intelligent environments,” Systems, Signals and Devices (SSD), 2012 9th International Multi-Conference on, pp. 1-6.

Savio, D., Ludwig, T., 2007. Smart carpet: A footstep tracking interface,” In: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops, Vol. 2, pp. 754-760.

Chang, K. W., Hsu, Y. L., Lin, C. K., Liu, Y. W., Chang, W. Y.,  2014. Development of a Motion Sensing Carpet for Multiple Interactive Applications,” Gerontechnology, 13(2).