The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010
Development of a Monitoring System for Bathers Using Ultrasonic Wave Sensor Masaki Matsumoto†1, Hiroyuki Agatsuma2 and Takehiko Abe3 Kanazawa Institute of Technology 3-1 Yatsukaho, Hakusan, Ishikawa 924-0838, Japan Email:
[email protected] [email protected] [email protected] Takuya Tajima Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka, 811-0295, Japan Email:
[email protected] Haruhiko Kimura Kanazawa University Kakuma, Kanazawa, Ishikawa 920-1192, Japan Email:
[email protected]
Abstract - This paper proposes a monitoring system which is able to detect the elderly person’s abnormalities in a bathtub. We improve an existing monitoring system which was developed in the past. Recently in Japan, the number of the elderly people who died in accidents while soaking in a bathtub, especially the apparent drowning accidents, has been increased. According to the Ministry of Health, Labour and Welfare vital statistics, the elderly people sixty-five and over accounted for more than eighty percent of the whole victim. Cause of such accidents is a disturbance of consciousness caused by fluctuations in blood pressure while soaking in a bathtub. The number of such unexpected accidents is rising steadily every year. In this context, it is necessary to develop a monitoring system which can detect the elderly person’s abnormalities in a bathtub. Early detection is especially of vital importance for the successful lifesaving of a bather. In order to develop a monitoring system, we use an ultrasonic wave sensor which does not affect the human body. In addition to the function of detecting abnormalities for preventing drowning, the system can detect bather’s fall in a bathtub. This system targets mainly the elderly person who lives alone. By using this system, we are able to find accidents immediately.
Keywords: Ultrasonic wave sensor, Drowning prevention, Monitoring system, Fall detection, Bathtub.
1. INTRODUCTION This paper reports the improvement of the existing monitoring system for detecting a bather’s abnormality [Onishi 2006] [Dobashi 2009]. To be concrete, we improve accuracy of detecting bather’s abnormality, and we develop new function of detecting bather’s fall in a bathtub. According to White paper on Aging Society, Edition 2009, Japan, the population of elderly people sixty-five and over is 22.1 percent of the total population of Japan. Japan
________________________________________ † : Corresponding Author
will be undoubtedly the real aged society in the future. The ratio of the elderly people has been increasing every year. Moreover, it will be 25.2 percent in 2013, 33.7 percent in 2035, and 40.5 percent in 2055. According to statistics in 2007, the ratio of one-person household of the elderly person sixty-five and over is 22.5 percent. After this, we can predict that the number of one-person households of the elderly person will increase more and more. Recently, there are many issues that report bathing accidents of elderly people, especially in a bathtub. These
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 articles point out several problems concerning drown elderly people. The number of deaths in a bath is estimated over 14,000 in a year. According to the ratio of the dead elderly people in a bathtub from 1995 to 2001, the ratio has been over 80 percent since 1996 [Harimoto 2004]. Eighty percent of aged people take a bath alone. The main causes of death in the bathtub are heart disease, cerebrovascular disorder and drowning. Heart disease and cerebrovascular disorder are caused by sickness. However, drowning is caused by an accident. The preventive measures for these deaths need to be examined. In order to decrease a large number of deaths in a bathtub, there are some studies which develop a monitoring system for detecting a dangerous condition of the elderly person in a bathtub. Mr. Onishi who belonged to Kanazawa Institute of Technology developed the sensing system that can detect the bather’s snooze condition [Onishi 2006]. However the system leaves much room for improvement. Although the system uses seven ultrasonic wave sensors, it costs a great deal. Moreover, detection accuracy of a dangerous condition of the system needs to be improved. After that, Mr. Dobashi who was a graduate of Kanazawa Institute of Technology improved the system [Dobashi 2006]. He decreased the number of sensors to reduce development cost. However, the existing system was not able to reach perfection which means 100 percent detection accuracy. In this study, we develop a new monitoring system which can detect bather’s snooze behavior and fall behavior in a bathtub. We only use one ultrasonic wave sensor, which leads to the reduction of system development cost .
maximum area (Figure 1). The system uses the voltage value obtained by the sensor to calculate “Distance” and “Degree of Change”, which are explained in section 2.4. Table 1: The specification of an ultrasonic wave sensor. Measuring length
400-3,000(mm)
Obtained value
0.000-5.000(V)
Minimum detectable size of object
50*50(mm)
Figure 1: Range of sensing area of an ultrasonic wave sensor (mm).
2.2 Function of System 2.2.1 Definition of Posture We define bather’s three postures in a bathtub. Posture A (Figure: 2, 3) means a safe state that bather sits with straightening back. Posture C (Figure: 6, 7) is a dangerous state that bather’s face is near the surface of water. The Posture B (Figure: 4, 5) is defined halfway between Posture A and Posture C.
2. PROPOSAL SYSTEM 2.1 An Ultrasonic Wave Sensor A sensor used in this study is UD-330 which is an ultrasonic wave displacement sensor made by KEYENCE CORPORATION. Amplifier unit is UD-300SO (5171). The equipment is used for smoothing process which is explained in section 2.4. Table 1 shows specification of the ultrasonic wave sensor. The sensor can obtain the reflection time and enables us to measure the distance from the sensor to a target object. Electric current value of 0-20mA obtained by the sensor corresponds to the measuring length of 400mm-3,000mm. The controller converts electric current value to a voltage value. The controller is equipped with interface unit (NR-500) and high-speed analog measurement unit (NR-HA08). The sensing area of the sensor closely resembles an object like a cone which has a circle of 400mm in diameter at minimum area and a circle of 650mm in diameter at
Figure: 2 Definition of Posture A (illustration).
Figure: 3 Definition of Posture A (picture).
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 Posture A-C and Posture B-C are defined as same as Posture A-B. (2)Fall, Bathing Motion Fall motion (Figure: 8(a ~ c)), which is a very dangerous motion, is defined that a bather stands and then falls forward. Figure: 9(a, b) shows that a bather goes into a bathtub for soaking, and Figure: 10(a ~ c) shows that a bather goes out of a bathtub. Figure: 4 Definition of Posture B (illustration). (3)A Chain of Motions A Chain of Motions is defined as follows. Into a bathtub --> Posture A --> Out of a bathtub --> Into a bathtub --> Posture A --> Posture A-C Table 2 shows a relationship between definitions of bather’s condition (posture and motion) and risk.
Figure: 5 Definition of Posture B (picture).
(a)
Posture A (picture).
Figure: 6 Definition of Posture C (illustration).
(b)
Standing in a bathtub. (c) Fall. Figure: 8 Fall in a bathtub.
(a)
Standing outside a (b) Into a bathtub. bathtub. Figure: 9 Into a bathtub.
Figure: 7 Definition of Posture C (picture).
2.2.2 Definition of Motion Three kinds of motions are objects of detection. (1)Snooze Motion A Snooze Motion means that a bather moves up and down the upper part of body. Posture A-B is defined a motion that a bather moves repeatedly between Posture A and Posture B. The motion is repeated by a fixed speed.
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010
(a)
Posture A (picture).
Shawe-Taylor 2000]. It uses a margin, which means minimum distance between hyper plane and training samples, as evaluation function. Our study needs binary classification. Therefore, we use a single SVM. When SVM is unable to separate the observation data into linear space, it would realize the linear separation by mapping data to higher dimensional space through non-linear conversion. The method for greatly reducing the amount of calculation is called the kernel trick. There are various kinds of kernel functions such as a linear, polynomial, sigmoid, and gaussian. Although SVM performance is said to be affected by selection of kernel and turning up the optimum parameters, the effective method for selecting the kernel has not yet been established [Tsuda 2000]. In this study, we used the gaussian kernel because the number of parameters of the gaussian kernel is small. The Eq. (1) means the gaussian kernel. K (x, x’) is the kernel function, x and x’ are input data, and γ is the dimensionality of data. K (x, x’) = exp (- γ |x-x’| 2)
(1)
2.4 Distance and Degree of Change (b)
Standing in a bathtub. (c) Out of a bathtub. Figure:10 Out of a bathtub. Table: 2
Relationship between definition of condition and risk.
Condition
Risk
Posture A-B
Normal
Posture A-C
Abnormal
Posture B-C
Abnormal
Into a bathtub Out of a bathtub Fall
Normal
Normal Abnormal
Explanation Low risk of landing on the water High risk of landing on the water High risk of landing on the water
In this section, we explain “Distance” and “Degree of Change”. The ultrasonic wave sensor can measure the distance from a sensor to a target object. The range is 4003000mm. The controller converts the distance between the sensor and a target object to a voltage value which is ranged from 0.000V to 5.000V. The distance value is a real number. Sampling period of the ultrasonic wave sensor is 10ms. The ultrasonic wave sensor obtains motion data with smoothing process. The smoothing process means a calculation for moving average of distance value which is defined as “Distance” in this study. “Distance” is determined by averaging the recent past 99 distance values and the current 1 distance value. In this, “Distance” means an average of distances in the past three seconds. “Distance” is calculated by using Eq. (2).
Taking into a bathtub Going out of a bathtub Fall in a bathtub
2.3 Method of state identification We use the support vector machine (SVM) to identify the bather’s state, because the SVM is said to be useful to identify an object with posture change [Cristianini and
n i 1
avg =
yi
(2)
n
The avg shows the moving average. y is the obtained distance(mm) from an ultrasonic wave sensor. n is number of data in the past three seconds. We also obtain “Degree of Change” which is calculated by using Eq. (3). a=|
n
n i 1
n
n
xi y i n i 1
x
i 1 2 i
(
xi n i 1
n i 1
xi )
2
yi
|
(3)
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 The a is “Degree of Change” which means the slope of the data. x is time(ms) and y is the obtained distance(mm) from an ultrasonic wave sensor. n is number of data in the past three seconds. Figure:11 shows how to obtain data for three seconds (Figure: 11(a~b)).
3.2 Experimental method The number of subjects is seven. They are grown-up men. (1)A Snooze Motion The time length of 1 record of learning data of Posture A-B, A-C and B-C is three seconds. Each motion is repeated 33 times by a subject. Therefore total 99 records are acquired per subject. Posture A-B -->
Posture A-C -->
Posture B-C --> …
Each motion is performed by turns every 10 second during 150 seconds. We obtain three seconds data every 2 seconds as test data. Figure:13 shows how to obtain test data of snooze behavior. Figure: 11 Obtained data of snooze behavior. In the experimental bathroom, when the ultrasonic wave reflects a wall of bathroom, distance value shows 2.580V. Therefore we define that threshold value is 2.500V.
3. EXPERIMENT 3.1 Experimental environment Figure:12 shows overview of experimental environment. Experimental conditions are as follows.
Room temperature: 22℃ High humidity: 70% Water temperature: 42℃ Ventilating fan: No
Figure: 13 Obtaining data of safe behavior or snooze behavior. (2)Fall, Bathing Motion Sampling period of learning and test data of Fall and Bathing Motion (into and out of a bath) is 10 seconds. Each motion is repeated five times by a subject. Therefore total 15 records are acquired per subject. (3)A Chain of Motions A subject performs a chain of motions five times. Each motion such as Into a bathtub, Posture A, Out of a bathtub, etc. is performed during 10 seconds, respectively.
3.3 Experimental results Table: 3 Result of state identification and ratio .
Figure: 12 Experimental environment (mm).
The 11th Asia Pacific Industrial Engineering and Management Systems Conference The 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, 7 – 10 December 2010 Table:3 shows experimental results of state identification and ratio. In the Table:3, N and A mean normal and abnormal respectively.
4. CONSIDERATION All identification ratio of a risk state of every motion exceeds over 90.0 %. This means high usability of the system. However, identification ratio of a safe state is a little low, because a risk state data is similar to a safe state data. Therefore the system still leaves room for improvement.
REFERENCES Onishi, Y., Abe, T., Kimura, H., Nambo, H. and Ogoshi, Y., Development of Abnormality Detection System for Bathers Using Ultrasonic Sensors, IEEJ Trans.SM, Vol.126, No.12, 2006, pp.662-668. Dobashi, H., Tajima, T., Abe, T., Nambo, H. and Kimura, H., Improvement of Abnormality Detection System for Bathers Using Ultrasonic Sensors, IEEJ Trans.SM, Vol.129, No.1, 2009, pp.6-13. Cabinet Office in Japan, White Paper on Aging Society, Edition 2009, Gyosei, 2009, pp.2-6. Harimoto, K., An Examination of Elderly People’s Death from Drowning in Their Home Bathtubs, The bulletin of Osaka College of Social Health and Welfare, No.2, 2004, pp.56-59. Cristianini, N. and Shawe-Taylor, J., An introduction to support vector machine and other kernel based learning methods, 2000, Cambridge University Press. Tsuda, K., What is support vector machine, IEICE Transactions, Vol.83, No.6, 2000, pp.460-466.
AUTHOR BIOGRAPHIES Masaki Matsumoto is a student of Kanazawa Institute of Technology in Japan. He engages in research on welfare engineering. Hiroyuki Agatsuma is currently a student of Graduate School of Engineering, Kanazawa Institute of Technology. He is a student member of the Japan Society for Welfare Engineering.
5. CONCLUSION In this study, we developed a monitoring system which can detect bather’s abnormalities in a bathtub. We can use only one ultrasonic wave sensor, which enables us to reduce cost of the system. The detection system for early stage accidents can give a major impact on the welfare sector, because it can reduce the risk in aged society. As for future policy, we intend to increase the number of data, motion patterns and subjects in order to improve the system with higher accuracy.
Takehiko Abe received his BA and PhD degrees from Kanazawa University, Japan in 1988 and 1997 respectively. He joined Daiwa Institute of Research Ltd. in 1988. He is currently a professor at Kanazawa Institute of Technology, Japan. His research interests include data mining and management information. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE), the Japanese Society for Artificial Intelligence (JSAI), the Japan Society for Production Management (JSPM) and the Japan Society for Management Information (JASMIN). Takuya Tajima received his master's degree from Graduate School of Managerial Engineering, Kanazawa Institute of Technology in 2003. Also in 2003, he joined FSAS Network Solutions Inc. In 2008, he completed his doctoral course in the Graduate School of Natural Science and Technology, Kanazawa University, Japan. He holds a PhD in Engineering. He was a research associate in Department of Electronics and Information Engineering, Ishikawa National College of Technology, Japan in 2007. Currently, he is an assistant professor in Fukuoka Institute of Technology, Japan. He is a member of JSAI, JSPM and Japan Industrial Management Association (JIMA). Haruhiko Kimura completed his doctoral course in Information Engineering in the Graduate School of Engineering at Tohoku University in1979. He holds a PhD in Engineering. Also in 1979, he joined Fujitsu Corporation. In 1980, he became a lecturer in Kanazawa Women's Junior College. In 1984, he became an assistant professor in the Economics Department of Kanazawa University. Currently, he is a professor in the Graduate School of Natural Science and Technology of Kanazawa University. At this time, he engages in research on optimal code conversion and the acceleration of production systems. He is a member of IEICE, JSAI, JSPM and the Information Processing Society of Japan (IPSJ).
