- Email: [email protected]
A Multisensor Communication Network Applied to the Monitoring of the Elderly
Copyright Cl IFAC Intelligent Components and Instruments for Control Applications, Annecy, France, 1997
A MULTISENSOR COMMUNICATION NETWORK APPLIED TO THE MONITORING OF THE ELDERLY
Group of research ICARE of the Technology University Institute, 1 Place Georges Brassens, B.P. 73,31703 Bl.AGNAC, France, Tel: (33)0562747575 Fax: (33)0562747576 email: {campo,val}@iut-hlagnacfr J
Laboratory ofAnalysis and Architecture ofSystems of the CNRS, 7 Av. du Colonel Roche, 31077 TOULOUSE Cedex, France, Tel: (33)0561336200 Fax: (33)0561336208 email: {campo,chan}@laas.Jr 2
3
INSERM CJF 94-06, 37 Allees Jules Guesdes, 31073 TOULOUSE Cedex, France, Tel: (33)0561145951 Fax: (33)0561336208
Abstract: So as to improve the living quality and to prevent the health risks of the elderly and disabled, a non intrusive monitoring multisensor system has been developed. The originality of this assistance is based on the knowledge of the persons' living habits, which involves first a "learning" stage, then the comparison of this database with the real situation. The result of this processing is then analyzed to detect an unusual situation and thus to set off an alarm. In order to do that, a comparative study of the existing sensors and systems of communication has allowed us to define the technical specifications that such a system has to possess with respect to the functionalities retained for our application. In this paper, we present an intelligent multisensor system of communication providing data necessary for learning about the living habits of the elderly and for using some expert rules to determine if the situation of the elderly person is dangerous. Keywords: multisensor system, communication networks, neural networks
achieved in the data processing and electronics areas, interesting technological solutions can be contemplated. In order to do so, it seems necessary to associate various complementary skills in sensors, in systems of communication and in information processing. These fields of competence, close to those necessary for home automation are here used to add a new functionality : non intrusive behaviour monitoring of the elderly. This paper presents an intelligent multisensor system of communication providing data necessary for learning about the living habits of the elderly.
1. INTRODUCTION
In most industrialized countries, the question of population ageing appears as a crucial issue and is of major importance in the current socio-economic context. Thus, even if, over the last few years, many manufacturers have been launching out into this potential market, developing products of comfort, security and new services such as remote monitoring or telemonitoring, it has remained difficult to monitor efficiently the psychological and behavioural state of the elderly at home or in specialized institutions. Considering the progress
439
2. PRINCIPLE AND METHODOLOGY
3. SENSORS AND COMMUNICATION NETWORK
The work aims at studying and realizing a multisensor system of data collecting and processing in specialized institutions or at home. Entirely automated.. by collecting and analyzing a certain number of parameters, the system has to be able to detect the presence/absence of an old person in his living sphere, to evaluate his mobility and be informed of a fall as fast as possible. According to the WHO Work group on the Prevention of Falls by the elderly (Vellas and Albarede, 1993), "a fall is an event which results in a person coming to stay inadvertently on the ground or other lower level and other than as a consequence of the following : sustaining a violent blow ; loss of consciousness ; sudden onset of paralysis, as in a stroke and an epileptic seizure" ; approximately one third of elderly people over the age of 65 who live in the community fall each year. Some falls result in a fracture, others result in serious injuries requiring medical care (Dargent-Molina and Brem, 1995). The fall can happen at any time, by day-time or by night, more often by night, and falls by night appear more dangeiOus than by day-time (Vellas and Albarede, 1993). An automatic detection software can be great help for the family or the staff allowing them to keep contact with the elderly person without being in the same room. Thus, the purpose of the multi sensor system proposed and studied is not to replace entirely the nursing staff but to give them reliable and active help, especially at night-time. The first principle used is that of "learning" about the evolution of some behaviour in a social and physical environment. Processing methods stemming from Artificial Intelligence (AI) (especially Artificial Neural Network (ANN) (ChaD, et aI., 1995b), allow us to learn about people ' s habits. One of the advantages of neural networks is that they permit "black box" processing, with non parametric learning and that they are able to modelize any system without any foreknowledge of its internal functioning. What is just needed is a set of input/output data picked up in the elderly's daily life that are characteristic of his habits and representative of the task to modelize. This set of data constitutes the database for learning (ChaD, et al., 1996). The second principle used is the real time data combined with expert rules to decide if the elderly people need some help in case of fall, run away, etc. This is why it has to be reliable and precise. This requires choosing and testing rigorously the various elements making up the global system.
So as to provide the processing system with as much information as possible without overloading the network, a judicious number of sensors is used. It must be reminded that the system is meant to be non intrusive. This excludes badges, pendants and other devices requiring personal actuation. Our choice has focused on widely spread sensors. Infrared barriers are used to detect people passing through strategic zones (passing through a door for example). Magnetic make contacts enable to know the open/closed state of doors and windows. Passive infrared sensors which are used to detect motion are based on a displacement area rectangular coverage as shown in figure 1 and allow us to know the person's position in the room and to follow the evolution of his mobility. Finally, other specific sensors allow us to check the On/Off state of lights, the switching on of a TV set and the presence of a person on his bed. Altogether, about twelve sensors are used for an experimental room composed of a bedroom and a bathroom. Caring for homogeneity, "All Or Nothing" (AON) sensors have been used to avoid analog sensors which are more delicate and more expensive to interface (Campo, et al., 1996). With respect to the functionalities retained for the application : presence/absence, mobility and fall , it is thus necessary to insure fast updating of the information coming from the sensors, especially with infrared barriers and passive infrared sensors hanging from the ceiling, so as to avoid information loss.
Fig. 1. Experimental room with detection zones
440
ETHERNET '0 MSPS
To do so, the sensors must have specific features. In particular, the motion sensors (IR.) must have a fast response-time and a very short keep-time. The passive infrared sensors used for the experimentation are pyroelectrical sensors with a very short keep-time « 1 s) compared with the classical anti-intrusion sensors which have a keeptime of about 5 times higher, and with a zoom and lens system which allows to define very precisely « 1 cm) the detection area. So, considering that the motion of a person is at about of 1 mls, the interrogation of these sensors is performed every one second. Intended to be installed in a specialized institution composed of several identical rooms, the system is of course based on a communication network. This solution presents various advantages especially far more simple cabling. Our choice has focused on a system of data collection on several levels partly based on a client-server solution. Concerning collection itself, it is achieved by a server microcomputer performing a cyclical retrieval from every sensor. This computer is connected to every sensor by a field bus based on InJOut ADAM modules, make ADVANTECH (Advantech, 1996a). Modules proposing eight AON entries have been mainly used. These modules can be connected to a communication bus, type RS485 . The maximum flow proposed is 38400 bps. The collecting PC has a bridge allowing it, via a fast series port, to be connected to a RS485 bus (Desodt, 1996). To have an estimation of the response-time of the network (sensors + modules + bus), some 500 measurements have been realized at a flow of 9600 bps. The average time between the request and the answer is of 15 ms and so more less than collection time. An application of data collection visualization and safeguard is developed by using the same maker's GENIE software (Advantech.. 1996b). So as to insure fast parallel data processing and an analysis with neural networks, the collecting and processing functions have been separated and ported into two distinct PCs. The advantage of this solution is that the CPU time of the server PC is only devoted to collecting the data provided by sensors. According to the number of rooms and sensors, it is thus easy to increase the number of collecting PCs. Still thanks to GENIE, a client-server application has been developed. An ETHERNET network with a flow (10 Mbps) higher than that proposed on the RS485 bus is used. The data collecting PC is the server for the processing client PC (figure 2). The client PC receives from one or several PC servers information in real time and can devote all its CPU time to data processing by neural networks. This solution also presents other
ACOUlSITION
AND SUPERVISION
.~~ .::.~ DIGITAl INPUT
as
ANAlOG INPUT
ANAI..OG INPUT
Fig. 2. Global system with client-server application advantages such as the possibility to deport the processing PC from the collecting zone, by using a local area network. Our method is presently improving in order to offer the possibility to centralise alarms by WAN system. Several local sites could thus be controlled from a distance (several tens or several hundreds of kms) by a unique service beneficiary. In this case, the necessary minimum baud rate is relatively low « 10 kbps) to insure the transfer of alarms. Now, the studies are focused on the interface between our home automation system of data collection and the WAN TRANSVEIL2G system proposed by FRANCE TELECOM.
4. DATA PROCESSING AND APPLICATION When boosting the monitoring procedure, the signals emitted by the sensors excited by the elderly either directly (TV, light switching) or indirectly (motion sensor) are transmitted to the monitoring algorithm managing computer by means of the communication interface. These raw data or observations will either be stored for deferred-time processing for learning (elaboration of the learning stage), or will be immediately realtime processed in the case of a decision making about a fall or a fugue (in the daily use phase) as shown in figure 3. Thus, data collected in real time constitute a real basis for the processing Pc. Three stages are planned in the implementation of the system : - the learning stage where data obtained by a questionnaire and way of life direct observation are submitted to the neural network and memorized in the form of synaptic weights: a historical account is thus created (with schedules and frequency), - the generalization stage where the diagnosis of habit changes is deduced from a procedure of data classification (expert system), - the personalization stage where the user is offered the possibility to alter and adjust the system himself
441
Table 1 Data collected from 51 : bathroom light S3 : IR sensor #3 S5 : bed sensor S7 : IR sensor #5
+
~::-b U, .
Loc.~onl- -: In' uu
File
Flk:
1
R U : ,"L.a~ront.I~
time
I
re.1
22:25:15 22:25:16 22:25:17 22:25:18 22:25:19 22:25:20 22:25:21 22:25:22 22:25:23 22:25:24
_I
1= 1
.
a simulating situation 52 : bedroom light S4 : IR sensor #2 S6 : IR sensor # 1 S8 : IR sensor #4
SI S2 S3 S4 S5 S6 S7 S8 o 000 1 000 o 0 0 0 100 0 o 0 100 000 o 1 0 0 0 0 0 0 o 0 1 0 0 0 0 0 0 0 0 000 0100000 o 000000 0110000 0100000
Fig. 3. Learning and test phases according to his satisfaction degree (Chan, et aI., 1995a). This diagnosis system thus completes the multisensor system. Indeed, with some knowledge on the elderly people behaviour and way of living, rules can be laid down, the real time data confronted to these rules allow to decide if a situation is normal or not. Thus, the fall will be able to be detected as an interruption of mobility... without any instrumentation on the person. The automatic detection of a fall is based on real time data collection. The period chosen to assist the elderly person is night-time. If we assume that at this period of time, everybody must be at bed, all data related to motion at night, for example between 9 pm to 7 am the next morning, are collected and processed. If the elderly person wakes up and walks in his flat to go to the bathroom or any other area and if he stays in some area of the room longer than a pre-set period of time (which can be adjusted), an alarm signal will set off. On 20 simulating situations with a person who falls in different areas of the room the results show that the system can detect a fall when it is "an event which results in a person coming to stay inadvertently on the ground or other lower level". A period of time is chosen to set up alarm for warning the family or the staff. An example of data file is shown. According to table 1, the person is resting on the ground from 22:25 :44, the timer for sensor #5 shows a period of time of 15 s of rest. The process shows an alarm signal at the end of 15 s. The fall is located in the bathroom. The light of the bathroom has not been switched on.
alarm
1 0 000 0 1 1 0 0 0 0 010 100
22:25:29 22:25:30 22:25:31
0 0 0
22:25:34 22:25:35 22:25:36 22:25:37 22:25:38 22:25:39 22:25:40 22:25:41 22:25:42 22:25:43 22:25:44
0010100 o 100 000 0 o 1 0 0 0 0 0 0 0101000 0101000 o 1 000 100 o 1 000 100 o 1 0 0 0 0 1 0 o 1 0 0 0 0 1 0 01000010 o 000000
22:25:58
0
o
0
0
0
0
0
5. CONCLUSION This paper presents the development of a data collection and processing multisensor system applied to the behaviour follow up of the elderly. The original principle consists in learning about the old person' s habits so as to diagnose a potential change in behaviour without any human intervention. In some cases, the assistance proposed can also be founded on the real time data collected with rules written by experts using what they know about the elderly people's way of living and habits to decide whether to give them help when a dangerous situation happens. The use and the adaptation of commonly used sensors, associated to a communication network from the industrial world offers a good solution to the problem at stake. The physical separation of hardware dedicated to collection and to processing allows time-saving during the collecting stage as well as during the analysis with a neural network. Some results are presented with simulating data collected on an experimental site.
442
REFERENCES Advantech (1996a). Total solution for PC-based industrial automation. Solution guide, 61, 8.18.18. Advantech (1996b). Total solution for PC-based industrial automation. Solution guide, 61, 7.27.7. Campo, E., T. Val, 1.1. Mercier and D. Esreve (1996). La surveillance multicapteurs dans l' habitat : Application la surveillance de personnes agees vivant isolees cl domicile.
a
Article submitted at Journal Europeen des Systemes Automatises (Bermes, Ed). ChaD.. M , T. Viard, ML. Caillavet and E. Campo (1995a). Smart Technology for the Elderly and Disabled Users at Home. Proceedings of the 2nd TIDE Congress of the European Context for Assistive Technology, 393-396, Paris. ChaD.. M , C. Hariton, P. Ringeard and E. Campo (1995b). Intelligent home automation system for the elderly and the disabled. Proceedings of
IEEE International Conference on Systems, Man and Cybernetics, 2, 1586-1589, Vancouver. ChaD.. M, H. Bocquet, E. Campo, T. Val, D. Esteve and 1. Pous (1996). Multisensor system and artificial intelligence in housing for the elderly and the disabled. Proceedings of Gerontotechnology Conference, Helsinki. Dargent-Molina, P. and G. Breart (1995). Epidemiologie des chutes et des traumatismes lies aux chutes chez les personnes agees. In:
Rev. Epidemiologie. et Sante Publique, 43, 7283, Paris. Desodt, P. (1996). Choisir un reseau de terrain. nOspecial, June/July, ISSN 154.845x, 35-47. Vellas, B. and 1.L. Albarede (1993). Sleep disorders and falls in healthy elderly persons. In: Sleep Disorders and Insomnia in the Elderly (B. Vellas, 1.L. Albarede, J.E . Morley & T. Roth, Eds), Vol.7, pp. 75-87. Facts and Research in Gerontology.
443
A MULTISENSOR COMMUNICATION NETWORK APPLIED TO THE MONITORING OF THE ELDERLY
Group of research ICARE of the Technology University Institute, 1 Place Georges Brassens, B.P. 73,31703 Bl.AGNAC, France, Tel: (33)0562747575 Fax: (33)0562747576 email: {campo,val}@iut-hlagnacfr J
Laboratory ofAnalysis and Architecture ofSystems of the CNRS, 7 Av. du Colonel Roche, 31077 TOULOUSE Cedex, France, Tel: (33)0561336200 Fax: (33)0561336208 email: {campo,chan}@laas.Jr 2
3
INSERM CJF 94-06, 37 Allees Jules Guesdes, 31073 TOULOUSE Cedex, France, Tel: (33)0561145951 Fax: (33)0561336208
Abstract: So as to improve the living quality and to prevent the health risks of the elderly and disabled, a non intrusive monitoring multisensor system has been developed. The originality of this assistance is based on the knowledge of the persons' living habits, which involves first a "learning" stage, then the comparison of this database with the real situation. The result of this processing is then analyzed to detect an unusual situation and thus to set off an alarm. In order to do that, a comparative study of the existing sensors and systems of communication has allowed us to define the technical specifications that such a system has to possess with respect to the functionalities retained for our application. In this paper, we present an intelligent multisensor system of communication providing data necessary for learning about the living habits of the elderly and for using some expert rules to determine if the situation of the elderly person is dangerous. Keywords: multisensor system, communication networks, neural networks
achieved in the data processing and electronics areas, interesting technological solutions can be contemplated. In order to do so, it seems necessary to associate various complementary skills in sensors, in systems of communication and in information processing. These fields of competence, close to those necessary for home automation are here used to add a new functionality : non intrusive behaviour monitoring of the elderly. This paper presents an intelligent multisensor system of communication providing data necessary for learning about the living habits of the elderly.
1. INTRODUCTION
In most industrialized countries, the question of population ageing appears as a crucial issue and is of major importance in the current socio-economic context. Thus, even if, over the last few years, many manufacturers have been launching out into this potential market, developing products of comfort, security and new services such as remote monitoring or telemonitoring, it has remained difficult to monitor efficiently the psychological and behavioural state of the elderly at home or in specialized institutions. Considering the progress
439
2. PRINCIPLE AND METHODOLOGY
3. SENSORS AND COMMUNICATION NETWORK
The work aims at studying and realizing a multisensor system of data collecting and processing in specialized institutions or at home. Entirely automated.. by collecting and analyzing a certain number of parameters, the system has to be able to detect the presence/absence of an old person in his living sphere, to evaluate his mobility and be informed of a fall as fast as possible. According to the WHO Work group on the Prevention of Falls by the elderly (Vellas and Albarede, 1993), "a fall is an event which results in a person coming to stay inadvertently on the ground or other lower level and other than as a consequence of the following : sustaining a violent blow ; loss of consciousness ; sudden onset of paralysis, as in a stroke and an epileptic seizure" ; approximately one third of elderly people over the age of 65 who live in the community fall each year. Some falls result in a fracture, others result in serious injuries requiring medical care (Dargent-Molina and Brem, 1995). The fall can happen at any time, by day-time or by night, more often by night, and falls by night appear more dangeiOus than by day-time (Vellas and Albarede, 1993). An automatic detection software can be great help for the family or the staff allowing them to keep contact with the elderly person without being in the same room. Thus, the purpose of the multi sensor system proposed and studied is not to replace entirely the nursing staff but to give them reliable and active help, especially at night-time. The first principle used is that of "learning" about the evolution of some behaviour in a social and physical environment. Processing methods stemming from Artificial Intelligence (AI) (especially Artificial Neural Network (ANN) (ChaD, et aI., 1995b), allow us to learn about people ' s habits. One of the advantages of neural networks is that they permit "black box" processing, with non parametric learning and that they are able to modelize any system without any foreknowledge of its internal functioning. What is just needed is a set of input/output data picked up in the elderly's daily life that are characteristic of his habits and representative of the task to modelize. This set of data constitutes the database for learning (ChaD, et al., 1996). The second principle used is the real time data combined with expert rules to decide if the elderly people need some help in case of fall, run away, etc. This is why it has to be reliable and precise. This requires choosing and testing rigorously the various elements making up the global system.
So as to provide the processing system with as much information as possible without overloading the network, a judicious number of sensors is used. It must be reminded that the system is meant to be non intrusive. This excludes badges, pendants and other devices requiring personal actuation. Our choice has focused on widely spread sensors. Infrared barriers are used to detect people passing through strategic zones (passing through a door for example). Magnetic make contacts enable to know the open/closed state of doors and windows. Passive infrared sensors which are used to detect motion are based on a displacement area rectangular coverage as shown in figure 1 and allow us to know the person's position in the room and to follow the evolution of his mobility. Finally, other specific sensors allow us to check the On/Off state of lights, the switching on of a TV set and the presence of a person on his bed. Altogether, about twelve sensors are used for an experimental room composed of a bedroom and a bathroom. Caring for homogeneity, "All Or Nothing" (AON) sensors have been used to avoid analog sensors which are more delicate and more expensive to interface (Campo, et al., 1996). With respect to the functionalities retained for the application : presence/absence, mobility and fall , it is thus necessary to insure fast updating of the information coming from the sensors, especially with infrared barriers and passive infrared sensors hanging from the ceiling, so as to avoid information loss.
Fig. 1. Experimental room with detection zones
440
ETHERNET '0 MSPS
To do so, the sensors must have specific features. In particular, the motion sensors (IR.) must have a fast response-time and a very short keep-time. The passive infrared sensors used for the experimentation are pyroelectrical sensors with a very short keep-time « 1 s) compared with the classical anti-intrusion sensors which have a keeptime of about 5 times higher, and with a zoom and lens system which allows to define very precisely « 1 cm) the detection area. So, considering that the motion of a person is at about of 1 mls, the interrogation of these sensors is performed every one second. Intended to be installed in a specialized institution composed of several identical rooms, the system is of course based on a communication network. This solution presents various advantages especially far more simple cabling. Our choice has focused on a system of data collection on several levels partly based on a client-server solution. Concerning collection itself, it is achieved by a server microcomputer performing a cyclical retrieval from every sensor. This computer is connected to every sensor by a field bus based on InJOut ADAM modules, make ADVANTECH (Advantech, 1996a). Modules proposing eight AON entries have been mainly used. These modules can be connected to a communication bus, type RS485 . The maximum flow proposed is 38400 bps. The collecting PC has a bridge allowing it, via a fast series port, to be connected to a RS485 bus (Desodt, 1996). To have an estimation of the response-time of the network (sensors + modules + bus), some 500 measurements have been realized at a flow of 9600 bps. The average time between the request and the answer is of 15 ms and so more less than collection time. An application of data collection visualization and safeguard is developed by using the same maker's GENIE software (Advantech.. 1996b). So as to insure fast parallel data processing and an analysis with neural networks, the collecting and processing functions have been separated and ported into two distinct PCs. The advantage of this solution is that the CPU time of the server PC is only devoted to collecting the data provided by sensors. According to the number of rooms and sensors, it is thus easy to increase the number of collecting PCs. Still thanks to GENIE, a client-server application has been developed. An ETHERNET network with a flow (10 Mbps) higher than that proposed on the RS485 bus is used. The data collecting PC is the server for the processing client PC (figure 2). The client PC receives from one or several PC servers information in real time and can devote all its CPU time to data processing by neural networks. This solution also presents other
ACOUlSITION
AND SUPERVISION
.~~ .::.~ DIGITAl INPUT
as
ANAlOG INPUT
ANAI..OG INPUT
Fig. 2. Global system with client-server application advantages such as the possibility to deport the processing PC from the collecting zone, by using a local area network. Our method is presently improving in order to offer the possibility to centralise alarms by WAN system. Several local sites could thus be controlled from a distance (several tens or several hundreds of kms) by a unique service beneficiary. In this case, the necessary minimum baud rate is relatively low « 10 kbps) to insure the transfer of alarms. Now, the studies are focused on the interface between our home automation system of data collection and the WAN TRANSVEIL2G system proposed by FRANCE TELECOM.
4. DATA PROCESSING AND APPLICATION When boosting the monitoring procedure, the signals emitted by the sensors excited by the elderly either directly (TV, light switching) or indirectly (motion sensor) are transmitted to the monitoring algorithm managing computer by means of the communication interface. These raw data or observations will either be stored for deferred-time processing for learning (elaboration of the learning stage), or will be immediately realtime processed in the case of a decision making about a fall or a fugue (in the daily use phase) as shown in figure 3. Thus, data collected in real time constitute a real basis for the processing Pc. Three stages are planned in the implementation of the system : - the learning stage where data obtained by a questionnaire and way of life direct observation are submitted to the neural network and memorized in the form of synaptic weights: a historical account is thus created (with schedules and frequency), - the generalization stage where the diagnosis of habit changes is deduced from a procedure of data classification (expert system), - the personalization stage where the user is offered the possibility to alter and adjust the system himself
441
Table 1 Data collected from 51 : bathroom light S3 : IR sensor #3 S5 : bed sensor S7 : IR sensor #5
+
~::-b U, .
Loc.~onl- -: In' uu
File
Flk:
1
R U : ,"L.a~ront.I~
time
I
re.1
22:25:15 22:25:16 22:25:17 22:25:18 22:25:19 22:25:20 22:25:21 22:25:22 22:25:23 22:25:24
_I
1= 1
.
a simulating situation 52 : bedroom light S4 : IR sensor #2 S6 : IR sensor # 1 S8 : IR sensor #4
SI S2 S3 S4 S5 S6 S7 S8 o 000 1 000 o 0 0 0 100 0 o 0 100 000 o 1 0 0 0 0 0 0 o 0 1 0 0 0 0 0 0 0 0 000 0100000 o 000000 0110000 0100000
Fig. 3. Learning and test phases according to his satisfaction degree (Chan, et aI., 1995a). This diagnosis system thus completes the multisensor system. Indeed, with some knowledge on the elderly people behaviour and way of living, rules can be laid down, the real time data confronted to these rules allow to decide if a situation is normal or not. Thus, the fall will be able to be detected as an interruption of mobility... without any instrumentation on the person. The automatic detection of a fall is based on real time data collection. The period chosen to assist the elderly person is night-time. If we assume that at this period of time, everybody must be at bed, all data related to motion at night, for example between 9 pm to 7 am the next morning, are collected and processed. If the elderly person wakes up and walks in his flat to go to the bathroom or any other area and if he stays in some area of the room longer than a pre-set period of time (which can be adjusted), an alarm signal will set off. On 20 simulating situations with a person who falls in different areas of the room the results show that the system can detect a fall when it is "an event which results in a person coming to stay inadvertently on the ground or other lower level". A period of time is chosen to set up alarm for warning the family or the staff. An example of data file is shown. According to table 1, the person is resting on the ground from 22:25 :44, the timer for sensor #5 shows a period of time of 15 s of rest. The process shows an alarm signal at the end of 15 s. The fall is located in the bathroom. The light of the bathroom has not been switched on.
alarm
1 0 000 0 1 1 0 0 0 0 010 100
22:25:29 22:25:30 22:25:31
0 0 0
22:25:34 22:25:35 22:25:36 22:25:37 22:25:38 22:25:39 22:25:40 22:25:41 22:25:42 22:25:43 22:25:44
0010100 o 100 000 0 o 1 0 0 0 0 0 0 0101000 0101000 o 1 000 100 o 1 000 100 o 1 0 0 0 0 1 0 o 1 0 0 0 0 1 0 01000010 o 000000
22:25:58
0
o
0
0
0
0
0
5. CONCLUSION This paper presents the development of a data collection and processing multisensor system applied to the behaviour follow up of the elderly. The original principle consists in learning about the old person' s habits so as to diagnose a potential change in behaviour without any human intervention. In some cases, the assistance proposed can also be founded on the real time data collected with rules written by experts using what they know about the elderly people's way of living and habits to decide whether to give them help when a dangerous situation happens. The use and the adaptation of commonly used sensors, associated to a communication network from the industrial world offers a good solution to the problem at stake. The physical separation of hardware dedicated to collection and to processing allows time-saving during the collecting stage as well as during the analysis with a neural network. Some results are presented with simulating data collected on an experimental site.
442
REFERENCES Advantech (1996a). Total solution for PC-based industrial automation. Solution guide, 61, 8.18.18. Advantech (1996b). Total solution for PC-based industrial automation. Solution guide, 61, 7.27.7. Campo, E., T. Val, 1.1. Mercier and D. Esreve (1996). La surveillance multicapteurs dans l' habitat : Application la surveillance de personnes agees vivant isolees cl domicile.
a
Article submitted at Journal Europeen des Systemes Automatises (Bermes, Ed). ChaD.. M , T. Viard, ML. Caillavet and E. Campo (1995a). Smart Technology for the Elderly and Disabled Users at Home. Proceedings of the 2nd TIDE Congress of the European Context for Assistive Technology, 393-396, Paris. ChaD.. M , C. Hariton, P. Ringeard and E. Campo (1995b). Intelligent home automation system for the elderly and the disabled. Proceedings of
IEEE International Conference on Systems, Man and Cybernetics, 2, 1586-1589, Vancouver. ChaD.. M, H. Bocquet, E. Campo, T. Val, D. Esteve and 1. Pous (1996). Multisensor system and artificial intelligence in housing for the elderly and the disabled. Proceedings of Gerontotechnology Conference, Helsinki. Dargent-Molina, P. and G. Breart (1995). Epidemiologie des chutes et des traumatismes lies aux chutes chez les personnes agees. In:
Rev. Epidemiologie. et Sante Publique, 43, 7283, Paris. Desodt, P. (1996). Choisir un reseau de terrain. nOspecial, June/July, ISSN 154.845x, 35-47. Vellas, B. and 1.L. Albarede (1993). Sleep disorders and falls in healthy elderly persons. In: Sleep Disorders and Insomnia in the Elderly (B. Vellas, 1.L. Albarede, J.E . Morley & T. Roth, Eds), Vol.7, pp. 75-87. Facts and Research in Gerontology.
443