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was not significant fluctuation in the day to day results obtained. However, 10
movements (out of a total of 1637 movements) were undetected by the system. We
found that all the movements that were not detected were actually of very short
sequence (5 or 6 sensors). The reason was due to failure of the treatment of data
stored in the .dat file by the program.
The system is generally capable of detecting all major movements in the room.
But in the event, when the patient happens to stand near the door, but not causing
movement for a long period without actually leaving the room, will be detected as
‘patient leaving the room’, since the last sensor to be activated will be the outside
door sensor. Similarly, the system couldn’t tell where the patient is lying in the room
if he or she is stationary for a long period, either on the bed or on the floor, since the
infra-red sensors will become inactive due to the absence of movement. But if the
patient falls and stirs on the floor, his movement is detected by the vertical sensors but
it is not detected by the ninth sensor (horizontal). In this case, GARDIEN
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can sound
an alarm. Since the ninth sensor is working, GARDIEN
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and the caregivers did not
note a patient fall; consequently, we can’t conclude yet about the efficiency of this
sensor.
Most of the telesurveillance systems that have been developed or tested until now,
used a system of multisensors in which infra-red sensors were used along with other
types of environmental sensors or wearable sensors (fall sensor for example). [7-10]
In all these cases, the data obtained from different sources were combined by ‘fusion
of data’ regarding activity of a person within an intelligent habitat. The installation of
a multisensor system increases the complexity of the habitat in addition to increasing
the expenditure. Moreover, the infra-red sensors that were used in all these cases, only
detected the presence or absence of a person by noting the movements within the
room.
GARDIEN
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, on the other hand, was developed only with passive infra-red
sensors, which due to its intelligent algorithm could not only detect the presence or
absence of a person, but also detected the type of activities done by the person within
the room including his or her exit from the room. This is an important aspect of the
system since it permits distinction between the entry and exit of night personnel with
that of the patient. This feature could well, in future, be combined with a system of
passive telealarm that can alert the caregivers in real-time whenever the patient tries
to leave the room, which are many times associated with falls or getting injured in the
corridor. The simplicity of installation of infra-red sensors within a room is a plus
point, since no other type of sensor is required.
Another important feature of the system is that nocturnal actimetry of the persons
living in the room is possible. The activity inside the room during one night is shown
in Fig. 6. In this figure, one can note that the patient was extremely agitated from
21.15 till 01.30 hours in the next morning. Following which, there was a period of
sleep lasting till 05.00 hours, when activity due to waking up was noted. For patients
staying for a longer period, it may serve as a means of identifying motor activity
trends that can provide data to the physicians to monitor the patient and attribute
deviations in the activity trend to various therapeutic interventions.
Studying behaviour trends in relation to treatment may help planning therapy and
follow-up of the patients. In addition, sleep patterns may be discovered in patients
suffering from insomnia. A patient with a known seizure disorder showing excessive
agitation in the bed on a particular night could signal a convulsion and in turn may be
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