![](bg4.png)
Figure 3: Monitoring data from Zephyr HXM.
simple.
In Figure 3 we can see the data as shown in the
mobile application. This data is received and shown
in real time. The alerts are also triggered in real time,
sending a notification to the user. We also tested the
activity levels by doing walks and runs, so we could
adjust the thresholds at which we consider an activity
a low level activity or a high level activity. For test-
ing the fall detection, due to the nature of the tests,
we could not use real falls, so we simulated falls.
The simulation consisted on users walking slowly and
tripping, falling on a mattress with the back facing
up, down, left or right side. This simulated falls were
all detected, by the application. The difficulty resides
in not considering daily episodes as falls. Episodes
as walking downstairs have force patterns similar to
falls. So, if we have a smartphone with a gyroscope
we can determine the orientation before and after a
fall. In a fall the orientation differs within a value
close to 90
o
, while when walking downstairs the ori-
entation variation is smaller. When a gyroscope is not
available a timer is started when a fall is detected and,
if the timer is interrupted by the user, there will be no
notification of a fall, if the timer runs out there will be
a fall notification.
5 CONCLUSIONS AND FUTURE
WORK
With this work, a Platform for Personal Monitoring
was developed in a smartphone that, taking advan-
tage of the capabilities of this kind of device, imple-
ments functionalities that go beyond the transmission
of the vital signs from the sensors to a remote server.
Therefore, we can conclude that it is possible to use
this kind of generic device in substitution of a spe-
cific Monitoring Platform. In terms of future work
we will implement protocols for communication with
new types of sensors and study the cost/benefits of
using smartphones with included gyroscope that pro-
vide better results in the fall detection algorithm.
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