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events is detected in both devices, with the exception
of a few users that present higher discrepancy, aug-
menting the standard deviation and introducing bias.
Better results would be expected if both devices were
locally and synchronised in time, i.e. the data were
obtained in the same body localisation, and perfectly
timely synchronised. By an empirical observation of
the data, overall, we also highlight that the same mor-
phological trends were noticed in both devices’ data,
however, with magnitude offsets and time warping is-
sues. This can also explain some of the discrepancies
in results.
Data Loss Results.
In this subsection, we present
the results for the five experiments in the Data Loss
Protocol described in Section 3.2.
• Test 1 – 20 Devices in Simultaneous Acquisi-
tion.
The experimental results showed that only
devices 109 and 110 exhibited transmission errors.
The devices were placed in the same chair, close to
each other, so interference could have caused the
malfunctioning. Device 110 displays the higher
error rate, with a value of 0.12 errors/s, while de-
vice 109 presents an error rate of 0.01 errors/s;
the remaining devices (18 devices) returned no er-
rors. Additionally, a timeline view of the errors
shows that these were systematic; i.e. consistent
throughout the acquisition.
• Test 2 – 10 Devices in Simultaneous Acquisi-
tion.
During the second experiment, the error rate
of device 110 persisted with a similar value (ap-
proximately 0.12 errors/s). However, new devices
(107, 111), started to return errors. The device 109
displayed an error rate of 0.2 errors/s, while de-
vice 111 an error rate of around 0.1 errors/s. Once
again, the errors were consistent throughout the
experiment.
• Test 3 – 20 Devices at Different Ranges to the
Antenna.
The results for a distance between the
devices and the router of 270, 450, 630, and 900
cm are displayed in Figure 4 to Figure 11, in Ap-
pendix. In the timeline view of the errors, as for the
previous experiences, the error rate was consistent
throughout the acquisition protocol. At the first
distance (270 cm), closer to the antenna, devices
109 and 110, on the whole, maintained their error
rate. A new device, device 115, started to display
errors, with a high error rate (approximately 1.5 er-
rors/s). At the second distance iteration (450 cm),
the devices returning an error were maintained,
however, the error rate from each decreased. Next,
for the third distance iteration (630 cm), the num-
ber of devices displaying errors increased to 6. The
error rate in the devices showing errors at the previ-
ous iteration expanded significantly. At the fourth
distance iteration (900 cm), a higher number of de-
vices have shown data loss (9 devices) with error
rates from 0.1 to 1.3 errors/s.
• Test 4 – Occlusion.
In a 20-minute experience,
no errors were obtained. Consequently, we can
conclude that body occlusion did not increase the
number of the devices’ errors.
• Test 5 – Random Movement.
Only one device
displayed errors, with an error rate of 0.08 errors/s.
Once again, the errors were consistent throughout
the experiment.
To conclude, after analysing the five setup experiments,
the FMCI device shows suitability to be used in simul-
taneous collective acquisitions, showing acceptable
packet loss.
5 CONCLUSION
Recent advances in wearable technology and its pro-
liferation into people’s daily living lead to a diversity
of devices focused on the acquisition of physiological
data. Additionally, we have been observing a transfor-
mation in entertainment, bringing new challenges and
possibilities to media providers and content creators.
Physiological data, namely the EDA, can be used to
measure an audience response and provide meaning-
ful information for both the audience, the artist and
producers, paving the way for futuristic shows and
entertainment experiences, in both co-located or dis-
tributed setting. Still, there are few practical options
for simultaneous data acquisition (e.g. in an audience
setting).
In this work, we (1) Introduce a new wearable de-
vice for EDA sensing, the Xinhua Net FMCI device,
which expands the current state-of-the-art by allowing
collective, simultaneous acquisition of data; (2) Eval-
uate its performance against the BITalino device, a
reference and recognised system. In order to perform
this task, we follow two protocols: (1) Data Quality
Protocol – replicating a methodological experiment
based on a common state-of-the-art test to elicit the
SNS activity, namely the isometric handgrip test; (2)
Data Loss Protocol – examining the device packet loss
during five different setup conditions.
In both protocols, the devices showed high simi-
larity between the acquired data, and no significant
data loss was observed in a collective setting with
multiple devices acquiring data simultaneously and
synchronously. Therefore, we can conclude its applica-
bility for future research in collective data acquisition
A Wearable System for Electrodermal Activity Data Acquisition in Collective Experience Assessment
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