|With changes in business practice and life style, increasingly people travel spending
prolonged periods of time sitting on airplanes, trains, or cars. Hence sitting
comfort has become a critical issue due to fitness and health implications.
Focusing on airplanes and based on the idea that a ’responsive environment’
(environment that adapts to independently assessed passengers conditions and
adjust itself to them) will provide high level of comfort, we investigated ways of
performing a continuos real time physiological monitoring of the passengers. A
smart seat is hence the interface structure to allow passengers to be monitored,
together with external sensors, such as environmental temperature sensors, humidity
sensors, etc. (those last ones are not subjects of the present work). The
smart seat is the physiological monitoring system that analyses in real time individual
passengers’ status, such as their body temperature, heart rate, posture,
activity, etc. Seated posture recognition is the subject of this study.
The aim of this study is to evaluate the relevant postures assumed on airplanes’
seats and to develop a robust method for their recognition. Moreover we wanted to
develop a commercial prototype of a non-invasive passengers monitoring system on
airplanes (i.e. a smart seat capable to detect different aspects of the passenger’s
conditions like its body temperature, its activity, etc.). Few studies have been
conducted in the past concerning seated posture recognition; all of them involved
a high number of sensors for an accurate result. The novelty of this study is the
overall simplicity of the system and the commercial feasibility; it is indeed based
on the following requirements: the number of sensors involved must be as small as
possible so to not interfere with other physiological monitoring modules integrated
on the seat (such as temperature evaluation, hearth rate analysis, etc.), and to
keep the weight and the cost relatively low.
The recognition was performed by using a combination of the following devices,
depending on the analysis performed: pressure mats mounted on the seat, force
plates, and eventually cameras for image analysis.
After determining the postures to be classified we created a ’Posture Database’
containing information about the relevant parameters for each of those. Two different
approaches to posture recognition were investigated: an analytical method
which reconstructs the passenger’s posture by using a small number of inputs
(solely form force plates data); and a pattern recognition method that makes
guesses about the passenger posture by accessing to the database of classified
postures. Both the approaches showed successful results.
In this pioneering study we created a Posture Database, not found in literature,
and developed methods for seated posture recognition that rely on the use of a
limited number of sensor.
Furtherly, a prototype of a smart seat capable to recognise the user’s posture
was developed within the FP6 project “SEAT: Smart tEchnologies for stress free
Air Travel” and it can lead to many exciting applications such as responsive
environment and personal comfort settings in vehicles, automatic control of airbag
deployment forces, ergonomics of furniture design, and biometric authentication
for computer security.