Wearable systems for e-health and wellbeing
Pers Ubiquit Comput
DOI 10.1007/s00779-017-1041-1
EDITORIAL
Wearable systems for e-health and wellbeing
Guglielmo Cola 1 & Alessio Vecchio 1
# Springer-Verlag London Ltd. 2017
Wearable devices, such as smartwatches and fitness bands, are
becoming a key element of our lives. They are used in an
always increasing number of activities, for example during
sport sessions for keeping track of energy expenditure, or
when walking as unobtrusive pedestrian navigation systems.
In general, these devices are worn continuously throughout
the day and thus provide the opportunity to gather information
about their users with unprecedented levels. In addition, many
wearable devices are directly worn over the skin and they may
include sensors not available on common smartphones (e.g.,
for monitoring the user’s heart rate). As a consequence, they
are particularly suitable for those medical applications where
continuous monitoring is fundamental. At the same time, the
massive amount of information collected through these devices is enabling novel applications in the context of ehealth and wellbeing. Finally, it is known that abundance of
information promotes an effective management of patients’
condition, and a well-informed patient is more likely to conduct a healthy lifestyle.
This issue of Personal and Ubiquitous Computing collects
recent, original research in the area of wearable systems for ehealth and wellbeing. Fourteen papers were initially submitted
* Alessio Vecchio
Guglielmo Cola
1
Dipartimento di Ingegneria dell’Informazione, University of Pisa,
Pisa, Italy
to this issue. After two rounds of review, four of them were
finally accepted for publication.
The first paper, BSleep behavior assessment via smartwatch
and stigmergic receptive fields^, presents a method for automatic assessment of sleep quality using a smartwatch.
Heartbeat rate and wrist motion samples are processed using
computational stigmergy, a bio-inspired technique that relies
on digital pheromone marks.
The second paper, BSocial Recommendations for
Personalized Fitness Assistance^, presents a novel framework—PRO-Fit—aimed at engaging users in fitness activities. The proposed framework minimizes the need for user
input and proactively generates personalized fitness schedules. Collaborative filtering and social network information
are exploited to automatically provide activities and fitness
buddy recommendations.
The third paper, BRobust Orientation Estimate via Inertial
Guided Visual Sample Consensus^, presents a method for
estimating the orientation of body joints using a wearable
camera paired with an Inertial Measurement Unit (IMU).
Visual information is used to correct the drift of the IMU,
whereas information produced by the IMU enables more accurate and efficient image-based estimation.
The last paper, BSVM-based classification method to
identify alcohol consumption using ECG and PPG
monitoring^, presents a method for detecting alcohol
intoxication that is compatible with the requirements of
wearable devices. Cardiac activity, observed through
simple sensor configurations, is given as input to a classification system in charge of estimating the status of
the user.
Finally, as co-guest editors of this issue, we would like to
thank all the authors for their contributions.
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