A smartphone has beenused as a data acquisition platform for signal processing based
on traditional ambulatory voice measures (f0, SPL, phonation
time) and model-based estimation of glottal airflow properties.
Wearable biophysiological sensors may enable us to measure
how the environment and our experiences impact our
physiology. An architecture and implementation of a system
for the acquisition, processing, and visualization of biophysiological
signals and contextual information has been
presented [63]. The electrodermal activity wrist sensor worn
by the users that measured their autonomic arousal. A novel
ubiquitous upper-limb motion estimation algorithm has been
proposed which concentrates on modeling the relationship
between upper-arm movement and forearm movement [64].
A link structure with 5 degrees of freedom (DOF) to model
the human upper-limb skeleton structure has been developed.
Parameters are defined according to Denavit–Hartenberg convention,
forward kinematics equations were derived, and an
unscented Kalman filter has been deployed to estimate the
defined parameters. The potential of incorporating a realtime
biofeedback system with artificial intelligence for wobble
board training, aimed at improving ankle proprioception has
been reported [65]. The biofeedback system depended on
Euler angular measurements of trunk and wobble board
displacements and a fuzzy inference system was used to
determine the quality of postural control. Wearable sensor
technology has been reported in an unforgiving environment
of the martial arts sparring ring [66] so that the piezoelectric
force sensors on body protectors would help taekwondo judges
and referees to score tournament matches. Overall, it is seen
that the possibility of using wearable sensing technologies are
enormous and more and more new applications and methodologies
are reported.