Page 16 - Handbook of Biomechatronics

Introduction 9

with much better defined data gathering algorithms. Here, combinations of
implantable myoelectric sensors and predictive controller approach using
learning strategies can contribute toward real-time user intent recognition.
Advances in neuroscience are the key component of a sound and solid
biomechatronic future. Neuroscience is the holy grail of biomechatronics.
The propositions made by perceptual control theories are an example
of possibilities in developing control strategy.
Neuromechanical biomechatronic systems are and will be in a good posi-
tion to offer true personalized solutions to many human concerns. The signal
processing and control problems in personalized biomechatronic systems
need to address cognitive and perception issues through emphasis on integra-
tion with motor control and motor learning concepts. Although the core of
current research funding is directed at such systems as all terrain autonomous
vehicles and exoskeletons, the subject will be gradually moving toward a new
generation of integration with the human neuromusculoskeletal system.
This is where proprioception and enhancement of peripheral information
acquisition systems could provide remarkable design opportunities for
biomechatronics.

3 MODELING

The multidisciplinary nature of mechatronic systems when combined
with an exceptionally unique and diverse set of not totally understood neu-
rophysiologicalsystemsdictatethenecessityforasuitablemultilingualmodel-
ing technology. The multiscale, multidimensional, and pseudodeterministic
nonlinear dynamic characteristics of such systems pose immense challenges to
established intradisciplinary modeling methodologies. Electrophysiological
energetic interactions taking place at the cellular level are governed by
multi-domain energetic paths encompassing biochemical, ionic, heat and
mass transfer across cellular membranes, and broadly, initiation and propaga-
tion of action potentials throughout the cellular structures. Any inter- or
intradisciplinarymodelingapparatusshouldbewellequippedwiththepoten-
tials to include nonlinearities in a model which is based on a linear analysis
scaffold. To include all different modeling languages in a biomechatronic
design project is rather challenging, if not difficult. An ensuing outcome of
this multilingual approach to modeling is restrictions on communications
among disciplinary project managers. Mathematical models capable of
embracing aspects such as electrophysiology which govern neuromechanical