Page 15 - Handbook of Biomechatronics

8 Ahmed R. Arshi

2.3 Sensory Interactions
Human body in both physiological and pathological states can be assumed
as a closed system with an array of input/output ports through which
energetic interactions occur with the surrounding. Information regarding
the nature of interaction is translated from a variety of energy domains by
neuromechanical sensory systems. The design of suitable biomechatronic
interfaces with neuromechanical sensory systems require an in-depth
understanding of the neuroanatomy. Sensors in body transform external
or internal stimuli from multitudes of energy domains to an information
carrying signal. Involuntary actuation signals are also transformed into
other energy domains to control the operation of cellular structures
through biochemical interactions like metabolism, whereas complex
movements such as skilled performance encountered in athletic agility
drills, require a different array of actuation signals. Body sensors rely
on identification and quantification of internal or external stimuli like
pressure, heat, texture, vibration, and tensile or compressive deforma-
tions. Highly dedicated mechanoreceptors for example, take advantage
of biomechanical deformations to produce time-dependent neu-
romechanical signals. Such systems are interesting for those involved in
biomimetics and biosensor design as well as those involved in rehabilita-
tion robotics or smart skin technologies.

2.4 Processing and Control
Body sensors are considered as a highly advanced data acquisition and infor-
mation gathering system. The biophysical/biochemical mechanisms
governing processing of gathered data result in involuntary mechanical
movements like heart rate control or voluntary artistic movements such
as in painting. Design of interactive interfaces which rely on this data will
attract more attention in biomechatronic circles in the years to come. Cur-
rent efforts rely on noninvasive physiological techniques like those used in
electroencephalogram (EEG), electromyogram (EMG) or through nerve
conduction studies. The information obtained using these devices require
advanced real-time signal processing and matching control algorithms.
The data gathered provides a complex array of real-time signals which could
be utilized in real-time operational biomechatronic systems. A gap between
the undecipherable large data and often ingenious solutions to control prob-
lems requires an alternative approach. This alternative mode of thought
needs new biosensor technologies to access the neuromechanical systems