Background :-
:-
Reaction
time, speed, force, and
tremor are parameters that are used to obtain a quantitative instrumental
determination of a patient’s neuro-psychophysical health. These parameters have
been used in the study of the progression of Parkinson’s disease, a
particularly degenerative neural process, but these parameters can also be
useful in detecting the wellness of a healthy person. As a matter of fact,
these measurements turn out to be an excellent method of finding reactive
parameters alteration due not only to a pathology, but also, for example, to
the use of drugs, alcohol, drugs used in the treatment of mental conditions, or
other substances that could affect a person’s reactive and coordination capabilities.
Whether
the person suffers from Parkinson’s disease; another pathology, or is healthy,
it is important to carry out continuous monitoring of his health condition. The
ordinary therapy for Parkinson’s disease has to be carefully dosed with considerable
frequency, because inadequate doses could have repercussions of the motion
capability of the patient. Therefore, it is important to control the value of
the parameters that determine nervous system health.
Moreover,
for a healthy person, a continuous health monitoring turn out to be an
excellent prevention system of some pathology and is an excellent method to
acquire consciousness of how lifestyle and behavior have repercussions on one’s
psychophysical well-being.
Abstract
This
seminar deals with the design and the development of a bio-robotic system based
on fuzzy logic to diagnose
and monitor the neuro-psychophysical conditions of an individual. The system,
called DDX, is portable without losing efficiency and accuracy in diagnosis and
also provides the ability to transfer diagnosis through a remote communication
interface, in order to monitor the daily health of a patient. DDX is a portable
system, involving multiple parameters such as reaction time, speed, strength
and tremor which are processed by means of fuzzy logic. The resulting output
can be visualized through a display or transmitted by a communication
interface.
New Experimental System (Ddx)
DDX
is the new experimental bio-robotic system for the acquisition and restitution
of human finger movement data. It is a bio-robotic system designed and
constructed with medical and clinical data for the analysis of Parkinson’s
disease. It was originally used for the analysis of neural disturbances with
quantitative evaluation of both the response times and the dynamic action of
the subject.
Software
By
pressing the button, three beacons are sent, signifying, respectively,
beginning pressure, race end, and force. First, the processor sends an impulse (like a warning) to the buzzer,
and the timer starts. It begins the sampling and, after a random interval,
sends another impulse to the buzzer (in order to obtain the starting signal).
The value of the timer is stored in to tj. When the patient has pressed the
push button, a beginning pressure beacon is sent, and the value of the timer is
assigned to ti This time is what we call
the “Reaction Time”. At the end of the movement stroke, an end-of-race beacon
is sent, and the value of the timer is assigned to tf. The speed of patient
motion can be calculated from these times. When the stroke ends, the pressure
is calculated using a simple circuit based on a strain gauge, a filter, an
amplifier and an analog to digital (A/D) converter. Tremor is measured by a
routine that reads data from the switching accelerometer on an input/output
(I/O) pin.
Conclusions
In
this article, an innovative bio-robotic system for neuro-psychophysical
health-condition detection is presented. Today, systems of detection are very
reliable but not portable and do not generally allow diagnoses to be sent via
the internet.
The
proposed fuzzy logic solution is portable without losing efficiency and
accuracy in diagnosis and also provides the ability to transfer diagnoses
through a remote communication interface in order to monitor the daily health
of a patient. The system is an intelligent machine based on soft computing
techniques, and its efficiency can be improved considering more patterns of
examples of functions, calibration, or, moreover, by using self-learning
techniques.
0 comments:
Post a Comment