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.
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.
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.
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.