With the advent of athletic medicines and a manifold increase in accidents along with the increase in life expectancy the need of homecare physical therapy has increased immensely. The demand of Physical Therapists has also increased in various other fields. The homecare physical therapy has a prescribed form which is needed by physical therapists because of the need of the government regulations and also because of giving the best treatment to the patients. This prescribed form is a very important tool for determining the best course of treatment for the patients.

For the increasing needs in defense and security the physical therapy is to play a great role in saving the lives of the soldiers and to give them an atmosphere they deserve. To the best of author’s knowledge homecare physical therapy is not computerized. It is based on simple questionnaires which are answered by the patient s and depending upon these questions and answers needs a suitable treatment is prescribed by the physical therapists.

With the increase in number of patients the computerization of physical therapy data is needed. The data for physical therapy is generally taken by the physical therapist from the patients. Because of the nature of the data, the treatment cannot be determined mathematically. In this thesis we suggest a fuzzy logic and neuro-fuzzy logic approach for providing aid to a physical therapist in prescribing the treatments to the patients. The approach is based upon the if-then-rules, which form the basis for the fuzzy logic.

The input output model of the Overall system is described;​ it consists of 5 sub systems having inputs and outputs. The output or the results which we obtain from these fuzzy logic programs are generally assigned a range 0 to 1. The range however is interpreted by the physical therapist according to the knowledge and expertise of the physical therapist and also the nature of the problem and the treatment requirement. Mathematically speaking, it depends upon a number of membership functions in a fuzzy logic approach model. In this study we have utilized a data of about 65 patients in the Detroit Metropolitan Area. Based on the Performa we developed a comprehensive table of data.

The different sets of this table have been computerized using the Mamdani and Sugeno approach to Fuzzy Logic. The results obtained using the fuzzy approaches are compared with the treatment results prescribed by a physical therapist. The correlations between the results obtained by fuzzy logic and by a physical therapist are also given. It is hoped that this approach will prove highly beneficial in the area of homecare physical therapy and thus in biomedical engineering.