A　novel　study　on　monitoring　and　analysis　of　the　debilitating　condition　of　patients　suffering　from　the　neurological　disorder　is　presented.　Parkinson＇s　disease　is　characterized　by　limited　motor　ability　of　a　patient.　Freezing　of　gait　is　a　major　nonmotor　condition　among　aging　patients　and　its　evaluation　can　reduce　the　chances　of　any　secondary　disorders.　In　this　paper,　amplitude　and　phase　information　of　the　radio　signals　observed　for　a　fixed　period　of　time　are　used　to　differentiate　the　motor　and　nonmotor　symptoms.　The　amplitude　information　is　classified　using　a　support　vector　machine,　while　the　linear　transformation　is　applied　to　obtain　sanitized　phase　information　for　detection.　The　proposed　method　is　very　handy　with　the　minimum　deployment　overhead.　The　analysis　shows　that　this　method　also　offers　a　high-accuracy　level　of　around　99%　based　on　the　observation　of　a　number　of　patients.　These　features　make　it　an　attractive　solution　for　real-time　patient　monitoring　systems.