Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.
Toward Improving Coastal-Fog Prediction (C-FOG)
