![]() Furthermore, walking styles of PD patients differ across subjects (including diverse motor anomalies). Moreover, there are many unknown aspects of the pathophysiology of FOG and its relation to PD. The current treatments and cueing techniques are just temporary solutions for limiting a FOG event time. FOG investigation is challenging because of its unpredictable and unreliable nature. Hence, automatic prediction of FOG is essential for generating the cueing only when a FOG event occurs. On demand cueing is more efficient than continuous cueing in decreasing the duration of FOG episodes. ![]() Fall can be because of different PD symptoms of which the major one is FOG. Falls and fractures can cause disabilities, significant impairment in the quality of life, and death with a 10.6% rate. ![]() Estimates show that 60.5% of PD patients experience at least one fall and 39% of them have recurrent falls which can cause fractures. However, it has other non-motor symptoms such as sleep disorder, cognitive changes, mood disorders, and fatigue. Therefore, the main symptoms of PD are related to motor disabilities including rigidity, bradykinesia, slowness, tremor, and freezing of gait (FOG). The major cause for PD is loss of the dopaminergic neurons in the part of the brain that is called the substantia nigra (SN) which is responsible for controlling the movement of different parts of the body. Parkinson’s disease (PD) is one of the most common progressive neuro-degenerative diseases with a worldwide prevalence of 22 per 100,000 person-years for all age groups, and up to 529 per 100,000 person-years in older populations. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects. 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. ![]() Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). 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. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW) and (2) walking with turning (WWT). The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The proposed FOG assessment system is called “Kin-FOG”. 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. 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. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. 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. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly.
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