5G-FOG: Freezing of Gait Identification in Multi-class Softmax Neural Network Exploiting 5G Spectrum

Freezing of gait (FOG) is one of the most incapacitating and disconcerting symptom in Parkinson’s disease (PD). FOG is the result of neural control disorder and motor impairments, which severely impedes forward locomotion. This paper presents the exploitation of 5G spectrum operating at 4.8 GHz (a potential Chinese frequency band for Internet of Things) to detect the freezing episodes experienced by PD patients. The core idea is to utilize wireless devices such as network interface card (NIC), radio frequency (RF) signal generator and dipole antennas to extract the wireless channel characteristics containing the variances amplitude information that can be integrated into the 5G communication system. Five different human activities were performed including sitting on chair, slow-walk, fast-walk, voluntary stop and FOG episodes. A multi-class, multilayer full softmax neural network was trained on the obtained data for classification and performance evaluation of the proposed system. A high classification accuracy of 99.3% was achieved for the aforementioned activities, compared with the existing state-of-the-art detection systems.