Wearable platform for automatic recognition of Parkinson Disease by muscular implication monitoring

Abstract

The need for diagnostic tools for the characterization of progressive movement disorders — as the Parkinson Disease (PD) — aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results.