RESUMO
Parkinsonian Tremor (PT) is the most common symptom of Parkinson's disease. Its early detection plays an important role in the diagnosis of the disease as it is often mistaken for another type of tremor, called Essential Tremor (ET). Accelerometry analysis has proven to be a trustworthy method for determining the frequency, amplitude, and occurrence of tremor. In addition, the use of portable and wearable sensors has increased due to the rapid growth of Internet of Things (IoT) technology, allowing data to be collected, processed, stored, and transmitted. In this paper, a wearable system consisting of a digital 3-axis accelerometer ADXL345 and micro-controller unit ESP32 was implemented to transmit accelerometry (ACC) signals from each upper limb simultaneously to a Graphical User Interface (GUI), that was developed in Python as an MQTT client, allowing the user to visualize both real-time and offline signals as well as to add markers to indicate events during the acquisition. Furthermore, this GUI is capable of performing an offline analysis consisting of the computing of Power Spectral Density (PSD) using Welch's method and a Spectrogram to visualize a time-frequency distribution of the ACC signals.
