## 3 Methods
### The NeuroPredict Platform
#### 3.1.4 Medical Monitoring and Evaluation Functionalities in the NeuroPredict Platform
It provides the following relevant sleep parameters: time-related parameters (time to fall asleep, time spent in each phase (light/deep/REM), total time awake, total time slept, and total time spent in bed), snoring-related parameters (total snoring time, snoring episodes count), apnea and breathing disturbances (apnea hypopnea index), number of wake-ups, number of times of getting out of bed, heart rate (minimum, average, and maximum value), respiratory rate (minimum, average, and maximum value). The platform is also designed to allow patients to report their own perceived quality of sleep through standardized tests. There are multiple forms for self-reporting SD severity; among them, the Pittsburgh Sleep Quality Index (PSQI) is widely used [80]. There are also forms customized for PD: the PD Sleep Scale (PDSS and PDSS2) and the Scales for Outcomes in PD Sleep (SCOPA-S) [7], which mainly complete the general sleep form questions with questions related to symptoms specific to PD. PSQI and PDSS2 were chosen to be integrated into the NeuroPredict platform. As in the case of cognitive functioning testing, new forms may be defined by users with the necessary roles and permissions. Tests may be filled out by patients directly on the platform or may be downloaded as pdfs and scored manually. * Daily activities monitoring  
Daily activities monitoring include (a) physical activities monitoring and (b) location monitoring. Daily activity monitoring is performed by integrating data from multiple sensors. Wearable devices providing accelerometer data provide the level of activity, including detection of physical activities like sports and GPS data, and provides location monitoring, specifically important for patients suffering from memory loss, for whom getting lost outside of their homes is a major risk. * Emotional state monitoring  
Emotional state monitoring is included in the platform as there is a known link between physical and mental health. Constant negative mental states may lead to mental health problems like anxiety and depression. Knowing about recurrent negative mental states may allow intervention and prevention of possible negative health effects. Predictivemodeling based on emotional states can help forecast mental health problems and facilitate early intervention. Emotional state monitoring is performed in multiple ways: (a) based on the emotional state provided by the IoMT devices integrated into the platform (Oura Smart Ring, Muse ECG Headband), and (b) through our own algorithm based on voice processing. In the Results section, the current work presents an algorithm for emotion detection based on voice processing. Patients can log their perceived emotional states in an emotional state log. These entries can be kept private or shared with authorized users who have access to the patient's medical file. Currently, the self-reported emotional states are neither used nor correlated with the states detected by the emotion detection algorithm or those assessed by the IoMT devices. * Speech and language abilities monitoring Monitoring speech and language abilities can be valuable for detecting and tracking the progression of both motor and cognitive issues. Certain changes in voice may be specific to motor control problems of the vocal tract, and certain speech problems may reflect language processing difficulties due to cognitive decline. Speech and language ability monitoring includes monitoring the following: (a) changes in certain acoustic features: pitch variation, speech rate, and words articulation; (b) language processing: evaluation of vocabulary usage, sentence structure, and grammatical accuracy; evaluation of word finding difficulty; comprehension testing; and narrative skills. Speech and language ability monitoring is a designed feature that will be implemented on top of the speech processing for emotional state monitoring, with functionality that already deals with voice acquisition, preprocessing, and feature extraction.