## 3 Materials and Methods
### Multimodal Sensing Strategy in NeuroPredict Platform  
The NeuroPredict platform's _Sensing and Acquisition layer_ integrates a carefully selected set of physiological and environmental parameters that are significant to the detection and monitoring of MS, PD, or AD, to facilitate high-accuracy, multimodal data gathering. In addition to signal type variation, the approach behind device selection places a strong emphasis on technical trustworthiness, user versatility, and long-term deployment reliability. Rather than prioritizing generic sensor connectivity, the platform emphasizes clinically relevant measurements and device-level complementarity over conservative connectivity of sensors. Every sensing device assists in accomplishing one or more reasoning targets, which might include contextual environmental assessment, and movement pattern detection. Several symptom categories are covered by the combination of wearable, near-body, and ambient technologies without overburdening the platform with obsolete or low-value inputs. Although some of the devices--like the GaitBand and the Blackbox units--have been designed and built by the NeuroPredict development team to satisfy performance, synchronization, and preprocessing requirements, others are commercially available and Bluetooth-enabled for smooth edge integration. An outline of the main devices currently included in the platform is laid out in Table 1, together with information on their physiological targets, relevance for disease monitoring, and integration roles within the platform:  
Depending on the specific type of signal, ongoing or periodic gathering is possible by enabling the integration alongside time synchronization of all devices via a single data \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device Type** & **Physiological Target** & **Relevance for Disease** & **Integration Role** \\ \hline GaitBand (custom-built) & Gait dynamics: acceleration, posture, Heart rate, heart rate & Detects gait impairments & Edge-level processing; custom signal acquisition \\ Withings ScanWatch & variability (HRV), Oxygen saturation (SpO\({}_{2}\)), skin temp, sleep quality & Monitors autonomic and sleep disruptions & Multimodal wrist device; BLE-synced with edge \\ Withings Thermo & Forehead temperature & Identifies potential febrile events & Thermal input for early fever detection \\ Withings BMP Core & Blood pressure, ECG, pulse & Tracks cardiovascular risks & Multimetric cardiovascular \\ Withings Full Body Scan & Body composition: fat, water, muscle, bone mass & and arrhythmias & device; validated usage \\ Withings Sleep Analyzer & Sleep cycles, apnea, heart/respiration rate & Supports assessment of weight-related risk factors & Periodic baseline and trend analysis \\ Fitbit Charge 5 & ECG, oxygen saturation, activity metrics & Captures nocturnal & Passive monitoring; feeds \\ Medical Blackbox & ECG, SpO\({}_{2}\), temperature, alcohol/carbon dioxide (CO\({}_{2}\)) levels & irregularities in AD/MS & sleep model inference \\ Ambiental Blackbox & Ambient temperature, humidity, air quality & Monitors physical strain & Activity inference and physiological correlation \\ Smart Ring (Oura) & Stress, HRV, respiration, body temperature & Tracks autonomic and stress-related fluctuations & Multisensor ring; fused with health trend data \\ Sensoria Smart Socks & Step pattern, weight distribution, gait symmetry & Analyses motor asymmetry and fatigue-related changes & Real-time gait feature extraction and feedback \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IoT-based devices and their functional contributions in the NeuroPredict platform. acquisition protocol. Aligning time-stamped segments across sensors enables multimodal fusion, which acts as a framework for reliable downstream analytics and model inference.