## 3 Materials and Methods
### Blackbox Devices' Concept and Development
* _GPS Module:_ The GPS module integrates a chip antenna and Qwicic cable connection, ensuring efficient hardware integration. It significantly reduces power consumption while enhancing performance and accuracy. The ability to receive signals from up to four Global Navigation Satellite System constellations simultaneously improves positional accuracy, particularly in environments with limited sky visibility, such as urban canyons or dense foliage. Additionally, Assisted-GPS technology is used to reduce the time-to-first-fix, ensuring faster positioning data. A differential GPS can also be employed to enhance precision, reducing errors to sub-meter accuracy. Based on the u-blox MAX-M10 GNSS platform, the GPS module used in the Monit4 Health system supports simultaneous reception from up to four GNSS constellations (GPS, Galileo, GLOMASS, and BeiDou), improving positioning accuracy and signal availability even in difficult-to-reach places like urban canyons. For power-optimized performance, it makes use of the u-blox Super-E technology, which uses as low as 25 mW while in continuous tracking mode. The Qwic interface facilitates communication over I\({}^{2}\)C, allowing for effective data transmission with low latency and simplified wiring. In order to improve energy consumption in wearable health monitoring applications, the module incorporates automated low-power modes, such as power save and periodic tracking, and enables an Assisted Global Navigation Satellite System for an expedited time-to-first-fix. It has anti-jamming and anti-spoofing characteristics, which are essential for preserving data integrity in linked health ecosystems, and enables satellite-based augmentation systems for improved location precision. The MAX-M10 GPS module is a dependable and energy-efficient option for real-time geolocation in the Monit4 Health system because of these features [60]. * _EMG Sensor (Myoware Sparkfun) for Muscle Signal Analysis_: This sensor measures EMG signals from muscle activity and features an integrated amplifier with an adjustable gain of up to 1000x. It includes a high-pass filter (0.5 Hz) to eliminate direct current offset and a low-pass filter (125 Hz) to reduce high-frequency noise. The sensor uses three disposable gel electrodes and outputs both raw and envelope signals, making it suitable for applications such as robotics and prosthetics. Signal conditioning is performed to reduce motion artifacts and interference from ambient electrical sources. A notch filter (50/60 Hz) is applied to mitigate power line interference. The Monit4 Health system's integrated EMG module uses a multi-stage analog signal conditioning architecture to record and process surface electromyography data with high fidelity. In order to separate the physiological EMG frequencies while reducing low-frequency motion artifacts and high-frequency noise, the signal collection chain starts with an instrumentation amplifier set up for an initial gain of 200\(\times\) and operating within a passband of 20 Hz to 498 Hz. Signal scaling according to electrode location and specific muscle activation levels is made possible through the following gain stage, which may be adjusted between 1\(\times\) and 100\(\times\). A 3.6 Hz high-pass filter is incorporated into the module to remove DC offset, guaranteeing baseline stability and reducing signal drift over time. After amplification, the signal passes through an envelope detection stage and a full-wave rectification circuit, enabling the recording of both rectified and envelope outputs. A high-resolution analog-to-digital converter digitizes these analog outputs, guaranteeing precise EMG signal sampling for edge processing. In the Monit4 Healthy Edge Processing Layer, adaptive feature extraction and threshold-based event detection are made possible through the architecture's support for the real-time monitoring of both raw and processed EMG data.