Francisco Manuel Vieira

Abstract
Recent advances in embedded systems, wireless communication, and IoT technologies have driven the development of Wearable Health Devices (WHDs), enabling continuous monitoring of biosignals with low power consumption and high data transmission rates. Among various wireless communication protocols, Bluetooth Low Energy (BLE) stands out due to its energy efficiency and high transmission rate, making it the preferred choice for developing compact and high-performance wearables. However, achieving precise time synchronization across multiple BLE-enabled devices remains a challenge, particularly in distributed systems where sensor nodes operate independently. In this work, we present the WeSync(BLE) our reference synchronization architecture developed for multiple wearable BLE-based biomedical devices intended to streamline the use of numerous wearable devices and synchronize the data acquired across them. A proof-of-concept of this reference synchronization architecture was made using proprietary BLE wearables (used for acquiring motion data). This demonstrated effective synchronization with minimal implementation and latency, achieving an absolute mean and standard deviation of 9.2 ± 6.7 milliseconds, at 1 hour of testing. This work paves the way for a more robust real-time wearable systems synchronization, advancing the analysis and study of biosignals.

Abstract
Parkinson's Disease (PD) is a neurodegenerative disease characterized by severe motor symptoms, with no cure to date, and deep brain stimulation (DBS) is one of the most effective therapies to reduce the symptoms. However, not all patients benefit equally of this therapy due to adverse effects caused by the stimulation of unwanted areas, making it essential to use diverse technologies when analysing the effect of the DBS in PD. Wearable devices, such as the iHandU System that analyses motor data combined with the Percept TM PC neurostimulator, capable of recording brain signals in real time, allow a more precise, personalized, and adaptive approach to treatment. While each method alone provides valuable but limited insights, combining and synchronizing data from the different sources enables a more comprehensive and dynamic understanding of the effects of stimulation on the patient. A study with 6 DBS-implanted participants from Centro Hospitalar Universitario de São João was conducted to test a synchronization protocol using both the Percept TM PC and the iHandU System. The protocol combined the Network Time Protocol and the Artifact-Based Synchronization Techniques, with an average of less than 500 ms of delay between signals. The results obtained show that this combination improves signal synchronization accuracy and consistency across subjects, minimizing delays and reducing reliance on visible peaks in cases of low signal quality or inconsistent artifact detection. © 2025 IEEE.

Abstract
Wearable Health Devices (WHDs) are increasingly becoming an integral part of daily life and significantly contributing to self-monitoring in healthcare. WHDs have a wide range of applications, ranging from sports to clinical settings, where the monitoring of cardiovascular health, particularly through ECG, plays a crucial role. This study introduces a unique WHD called VitalSticker, which exhibits distinctive features such as having a comfortable tiny patch form-factor to be attached to the chest, collecting multiple vital signs with medical-grade quality (ECG, respiration, temperature and actigraphy) and seamlessly sending data to a companion app. This paper encompasses a detailed description of the hardware, firmware, and case design of the WHD. A study was conducted to assess the quality of the ECG signal acquired by VitalSticker, comparing it with the signal obtained from a CE medical-grade certified ambulatory device. The results demonstrate that our VitalSticker achieves similar medicalgrade quality when compared to the reference device, surpassing its counterpart in several specifications. Furthermore, this study presents the successful implementation of an ECG baseline wander correction filter that runs on the tiny on-board wearable microcontroller without introducing any artifacts into the ECG signal, reducing the need for further processing for this outside the wearable patch.