9/14/2023 0 Comments Sync signal web and phone![]() ![]() First, it is the mainstay for validating emerging technologies with established devices as the gold standard. ![]() The precise temporal synchronization between physiological signals has a significant impact on downstream applications. ĭespite the advantages and convenience of monitoring various physiological conditions offered by digital health innovations, one challenge that arises is how to synchronize the timestamps of multimodal physiological signals across different devices. By leveraging big data analytical tools, particularly machine learning and deep learning, digital health helps shift healthcare toward prevention and the early detection of diseases. The recent advances and wide adoption of wearable and wireless sensor technologies, such as fitness trackers, wireless patches, and smartwatches, bring about large volumes of patient-generated health data (PGHD) that are user-initiated and include the continuous multimodal monitoring of physiological conditions. Digital health, or mobile health (mHealth), is gradually transforming the clinical management of chronic diseases, such as diabetes mellitus and heart diseases, which require close and continuous monitoring between clinical visits, as well as fostering telehealth that is accelerated in the era of COVID-19. The field is further accelerated by the development of big data service platforms that provide the infrastructure for large-scale data storage and analysis. The last decade has witnessed a rapid expansion in the field of digital health, driven by the burgeoning portable and wearable sensor technologies, medical devices based on Internet of Things (IoT), and advancement in big data analytics through AI and machine learning. Conclusions: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. Results: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. ![]() Methods: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. ![]() The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. Background: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. ![]()
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