Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

Wearable edge machine learning with synthetic photoplethysmograms




Julkaisun tekijätSirkiä Jukka-Pekka, Panula Tuukka, Kaisti Matti

KustantajaElsevier Ltd

Julkaisuvuosi2024

JournalExpert Systems with Applications

Tietokannassa oleva lehden nimiExpert Systems with Applications

Artikkelin numero121523

Volyymi238

JulkaisunumeroPart B

DOIhttp://dx.doi.org/10.1016/j.eswa.2023.121523

Verkko-osoitehttps://doi.org/10.1016/j.eswa.2023.121523

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/181473223


Tiivistelmä

Strict privacy regulations pose challenges to the development of machine learning (ML) in the field of health technology where data is particularly sensitive. Gathering and using robust, bias-free, and suitably anonymized datasets required by ML models is difficult, time-consuming, and thus expensive. Parametric synthetic data offers a solution by mimicking real-world processes with easily adjustable parameters that shape the information content of the data as desired. This article presents a system demonstrating how synthetic data can be used in conjunction with wearable edge devices. Importantly, the system preserves privacy as there is no risk of leaking sensitive information from the model or during the use of the wearable device. The system consists of (1) a synthetic photoplethysmogram (PPG) model, (2) convolutional neural network (CNN) models trained with the synthetic signals, (3) a wearable edge device that computes heart rate from real-time PPG signals using the developed CNN models, and (4) an accompanying mobile phone application receiving the results. The synthetic model produces realistic PPG signals together with labels that can be used in CNN model training. The quality of the synthetic data is sufficient to train even a tiny CNN model with only two convolutional layers and 28 parameters to detect PPG waveform feet. The developed wearable device is able to run the model smoothly and the performance of the model is on par with the more complex models and other foot detection algorithms.


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Last updated on 2023-31-10 at 09:12