Ensemble Learning Method for Human Identification in Wearable Devices.

Wearables today play a key role in E-Health computing, with investments expected to exceed $70 billion by 2024. With the massive use of apps on wearable devices, it is crucial to improve safety when using wearables, considering that important information about user information is stored on these devices. We present SOMEONE ensemble learning, a set machine learning algorithm for body recognition of wearable devices, which operates on the basis of both PhotoPlethysmoGram (PPG) and ElectroCardioGram (ECG) signals. We consider an individual's PPG and ECG signals, where algorithms process these signals stored on the wearable device to identify the user. The SOMEONE algorithm achieves better results on metrics such as F1 score, accuracy, false acceptance rate (FAR) and false rejection rate (FRR) for human recognition in MIMIC dataset of ECG signals and CapnoBase dataset of PPG signal.