The increasing pervasiveness of wearable sensors opens new scenarios in the continuous monitoring of health parameters. In particular, wearables are becoming the sensing part of the Internet of Medical Things (IoMT), i.e. IoT in the healthcare field. Currently, several IoMT based devices capable of measuring blood pressure (BP) are starting to be offered on the market, giving the possibility to monitor BP every time and everywhere. An open issue is the lack of the traceability and reliability of the BP measurements. The aim of this paper is stimulating the research to fulfil this lack by presenting an overview for IoMT and commercial wearables in BP monitoring, from a metrological point of view.

An Overview on Internet of Medical Things in Blood Pressure Monitoring

Wireless ECG and cardiac monitoring systems: State of the art, available commercial devices and useful electronic components

Measurement of multimodal physiological signals for stimulation detection by wearable devices

A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

ECG compressed sensing method with high compression ratio and dynamic model reconstruction

A novel compressive sampling method for ECG wearable measurement systems

Non-Uniform Wavelet Bandpass Sampling Analog-to-Information Converter: A hardware implementation and its experimental assessment

Continuous and long-term measurement of physiological signals out of clinical settings may face different processing requirements resulting in higher costs or reduced performance. Improved techniques of signal reconstruction from compressed representation may be a solution. This paper presents an approach based on Compressed Sensing to reconstruct peaks of Galvanic Skin Response measured by a wrist-worn device. Specifically, a random measurement matrix is employed in the reconstruction phase. Results show that the proposed approach detects the correct number of peaks better than the Ledalab automatic toolbox, even with high compression rates.

Reconstruction of Galvanic Skin Response Peaks via Sparse Representation

This paper proposes a dynamic method based on Compressed Sensing (CS) to reconstruct multi-lead electrocardiography (ECG) signals in support of Internet-of-Medical-Things. Specifically, the sensing matrix is dynamically evaluated through the signal samples acquired by the first lead. The experimental evaluation demonstrates that, compared to the traditional CS multi-lead method adopting a random sensing matrix, the proposed dynamic method exhibits a lower difference from the original ECG signal.

A Dynamic Approach for Compressed Sensing of Multi–lead ECG Signals

Physical exertion undoubtedly influences physiological parameters. The aim of this paper is to propose a Machine Learning classifier able to evaluate the physical state of subjects monitored through a wearable device, by simply analysing their Blood Volume Pulse signals. Moreover, a Fatigue-Related Index is presented to quantify the physical well-being status. Results show that the Support Vector Machine classifier provides the best performance for detecting fatigue-induced stress, since it shows an accuracy of 97.50%. The obtained results prove that the proposed approach allows to support the assessment of the worker's well-being status, with the aim of improving the workload management in the context of Industry 4.0.

Grazia Iadarola

Papers

A novel compressive sampling method for ECG wearable measurement systems

Non-Uniform Wavelet Bandpass Sampling Analog-to-Information Converter: A hardware implementation and its experimental assessment

Reconstruction of Galvanic Skin Response Peaks via Sparse Representation

A Dynamic Approach for Compressed Sensing of Multi–lead ECG Signals

Learning classifiers for analysis of Blood Volume Pulse signals in IoT-enabled systems