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18 pages, 5433 KiB

Open AccessArticle

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis

by Kee S. Moon and Sung Q Lee

Sensors 2023, 23(15), 6790; https://doi.org/10.3390/s23156790 - 29 Jul 2023

Cited by 2 | Viewed by 1714

Abstract

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients’ health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level. Full article

(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)

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18 pages, 7827 KiB

Open AccessArticle

mCrutch: A Novel m-Health Approach Supporting Continuity of Care

by Valerio Antonio Arcobelli, Matteo Zauli, Giulia Galteri, Luca Cristofolini, Lorenzo Chiari, Angelo Cappello, Luca De Marchi and Sabato Mellone

Sensors 2023, 23(8), 4151; https://doi.org/10.3390/s23084151 - 21 Apr 2023

Cited by 2 | Viewed by 2002

Abstract

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype’s architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation. Full article

(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)

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17 pages, 2726 KiB

Open AccessArticle

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

by Serena Moscato, Stella Lo Giudice, Giulia Massaro and Lorenzo Chiari

Sensors 2022, 22(15), 5831; https://doi.org/10.3390/s22155831 - 04 Aug 2022

Cited by 19 | Viewed by 2584

Abstract

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context. Full article

(This article belongs to the Special Issue Intelligent Mobile and Wearable Technologies for Digital Health)

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