A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis

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.

Keywords: biomedical signal processing; digital health; medical equipment; multi-sensor fusion; respiration; wearable biomedical sensors.

Figure 1

The wireless wearable respiratory health monitoring system.

Figure 2

The time and amplitude characteristics of lung sound and ECG signals of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale.

Figure 3

The time and filtered amplitude characteristics of lung sound signals of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale.

Figure 4

The time and filtered amplitude characteristics of the high-frequency filtered signals and a seventh-order polynomial curve fit of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale.

Figure 5

The AUC and cumulative AUC from lung sound signals obtained from a seventh-order polynomial curve fit of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale. (d) The cumulative derivative AUCs relate to lung volume during inhaling.

Figure 5

The AUC and cumulative AUC from lung sound signals obtained from a seventh-order polynomial curve fit of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale. (d) The cumulative derivative AUCs relate to lung volume during inhaling.

Figure 6

The time and filtered amplitude characteristics of ECG sound signals of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale.

Figure 7

The time and filtered amplitude characteristics of the low-frequency filtered ECG signals and a seventh-order polynomial curve fit of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale.

Figure 8

The AUC and cumulative AUC from ECG signals obtained from a seventh-order polynomial curve fit of three breathing cycles obtained from the chest location; (a) 1000 mL, (b) 750 mL, and (c) 500 mL tidal volumes; one breathing cycle: 2 s inhale and 2 s exhale. (d) The cumulative derivative AUCs relate to lung volume during inhaling.

Figure 9

Box plots for the cumulative AUCs during one breathing cycle for different tidal volumes: (a) sound sensor data (subject 1); (b) ECG sensor data (subject 1); (c) sound sensor data (subject 2); (d) ECG sensor data (subject 2).

Figure 10

The sound and ECG signals of three coughing cycles obtained from the chest location (a); box plots for the cumulative AUCs during one breathing cycle for different tidal volumes: (b) sound sensor data; (c) ECG sensor data.

Figure 11

The sum of squared errors between the pre-stored template signature matrices and the measured matrices for different tidal volumes. (a) subject 1 (p-value: 0.0018); (b) subject 2 (p-value: 0.052).