A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios

Abstract

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system's reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.

Keywords: IoT; cloud computing; digital health; occupational health monitoring; real-time emergency management; wearable device.

Figure 1

This diagram illustrates the typical Cloud (A) and IoT (B) architectures along with their respective ecosystems. It depicts the data flow and interactions among various components, including mobile devices, terminals, and containerized services. The diagram highlights the integration of IoT devices and communication protocols, such as MQTT, with Cloud-based infrastructure, emphasizing how these elements collaborate to enhance operational efficiency and responsiveness in data processing and analysis.

Figure 2

This diagram outlines the step-by-step process followed in the study, starting from the release of the first beta version of the platform and the collection and evaluation of questionnaires for participants’ enrolment. It includes key phases such as participant eligibility screening, monitoring sessions, data collection, and artifact analysis. The workflow also highlights the data validation, analysis, and assessment of the system’s performance and reliability under real-world conditions. For each step, the main categories of roles involved are highlighted: CRI personnel (control center operators, medical doctors and participants) and ICT team.

Figure 3

Architecture and main components of the proposed solution: real-time data are collected from the wearable device, which communicates with a smartphone to provide immediate alerts regarding health parameters such as heart rate, respiratory rate, temperature, and fall detection. The data flows into a robust Data Persistence Layer that employs both Redis for fast data caching and a main database for long-term storage. The backend system utilizes the MQTT protocol to effectively manage alarm notifications, ensuring timely responses to critical health events. Finally, the analytics component processes the collected data, enabling comprehensive data analysis.

Figure 4

Sequence diagram describing the reading of a signal from the device (e.g., a temperature sample at 1/300 Hz, corresponding to a 5 min interval), the storage of the data in the Cloud system, the generation of an alarm, and the propagation of the alarm to the mobile app and the web client.

Figure 5

The diagram illustrates the data flow from raw to processed data, showcasing the payload format along with examples. Each payload includes identifiers for both the account and the device, the timestamp of the measurement or event, and corresponding values that vary based on the type of device.

Figure 6

The IoT device comprises a sensor-equipped t-shirt (A) and a data collection and preprocessing unit (B). This unit gathers and transmits data to a mobile smartphone application via Bluetooth, which then forwards the information to the Cloud platform.

Figure 7

(A) Boxplots of the statistical distribution of age, divided into Male (blue) and Female (pink), and into the three macroregions where the project took place: North (green), Center (orange), and South (violet) of Italy. Each boxplot bar represents the statistical distribution of the corresponding dataset, including the mean (white line), the 68th percentile (the border of the box), and the max/min values (the extreme lines) (B) histogram distribution of recording hours per participant (device). Each participant recorded an average of 26.23 h for a total of 23,394 h over about 1 year of experimentation; (C) distribution of the number of active devices over the duration of experimentation.

Figure 8

Some screenshots of the web Interface: (A) the dashboard of alarms and (B) details page for person related information: registry, health status, ECG (amplitude [mV] and time [s]) and monitored parameters during working shift.

Figure 9

Some relevant workflows of the application. (A) participant onboarding, (B) checking of the health status, (C) alarm management.

Figure 10

(A) histogram of ingestion time, (B) scalability analysis of read operations, (C) comparison of TTFB between normal case and use of web cache.