Using IoT for heartbeat tracking is nothing new. All of us have probably seen or worn some kind of fitness tracker, chest band, or smartwatch, which are all part of the IoT. Wearables are on the rise, and this surge in popularity has several causes, from advances in sensor technology and software to increased demand for remote health monitoring. The pandemic’s aftermath, the aging population, and the shortage of healthcare professionals all add to the necessity of precise and efficient health tech solutions.
But just how reliable and precise are they? Can they make healthcare more patient-centric and efficient, or just add more chaos to well-oiled processes? We at Techstack are going to share our knowledge and use cases on IoT heart rate monitoring, and then we’ll let you decide whether it’s worth the investment.
IoT in Health Monitoring: What’s with the Market
The World Health Organization estimates a shortfall of 10 million health workers by 2030, and with one in four people in Europe and America aged 65 or more by 2050, the issue of efficient and patient-centric healthcare is critical.
IoT healthcare technology can potentially soften the hit on the healthcare system. While complex IoT health monitoring systems are still making their way toward mass adoption, heartbeat tracking solutions, sugar level checkers, blood pressure monitors, and other individual devices like these have already gained momentum.
In fact, wearables are a major driver of IoT healthcare tech, and their number is increasing each month: the market is projected to reach more than $184 million by 2031 (from $54.8 million in 2020), with the fitness and sports industry as the dominant segment, and healthcare following closely.
Within this range of options, IoT heart rate monitoring stands out as one of the most widespread applications of wearables. Heartbeat tracking devices are projected to bring in around $45 billion in revenue by the end of 2033.
What’s remarkable is that hospitals and clinics, not individuals, are the primary demand drivers for these IoT devices. The reason is the acute need to optimize care for chronic patients, make treatment more affordable distance- and money-wise, prevent rather than cure, better control prescriptions and medications, and more.
That’s why we expect the trend to persist and evolve into more complex patient-centric systems that will combine IoT and telemedicine, as well as advanced data processing solutions. Let’s see how companies leverage IoT to track heartbeat and how software providers like Techstack address technical challenges.
Leveraging IoT for Real-Time Heart Rate Monitoring
The recreational use of heartbeat tracking solutions might sound more familiar, but in fact, clinics and hospitals use them (or encourage their patients to do so) in a number of health-related cases:
- to monitor patients with cardiovascular disorders
- to provide better post-surgery care
- to remotely monitor chronic disease patients like those with diabetes, hypertension, obesity, etc., and prevent them from needing frequent hospital checks
- to better manage rehabilitation and physical therapy
While individual use of heartbeat tracking solutions centers around smartwatches, Fitbit trackers, and Garmin wearables, hospitals focus more on ECG monitors, implantable loop recorders, cardioverter defibrillators (ICDs), and cardiac telemetry wearables. These IoT devices can monitor heart rate continuously in real-time, be symptom-enabled, or even serve as first aid (like ICDs). This all brings us to the following benefits of IoT heart rate monitoring that healthcare professionals value the most:
- streamlined information recording
- continuous, real-time monitoring of chronic disease patients
- reduced need for personal visits
- early detection of complications and disorders
- optimized prescription management
To reap these benefits, organizations need to leverage the latest machine learning and big data technologies when dealing with IoT product engineering. Ideally, the hospitals need a full-cycle system with an IoT device on one end and a healthcare professional on the other:
Let’s dive a bit deeper into the technical details of IoT heartbeat tracking, as shown in this scheme.
The technical side of IoT heart monitoring
Data is a cornerstone of any IoT solution. Gathering, processing, and analyzing it properly is the key advantage—and the key challenge—of modern software. By harnessing the power of AI, we can effectively handle large data sets generated by connected devices. Here’s what the IoT system looks like based on the data journey within it.
IoT devices, like bands or patches, collect raw data with the help of ECG and PPG sensors made of LEDs and photodetectors (PDs). PDs capture changes in the vessels by absorbing the long wavelengths from LEDs, and through that, PPG sensors measure volumetric changes in blood flow. The gathered data usually includes heart rate, rhythm, and oxygen level. For more precision or IoT-enabled heart rate optimization, we use ECG sensors, which measure the heart’s electrical activity, or a combination of both (ECG and PPG).
Data signals gathered by sensors are transmitted to the processing unit via a wireless connection, such as Bluetooth, Wi-Fi, satellite, or cellular network. What we need here is high range and bandwidth with minimal costs and power consumption. A relatively new option for IoT devices is low-power wide-area networks (LPWAN) like LoRa and SigFox, but they are not designed for real-time monitoring. Bluetooth and Wi-Fi connections are still more widespread in IoT heartbeat tracking.
Data processing involves storage, cleaning, classification, and tagging. The volume of IoT heartbeat data imposes specific requirements on the processing location. The options are:
- on-device processors or microcontrollers where data is filtered locally and then transmitted further to the central server
- scalable cloud-based servers when the data goes directly from the wearable devices to the cloud server
- fog computing, which provides a layer between the local server or gateway and the cloud, reducing latency and bandwidth range
- event-triggered data processing with serverless cloud architecture (Function-as-a-Service paradigm), where a function is executed in response to a certain event, i.e., the transmission of new heartbeat data
- distributed architecture, where data is processed in multiple system nodes: the edge device, the local server, and the cloud
The decision is largely individual and depends on the original system architecture, data volume, privacy requirements, and other factors.
To analyze information from IoT heartbeat monitoring devices, we need the power of machine learning. With the help of ML and deep learning, the system can classify data, identify patterns, detect abnormalities, and make predictions based on historical data. For example, convolutional and long-short memory neural networks are used to analyze and classify ECGs. As a part of a health monitoring system, AI can send alerts and notifications when detecting anomalies and trigger subsequent actions (e.g., alerting a person, messaging a doctor or facility, enabling first aid protocol, etc.).
A user-friendly interface is essential to making the most of the information generated by an IoT heartbeat solution. Visualizations, graphs, notifications, and alerts assist end users, whether individuals or companies, in accurately and promptly interpreting the data.
Properly processing and analyzing IoT data is crucial. This process is technically complex and requires expertise in IoT product development. Techstack, a company experienced in developing IoT and telemedicine solutions, has a case to prove this point.
A team of eight Techstack experts designed a solution to collect and analyze heart rate data for healthcare devices using serverless microservice architecture on the AWS cloud. One extra challenge was integrating a new module with the existing WPF application and migrating the solution to the serverless architecture. The Techstack team’s expertise in data management proved invaluable in finding the solution to process large heart rate datasets efficiently. As a result, our healthcare client improved data analysis and scalability, boosting the system performance and streamlining internal processes.
So, what is the primary challenge in building IoT heartbeat tracking solutions? Why don’t more healthcare companies leverage these devices or more complex IoT systems? On top of development costs and the shortage of tech talent, there are a few more challenges to consider.
Challenges of Heartbeat Tracking with IoT
The most popular wearable solutions for heart monitoring use either PPG or ECG sensors, so we’ll use these types of biometric technologies to illustrate challenges in IoT health monitoring and heartbeat data analysis.
PPG sensors are used in watches, bands, earrings, and other devices, with chest straps considered the most accurate among them. However, for clinical IoT heart monitoring, peak accuracy from both PPG and ECG sensors is a must. The main issue here is removing optical noise, as you must filter the heartbeat signal from other noises to properly process data. Machine learning helps resolve this and boost the device's accuracy, which we’ll discuss in more detail in the next chapter.
As we said, data forms the foundation of any IoT device. In heartbeat tracking solutions, transmitting data in real-time while maintaining low latency is essential. There are no off-the-shelf solutions for products like these: every case requires building the most appropriate system architecture and then deciding on the best connectivity options. For clinical heartbeat monitoring systems, the challenge becomes even greater as they have to work in areas with low connectivity while still delivering instant feedback.
Another issue is retaining data privacy and security. IoT heartbeat-tracking devices are quite vulnerable to cyber-attacks due to numerous ‘entry points’ for hackers, from weak authentication protocols to network security and outdated software.
The collection and processing of patient data is another concern, as healthcare organizations and third parties (like labs or research institutions) do not have unified data privacy frameworks. Interoperability is an issue, too, as it requires different parts of the IoT system to exchange data securely. And we won’t even start on regulatory compliance, which presents a major challenge for less experienced IoT software providers. You will find more on Techstack’s strategies for data privacy in the next chapter.
Integration with other systems
To use IoT healthcare technology on the enterprise level, we must seamlessly integrate heartbeat tracking functionality into more complex systems and ensure interoperability between all elements. With different manufacturers and multiple tech frameworks, creating a unified ecosystem is a serious issue. In fact, integrating a new module into the existing heart monitoring solution was one of the most intricate tasks in Techstack’s use case described earlier.
Overall, IoT heartbeat monitoring involves quite a few tech challenges that demand solid expertise in the field. Here’s how we at Techstack approach them.
Strategies for Effective Heart Rate Monitoring
As an experienced software development company, Techstack relies on specific time-proven strategies to build IoT healthcare solutions, from refining sensor accuracy to ensuring data security and compliance. Let’s zero in on some of them.
Enhancing accuracy in heartbeat tracking
The precision of IoT heart rate monitoring solutions depends on sensor accuracy and proper data processing and analysis. The following factors influence the signal of PPG and ECG sensors:
- sensor quality
- noise filtering
- signal processing
- power management
To guarantee high quality, we get sensors from reliable manufacturers and upgrade them regularly. Greater precision can also be achieved by combining PPG and ECG sensors in one device. In this case, we need specific algorithms for data fusion, as the signal comes from different sensors.
Noise filtering comes next. Adaptive filters can adjust their parameters in real-time based on the characteristics of the incoming signal, fixing varying noise levels and signal distortions. We also use notch filtering for devices with ECG sensors to remove unwanted frequencies like powerline hum.
Signal processing requires a separate strategy as well. At this stage, we at Techstack leverage the power of AI to tackle a range of tasks, including:
- ECG waveform analysis, comparison, and pattern recognition
- extracting features of specific events
- anomaly detection
- real-time feedback and alerts
Here’s a practical example of deploying ML. After certain cardiac surgeries (e.g., catheter ablation), patients need follow-up ECG monitoring and regular outpatient visits. Sometimes, though, they can use an IoT heart rate monitoring device instead of coming for an in-person checkup. A deep learning model of a device like that can preprocess and classify ECG recordings, sending objective and timely reports to doctors. The researchers on the case report a 98.2% accuracy of their deep learning-based device.
To sum up, sensor accuracy can be enhanced through high-quality hardware and robust strategies for data gathering, processing, and analysis.
Focusing on data security and privacy
Enhancing IoT security is one of the persisting trends in healthcare in its movement toward higher telemedicine adoption. Security and privacy frameworks may include:
- access controls
- authentication methods
- data anonymization
- other regulatory compliance
- secure product development
The development process lays the foundation of a secure IoT heart monitoring solution. At Techstack, our strategy is to adhere to DevSecOps for IoT products. This means we prioritize security when building solutions and test continuously from the get-go.
Regulatory compliance is also the developers’ responsibility, as some frameworks and protocols must be incorporated into the system architecture from the start. In addition to GDPR, HIPAA, and the ISO/IEC 27000-series, we adhere to HITRUST CSF. This global framework unifies the security and privacy requirements of other regulators and provides a prescriptive set of controls for advanced healthcare technology and data analysis.
Another strategy is using blockchain for secure data analysis and sharing. Coupled with an IPFS system for data storage, blockchain can solve the issues of network failure, data tampering, anonymization, data loss, and others. Finally, AI can serve as an extra layer of protection, detecting and preventing cyber threats to the IoT system in real-time.
Final Thoughts: Future of IoT Health Monitoring
Although typically associated with fitness trackers, smartwatches, and other gadgets, heartbeat tracking solutions hold tremendous potential for healthcare companies. Integrating IoT with telehealth services can bridge the gap between providers and patients, make healthcare more affordable and cost-effective, and help solve the dire shortage of health workers.
In the coming years, the industry and tech providers will aim to optimize healthcare delivery through IoT and telemedicine. If you, too, are interested in efficient, patient-centric solutions, Techstack can become your trustworthy tech partner. Contact us to learn more about our expertise in creating reliable and compliant healthcare IoT products.