Health Monitoring and Management Using Internet-of-things (IOT) Sensing With Cloud-based Processing: Opportunities and Challenges (Computer/Electronics Project)

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Among the panoply of applications enabled by the Internet of Things (IOT), smart and connected  health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively  mined, such information can bring about a positive transformative change in the health care landscape.

In  particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing  algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-and-treat reactive paradigm, to a proactive framework for prognosis of  diseases  at  an  incipient  stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the   specific circumstances and needs of the individual, and (c) help reduce the cost of health care  while simultaneously improving outcomes. In this paper, we highlight the opportunities and  challenges for IOT in realizing this vision of the future of health care.


Figure 1 illustrates the system architecture for a remote health monitoring system, whose major components we describe next:

Data Acquisition is performed by multiple wearable sensors that measure physiological biomarkers, such as ECG, skin temperature, respiratory rate, EMG muscle activity, and gait (posture). The sensors connect to the network though an intermediate data aggregator or concentrator, which is typically a smart phone located in the vicinity of the patient.

The Data Transmission components of the system are responsible for conveying recordings of the patient from the patient’s house (or any remote location) to the data center of the Healthcare Organization (HCO) with assured security and privacy, ideally in near real-time.

Cloud Processing has three distinct components: storage, analytics, and visualization. The system is designed for long term storage of patient’s biomedical information as well assisting health professionals with diagnostic information.

Components of a Remote Patient Monitoring System that is based on an IOT-Cloud Architecture.

Components of a Remote Patient Monitoring System that is based on an IOT-Cloud Architecture.


Physiological data is acquired by wearable devices that combine miniature sensors capable of measuring various physiological parameters, minor preprocessing hardware and a communications platform for transmitting the measured data.

The wearability requirement, poses physical limitations on the design of the sensors. The sensors must be light, small, and should not hinder a patient’s movements and mobility. Also, because they need to operate on small batteries included in the wearable package, they need to be energy efficient.


Data aggregated by the concentrator needs to be transferred to the cloud for long term storage. Offloading data storage to the cloud offers benefits of scalability and accessibility on demand, both by patients and clinical institutions. Also, utilized with analytics and visualization (described in subsequent sections), cloud hosting and processing can reduce costs at HCOs and provide better diagnostic information.

  • Hybrid Cloud/Cloudlet Architecture.
  • Context-Aware Concentration via Smart Devices.
  • Privacy of the Data Concentrator.
  • Secure Data Storage in the Cloud.


Compared with the lab and office based measurements that are the workhorses of current clinical medical practice, wearable sensors can readily incorporate multiple physiological measurements and enable gathering of data with much finer temporal sampling over much longer longitudinal time scales. These rich datasets represent a tremendous opportunity for data analytics: machine learning algorithms can potentially recognize correlations between sensor observations and clinical diagnoses, and by using these datasets over longer durations of time and by pooling across a large user base, improve medical diagnostics.

Analytics Workflow for Systems Integrating Wearable Sensor Technology into Clinical Practice.

Analytics Workflow for Systems Integrating Wearable Sensor Technology into Clinical Practice.


It is impractical to ask physicians to pore over the voluminous data or analyses from IOT-based sensors. To be useful in clinical practice, the results from the Analytics Engine need to be presented to physicians in an intuitive format where they can readily comprehend the interrelations between quantities and eventually start using the sensory data in their clinical practice. Visualization is recognized as an independent and important research area with a wide array of applications in both science and day to day life.


In this paper, we reviewed the current state and projected future directions for integration of remote health monitoring technologies into the clinical practice of medicine. Wearable sensors, particularly those equipped with IoT intelligence, offer attractive options for enabling observation and recording of data in home and work environments, over much longer durations than are currently done at office and laboratory visits.

This treasure trove of data, when analyzed and presented to physicians in easy-to-assimilate visualizations has the potential for radically improving healthcare and reducing costs. We highlighted several of the challenges in sensing, analytics, and visualization that need to be addressed before systems can be designed for seamless integration into clinical practice.

Source: University of Rochester
Authors: Moeen Hassanalieragh | Alex Page | Tolga Soyata |  Gaurav Sharma |  Mehmet Aktas| Gonzalo Mateos| Burak Kantarci |  Silvana Andreescu

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