A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective

Abstract

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

Keywords: clinical IoT; digital health; health management; health monitoring; healthcare analytics; medical decision support.

Fig. 1.

Modern smart healthcare applications are intricate multidimensional systems that not only focus on the personalized acquisition of physiological data but also incorporate information from various external sources such as past records of patients from their hospitals, research and educational resources, and even environmental information from smart city applications.

Fig. 2.

Graphics showing typical symptoms of Parkinson’s (left) and Huntington’s (right) disease that are the focus of the multisensor case study that we will use to illustrate the ideas discussed in this paper. Based on [122], [123].

Fig. 3.

High-level system architecture illustrating HIoT integration into clinical healthcare. IoT sensors record measurements for a range of physiological and health-related physical attributes. The data is communicated over the network and aggregated in the cloud by IoT concentrators. Cloud-based analytics and inference algorithms operating on the data provide decision support to physicians via visualization interfaces, dashboards, and real-time alerts to individual users. Security and privacy solutions must be implemented to include other components, ensuring data protection from acquisition to storage.

Fig. 4.

A graphic showing five different locations on the body for applying BioStampRC sensors in our PD/HD case study [152] (left) and a participant wearing sensors at these locations for in-clinic assessment [125] (right).

Fig. 5.

Screen shot of MC10 web portal used in our PD/HD case study (named Sensor-MD Condensed 2). We can identify six subjects with unique subject ID (005), sex (M/F), and age (years) indicated on the right side. Total duration of recorded data is indicated on the right of an icon with a green tick mark. Clicking on the green tick marked icon downloads the data which can be utilized for analysis. On the far right, we can observe the statistics of the study showing the total number of subjects in the study followed by graphs showing number of male and female participants and age distribution.

Fig. 6.

Classification of the activity states in our PD/HD case study as percentage of time spent (total duration ≈ 46 hours) lying down, sitting, standing, and walking for Control, PD, HD, and prodromal Huntington’s disease (pHD) participants. “n” is the number of participants analyzed [125].

Fig. 7.

Data analysis performed for our PD/HD case study to assess inter-leg coordination. The plot shows the normalized vector cross-correlation [124] of the recorded accelerations from left leg and right leg sensors as a function of time lags for a HD and a control participant. A 2-step cycle is annotated and highlights stronger peaks for the controls at the one and two step intervals compared with participants with HD.

Fig. 8.

Visualization example from our PD/HD case study. A clock based visualization shows the variation in at-rest tremor magnitude in individuals with PD and compares it against controls and between on and off medication states. The tremor magnitude is quantified as the fraction of power in the 4 to 6.5 Hz frequency band (radial axis) for the principal axis acceleration recorded from MC 10 sensors affixed to the forearm over an hour duration (angular axis): (a) PD vs control (left), and b) PD ON vs OFF medication (right).