Sensor fusion brings situational awareness to health devices

A data-centric sensor fusion architecture is essential for building situationally-aware applications.
With sensors and sensing applications proliferating in the modern world, traditional data silos becoming available through service-oriented architecture (SOA), and new social data becoming accessible through apps, we now have the ability to consolidate large volumes of data to better comprehend and analyze the environment around us. By consolidating and integrating this data in real time, we have opportunities to develop new suites of smart applications that can change the way we manage our health, drive our cars, track inventory–the possibilities are endless.

But it will require several new technologies to make this happen: the use of traditional sensor fusion techniques for acquiring and organizing that information and techniques for “situational awareness” that will make the system as a whole and the device acquiring and using that data aware of the specific environment in which that data is to be used.

What is situational awareness?
Situational awareness (SA) refers to a system being aware of its surroundings, its users and their working context, with the ability to show relevant information that will assist users in decision making. SA creates a model that captures the system state and provides an understanding of how events affect that state. A good SA model integrates relevant information from multiple sources, determines the relative importance of different events, and projects the state of the system based on events. To build a system that is situationally aware, the model must be accurate and must update quickly to reflect current events.

SA systems are not the same as systems that do multi-sensor data fusion. Multi-sensor data fusion techniques combine data from multiple sensors, providing more accurate measurements of the environment.

However, a multi-sensor system does not understand the context of the user or the state of the system, and has little intelligence to process the data. Consider for example a device that measures body temperature from multiple places on the body.

The device may use sensor fusion techniques to eliminate faulty readings and provide the most accurate body temperature reading based on its sensor-fusion algorithms. A SA system, by realizing that one particular sensor always returns an outlying reading, may recommend the user to check if it is working or properly connected.

An SA system by itself does not provide value. It is entirely possible for an operator to have an excellent SA system and still make an incorrect decision. This could be due to poor strategies, poor training, or poor interpretation, among other reasons.

Sensor fusion brings situational awareness to health devices
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(For more info on Figure 1, go to “How Wireless Technology Will Change Global Health,” Leslie A. Saxon, M.D., September 27, 2010, Fast Company at ***a***obal-health-leslie-saxon-md.)

Where possible, an SA system needs to take the next step: it needs to recognize patterns and either take autonomous action or proactively direct operator attention. Pattern-matching technology or machine-learning techniques can recognize correlated events and assist with delivering awareness to the operator. With experience, the pattern-recognition/action-selection sequence can become automated and reduce demands on the operator.

As an example, in health care, practitioners of bio-informatics have recognized the value of providing situational awareness in their sensing applications. By correlating–in real time–sensor data for ECG, blood oxygen, blood pressure, respiration or pulse, and applying patterns to monitor events of interest, we can build systems that can be used to manage patients with chronic conditions (such as heart diseases or diabetes) and ***a*** alert the patient or the medical provider for anomalous events.

The specific techniques used by each application to provide SA will vary. However, to deliver such awareness of the environment to real-time systems, application architects and system developers must follow the guidelines described below.

Integrating and interpreting information

With the informational deluge, there is a gap between the large volume of sensing data produced and a human’s ability to process the information. Ironically, overwhelmed or under-trained operators may be even less informed with numerous, highly capable sensing devices than with fewer, simpler ones. For the information to be processed correctly, it must be integrated and interpreted correctly. For example, if a home-based monitoring system were to be developed for managing cardiac patients, it will not be possible to be continually watching for ECG, pulse, or blood pressure reading and trying to detect events of interest–the system will not be usable or valuable. What is required is a system that integrates and correlates data from different medical devices in real time.

Sensor fusion brings situational awareness to health devices
Click on image to enlarge.

(For more info on Figure 2, go to

However, integrating information from distributed sensor systems such as these medical devices is more complicated than integrating data in traditional enterprise systems.

Integration of heterogeneous architectures: Unlike traditional enterprise systems, embedded and RTOS markets for operating systems are heavily fragmented; typical sensing systems use a range of operating systems (INTEGRITY, VxWorks, LynxOS, TinyOS, …), devices and network protocols (such as UDP, TCP, Bluetooth, Infiniband, wireless, radio) and middleware protocols (JMS, HTTP, DDS, …). Often, no single sensing system can provide a comprehensive event-detection or monitoring system. Instead, a combination of best-of-breed components–each designed for a specific purpose, operating system, and network protocol–must together provide a comprehensive solution. Data from multiple sources must be organized and prioritized to support distributed, cooperative decision-making.

Dynamic, evolvable, and type-safe data representation and encapsulation: These SA models must allow for the collection of a variety of data types from sensor probes. To address the various data types and characteristics of information collected, as well as possible schema evolution, the SA model must provide an approach for having self-describing data or a similar mechanism that allows clients to discover and process the schema dynamically. What this means is that a SA system cannot define a unified and complete data structure upfront, which all the medical devices use. What is required is a methodology where different devices/sensors can still use different data types, and can still be integrated without complex code.

Event correlation and aggregation: SA is about inferring activity of interest–events–either by monitoring for known abnormalities or intelligently adapting to the environment to infer abnormal events. To do so, events from different sensors must be correlated and aggregated. For example, some events are immediately recognizable (such as systolic blood pressure > 200). Other events are characterized by intermittent activity spanning a much longer timeframe (hours, days, or even weeks) and may not even be identified as an event until a vast collection of records are considered in aggregate (for example, systolic blood pressure = 160 but has been steadily rising for the last week). Thus, while every event has a definitive beginning, this starting point is not always discernible at the time of occurrence. Neither is the time it takes for such an event to unfold or its ultimate duration predictable. Event-dete ***a***ction tools are needed to normalize events from differen ***a***t sources and correlate them by time or distance to identify possible information of interest.

Data-centric architecture
The architecture for connecting sensors and distributing their data can either follow a message-centric or data-centric design pattern. In a message-centric model, the infrastructure does not understand your data. The infrastructure carries “opaque” content that varies in structure from message to message. Because the messages have no identity, they are indistinguishable to the infrastructure. They also lack lifecycle management. Often used for Enterprise Service Bus (ESB) messaging, the Java Message Service (JMS) API and Advanced Message Queuing Protocol (AMQP) are examples of such message-centric technologies. For these technologies, there is no need for a semantic understanding of the data.

A data-centric sensor fusion architecture uses the principles of a global data-space. It resembles a virtual, centralized database. From the operator’s viewpoint, the data collected from different sources appears as if it is from a single source. The operator does not have to worry about accessing the data source from each sensor, normalizing the data, etc. All sensors contribute their data to the global data-space. Applications access this data, similar to a database, with no concern for the distributed nature of the system.

With a data-centric design (see Figure 3), developers specify only the data requirements–inputs and outputs–of each subsystem. Applications focus only on the data they intend to consume or produce and leave the mechanism to procure, normalize, filter, and enrich the data to a data bus. What this implies is that while developing applications for providing situation awareness, say tracking cardiac health, we do not need to worry about how to connect to the medical device, how to do endian conversion, how to transform data types, how to poll for the next sample, how to demarshall messages on the socket to a data structure–the data-centric middleware should be able to manage all these operations.

Sensor fusion brings situational awareness to health devices
Click on image to enlarge.

In a data-centric model, the infrastructure does understand your data. In particular, it understands:
What data schemas will be used.

Which data items are distinct from which others.

The lifecycles for the data items.

How to attach behavior (such as filters and Quality of Service) to individual data items.

A data-centric architecture removes the tight coupling between the data producer and consumer, thus making the design scalable and evolvable. Examples of data-centric design technologies include the Data Distribution Service (DDS) for Real-Time Systems standard and the Real-Time Publish-Subscribe (RTPS) wire protocol, both from the Object Management Group (OMG).

This requirement is critical for building a situational aware system, which becomes more aware as it senses more sources and analyzes them in real time. With a data-centric architecture, the middleware understands the data; only the relevant data is put on the wire, avoiding performance bottlenecks. As an example, while the sensor may be making temperature readings at 5 Hz, we can use the middleware to only send the information at 1 Hz or when the temperature exceeds 99 F. This capability is not conveniently possible without having a data-centric architecture.

Analyzing data in real time

To provide situational awareness, systems need to aggregate, correlate, cleanse, and process sensor data in real time.

New technologies such as Complex Event Processing (CEP) allow users to perform traditional database and data-mining tasks like data validation, cleaning, enrichment, and analysis without first persisting the data. By using CEP, application developers can query, filter, and transform data from multiple sensors for event detection in real time. With the ability to automate pattern monitoring in real time through CEP, operators can develop autonomic event-response mechanisms and get critical information for isolating events.

For example, sticking with the cardiac tracking application, we can have a system that integrates data from medical devices measuring ECG, temperature, pulse. By using CEP, we can define watching for events with patterns of interest: temperature < 97 AND pulse > 110.

SA systems often need to add new queries in a monitoring system without recompiling code or restarting the system. With CEP, operators can develop new pattern filters without recompiling the code. This feature becomes very critical in use-cases where the situation is dynamic, for example, in agent monitoring or the clinician adding new patterns for monitoring.