Systems, Man & Cybernetics - April 2016 - 17

Vital-Signs
Monitoring

ECG

Blood
Pressure

Wellness
Management

Fall
Detection

Blood
Pressure

FSR
Sensor

Accelerometer Accelerometer

Passive
Infrared

Ultrasonic

Middleware

ECG

Blood
Pressure

FSR
Sensor

EMG

Accelerometer

Passive
Infrared

Ultrasonic

Kitchen
Bath

Living
Room

Bed Room

Light
Sensor

Motion
Sensor

Figure 1. multiapplication multicontext inferencing in a sensor-rich pervasive health-care environment.

minimizes the energy overhead, while still ensuring that
the applications' needs for high-quality context inferences are met. To enable such a dynamic and automated
association between application requirements and the
available sensor resources in any environment, we make
the following two key contributions in this article.
◆ First, we suggest the use of an explicit functional
model to relate the accuracy of any inferred context
to a measure of uncertainty about the true values of
the sensor data.
◆ Then, we develop and evaluate an optimizationbased heuristic that uses the model to dynamically
select both a set of sensors and the parameters of the
sensors to satisfy the context requirements of multiple context-aware applications, while minimizing the
energy overhead of sensor-data transmission.
A Formal Model for Context Inference
Our goal is to determine the automated adaptation of
sensors so as to reduce the energy overheads associated
with data transmission from the sensors without compromising the context requirements of any of the health and
wellness applications. In achieving this objective, the accuracy or fidelity requirements associated with the context
requirement are highly application dependent; for

example, the fall-monitoring application may find an accuracy of 90% acceptable (i.e., it misses one out of ten cases
of falls/stumbles), and the vital-signs-monitoring application may require a much higher accuracy of 99.999%,
while the wellness application may satisfied with a much
lower fidelity of 50% in detecting the amount walked during the day (i.e., it is okay to under- or overcount the
amount of time spent walking by . 50% ). Accordingly,
we must first establish a formal functional model that
relates the underlying accuracy/fidelity of the sensor data
to the accuracy of the specific inferred context.
Given that a context metric is inferred from the combination of values from multiple low-level sensors, we
define the quality of inference (QoINF) as the error probability in estimating a context state, given the imprecision i n t he va lues of t he cont r ibut i ng sen sor s.
Concretely, we compute QoINF based on the average
estimation error of the context; alternative definitions of
accuracy (such as the percentage of false positives or
false negatives) are equally reasonable and do not affect
the remaining description of our model. Two key observations drive our use of QoINF.
1) While different combinations of sensor types may be
used to infer the same high-level context at different
levels of accuracy, it is almost universally true that the
Ap ri l 2016

IEEE SyStEmS, man, & CybErnEtICS magazInE

17



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - April 2016

Systems, Man & Cybernetics - April 2016 - Cover1
Systems, Man & Cybernetics - April 2016 - Cover2
Systems, Man & Cybernetics - April 2016 - 1
Systems, Man & Cybernetics - April 2016 - 2
Systems, Man & Cybernetics - April 2016 - 3
Systems, Man & Cybernetics - April 2016 - 4
Systems, Man & Cybernetics - April 2016 - 5
Systems, Man & Cybernetics - April 2016 - 6
Systems, Man & Cybernetics - April 2016 - 7
Systems, Man & Cybernetics - April 2016 - 8
Systems, Man & Cybernetics - April 2016 - 9
Systems, Man & Cybernetics - April 2016 - 10
Systems, Man & Cybernetics - April 2016 - 11
Systems, Man & Cybernetics - April 2016 - 12
Systems, Man & Cybernetics - April 2016 - 13
Systems, Man & Cybernetics - April 2016 - 14
Systems, Man & Cybernetics - April 2016 - 15
Systems, Man & Cybernetics - April 2016 - 16
Systems, Man & Cybernetics - April 2016 - 17
Systems, Man & Cybernetics - April 2016 - 18
Systems, Man & Cybernetics - April 2016 - 19
Systems, Man & Cybernetics - April 2016 - 20
Systems, Man & Cybernetics - April 2016 - 21
Systems, Man & Cybernetics - April 2016 - 22
Systems, Man & Cybernetics - April 2016 - 23
Systems, Man & Cybernetics - April 2016 - 24
Systems, Man & Cybernetics - April 2016 - 25
Systems, Man & Cybernetics - April 2016 - 26
Systems, Man & Cybernetics - April 2016 - 27
Systems, Man & Cybernetics - April 2016 - 28
Systems, Man & Cybernetics - April 2016 - 29
Systems, Man & Cybernetics - April 2016 - 30
Systems, Man & Cybernetics - April 2016 - 31
Systems, Man & Cybernetics - April 2016 - 32
Systems, Man & Cybernetics - April 2016 - 33
Systems, Man & Cybernetics - April 2016 - 34
Systems, Man & Cybernetics - April 2016 - 35
Systems, Man & Cybernetics - April 2016 - 36
Systems, Man & Cybernetics - April 2016 - 37
Systems, Man & Cybernetics - April 2016 - 38
Systems, Man & Cybernetics - April 2016 - 39
Systems, Man & Cybernetics - April 2016 - 40
Systems, Man & Cybernetics - April 2016 - 41
Systems, Man & Cybernetics - April 2016 - 42
Systems, Man & Cybernetics - April 2016 - 43
Systems, Man & Cybernetics - April 2016 - 44
Systems, Man & Cybernetics - April 2016 - 45
Systems, Man & Cybernetics - April 2016 - 46
Systems, Man & Cybernetics - April 2016 - 47
Systems, Man & Cybernetics - April 2016 - 48
Systems, Man & Cybernetics - April 2016 - 49
Systems, Man & Cybernetics - April 2016 - 50
Systems, Man & Cybernetics - April 2016 - 51
Systems, Man & Cybernetics - April 2016 - 52
Systems, Man & Cybernetics - April 2016 - 53
Systems, Man & Cybernetics - April 2016 - 54
Systems, Man & Cybernetics - April 2016 - 55
Systems, Man & Cybernetics - April 2016 - 56
Systems, Man & Cybernetics - April 2016 - Cover3
Systems, Man & Cybernetics - April 2016 - Cover4
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