Ambient Diagnostics - Ambient Intelligence for Scientific Discovery - page 223

Ambient Diagnostics

Yang Cai 1 , Gregory Li 1 ,TeriMick 1 , Sai Ho Chung 1 , and Binh Pham 2

1 Carnegie Mellon University, USA
{ ycai,gregli,teri } @cmu.edu , [e-mail]
2 Queensland University of Technology, Australia
[e-mail]

1 Introduction

People can usually sense troubles in a car from noises, vibrations, or smells. An
experienced driver can even tell where the problem is. We call this kind of skill
'Ambient Diagnostics' .
Ambient Diagnostics is an emerging field that is aimed at detecting abnor-
mities from seemly disconnected ambient data that we take for granted. For
example, the human body is a rich ambient data source: temperature, pulses,
gestures, sound, forces, moisture, et al. Also, many electronic devices provide
pervasive ambient data streams, such as mobile phones, surveillance cameras,
satellite images, personal data assistants, wireless networks and so on.
The peripheral vision of the redundant information enables Ambient Diag-
nostics. For example, a mobile phone can also be a diagnostic tool. As the sounds
generated by breathing in asthma patients are widely accepted as an indicator
of disease activity [1, 2], researchers have investigated the use of a mobile phone
and electronic signal transfer by e-mail and voice mail to study tracheal breath
sounds in individuals with normal lung function and patients with asthma [3].
The results suggest that mobile phone recordings clearly discriminate tracheal
breath sounds in asthma patients and could be a non-invasive method of moni-
toring airway diseases.
It is challenging to extract just one bit of diagnosis (positive or negative) from
massive ambient data. First, we need pivotal heuristics or domain knowledge. In
many cases, the heuristics just serve as an early warning rather than an accurate
examination. For example, medical studies show that snoring may be related to
hypertension, cardiac dysfunction, angina pectoris and cerebral infarction. The
immediate rise in systemic blood pressure during snoring has been confirmed
by polygraphic recordings [60]. A 'snoremeter' could be added into a mobile
phone because it already has a microphone inside. It would provide valuable
early warnings for related diseases.
Second, we need physical heuristics that effectively filter out the trivial data
while only keeping the abnormities. Knowing the physical properties of the tar-
geted system would greatly benefit a diagnosis. For example, if we know that
the needle in a hay stack is metal, then we can work around the metal proper-
ties and make the hay disappear. Ideally, physical heuristics map the data to a
feature space that only displays limited interesting features. Determining which