IEEE Signal Processing - May 2018 - 111

In this article, we elaborate on the
"signal processing for wearable health
care" assignments for students enrolled
in the courses 1) advanced signal process-
ing (ASP) and 2) adaptive signal process-
ing and machine intelligence (ASP-MI),
both taught at a later stage undergraduate
and postgraduate level in the Department
of EEE at Imperial College London.

The course
Data analytics courses increasingly
attract a wider profile of undergraduate
and M.Sc. students from across engi-
neering and computing areas, and for
best learning outcomes, it is critical to
strike a balance between mathematical
rigor and principles of the physical
world (e.g., signal-generating mecha-
nisms). We do so through structured
computer exercises, as illustrated in Fig-
ure 1. The proposed framework was
designed to provide a stimulus for enqui-
ry into the algorithmic requirements to
achieve certain digital signal processing
tasks, and, hence, the approach becomes
a path to learning, discovery, and con-
solidation of knowledge. In our case,
MATLAB assignments are designed so
as to explore real-world applications in
audio processing and biomedical and
power engineering.

extracting and analyzing both the heart
rate and breathing rate of a person from
his or her ECG.
In other words, as humans are
beginning to merge with the technolo-
gies they have created, there is an
urgent need to keep abreast with sig-
nal processing education and explore
innovative and participatory ways to
present the taught material to highlight
the central role of signal processing as
an enabling technology for many of
these advances.

Why is the ECG so convenient?
Among the different physiological sig-
nals available for noninvasive record-
ings, we chose the ECG for several
reasons. First, the ECG has significant
value both in clinical and nonclinical
applications because of the ease of
interpretation, reliability, and physiolog-
ical meaningfulness. In nonclinical
applications, for example, the activity of
the autonomic nervous system, which is

Rigor

ASP

Signal processing
for wearable health care

Practicality

Intuition

Figure 1. Striking a balance among mathematical rigor, engineering intuition, and practical applicability is a key challenge in the ASP curriculum.

11
Amplitude (mV)

The emergence of inexpensive physio-
logical sensors, which are currently
embedded in advanced wearable devic-
es, presents new opportunities for their
use in real-time and continuous-health
monitoring applications, with signal
processing being the key enabling tech-
nology. For instance, the growing popu-
larity of devices such as the Apple
Watch and Fitbit are inspiring student
engineers and entrepreneurs to develop
novel applications for health-care pur-
poses. These opportunities come with
significant challenges related to the
detection, estimation, and classification
of relevant biomedical features.
To expose our students to these chal-
lenges and equip them with the neces-
sary tools and analysis frameworks
to address these issues, we have con-
structed an assignment that focuses on

related to stress, can be estimated from
heart rate variability (HRV), a time
series obtained from the time difference
between consecutive R-peaks in the
ECG [9]. Figure 2 shows an ECG wave-
form with the corresponding P-, R- and
T-waves. The time interval between
consecutive R-peaks is referred to as the
RR-interval (RRI).
Besides the heart rate, RRIs can be
used to obtain the breathing rate of a
person through a phenomenon known as
respiratory sinus arrhythmia (RSA). In
signal processing terms, breathing modu-
lates the cardiac function by respiratory
effort that can be readily observed by
the acceleration of the heart rate during
inhalation (breathing in) and the decel-
eration of the heart rate during exhalation
(breathing out). In addition, the strength
of RSA in an individual can be used to
assess cardiovascular health and psycho-
logical stress [10].
Finally, an ECG trace can be eas-
ily obtained from a person without

RR Interval

R
10

T
P

0.5

1
Time (s)

1.5

Figure 2. An example ECG trace with visible P-, R- and T-waves.
IEEE Signal Processing Magazine

May 2018

111

Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018