Preliminary Analysis of the Use of Smartwatches

Preliminary Analysis of the Use of Smartwatches
Preliminary Analysis of the Use of Smartwatches for
Longitudinal Health Monitoring
Emil Jovanov
Abstract—New generations of smartwatches feature
continuous measurement of physiological parameters, such as
heart rate, galvanic skin resistance (GSR), and temperature. In
this paper we present the results of preliminary analysis of the
use of Basis Peak smartwatch for longitudinal health
monitoring during a 4 month period. Physiological
measurements during sleep are validated using Zephyr
Bioharness 3 monitor and SOMNOscreen+ polysomnographic
monitoring system from SOMNOmedics. Average duration of
sequences with no missed data was 49.9 minutes, with
maximum length of 17 hours, and they represent 88.88% of
recording time. Average duration of the charging event was
221.9 min, and average time between charges was 54 hours,
with maximum duration of the charging event of 16.3 hours.
Preliminary results indicate that the physiological monitoring
performance of existing smartwatches provides sufficient
performance for longitudinal monitoring of health status and
analysis of health and wellness trends.
Reliability of measurements and lack of context of
collected information is often cited as a major obstacle for
wider use of ubiquitous wearable health monitoring.
Applications of interests include analysis of physiological
rhythms, such as circadian rhythms, and long term
monitoring of trends of wellness indicators. In this paper we
present analysis of use and reliability of continuous
physiological measurements of Basis watch and comparison
with the standard polysomnographic monitoring systems.
I. INTRODUCTION
Physiological measurements are validated with two
standard
monitors
Zephyr
Bioharness
3
and
polysomnographic monitor SOMNOscreen+ during sleep.
The watch is waterproof to 5 ATM, which means that it can
be used in the water like a regular water-resistant sports
watch. However, capacitive screen commands and heart rate
monitoring are usually disturbed during washing or shower.
Therefore, during this experiment watch was removed during
showers, which introduces a missed sequence of records.

The smartwatch industry is fast growing, from USD
1.3 billion in 2014 to expected 117 billion in 2020 [1]. New
generations of watches, such as Basis Peak [2], feature
continuous measurement of physiological parameters, such as
heart rate, galvanic skin resistance (GSR), and temperature.
Basis Peak receives messages and notifications that are
potentially very useful for ubiquitous monitoring
applications. Basis watch wirelessly syncs with an iPhone or
Android phone app. The trend is expected to accelerate with
the introduction of Apple Watch [3].
Smart sensors, such as pedometers, sleep monitors, and
smartwatches, facilitate one of the main trends in big data
science – the quantified self (QS) [7][8]. QS community is
engaged in self tracking or group tracking of physiological,
behavioral, or environmental information. The community
shares insights, approaches, and algorithms. New sensors and
systems enable seamless collection of records and integration
in databases that can facilitate data mining and new insights.
Several companies created health kit toolsets, such as Google
Fit, Apple HealthBook, Samsung S.A.M.I, and Microsoft
Healthvault.
The smartwatch is worn more than any other wearable
sensor or device. Due to the constant contact with the skin it
may collect biological, environmental, and behavioral
information about user’s activity, and even identify the user.
E. Jovanov is with the Electrical and Computer Engineering Department,
University of Alabama in Huntsville, Huntsville, AL 35899, USA
(Phone: 256-824-5094; fax: 256-824-6803; e-mail: emil.jovanov@uah.edu).
II. METHODS
Preliminary analysis of the use and reliability of
physiological measurements on the smartwatch includes a
single male subject, age 54. In this paper we present analysis
of continuous monitoring using Basis smartwatch during 122
days, or 173,410 measurements. The system stores
measurement on the server at a rate of one measurement per
minute [2].
We define three classes of missed data events:
 Smartwatch charging is defined as interruption of
more than 60 minutes,
 Short breaks represented by continuous missing
records 10-60 minutes
 Missed data events are represented as continuous
missing records of up to 10 minutes.
Typical example of a display representing daily activity
can be seen in Fig. 1. A 3-hour charging event can be clearly
depicted starting from 10 am, followed by the physical
exercise around 1 pm. All measurements were downloaded in
CSV format using iPhone Basis application.
Zephyr Bioharness 3 sensor [4] was used for recordings
during sleep to compare measurements with recordings on
Basis Peak. Bioharness 3 is a chest belt that monitors body
position, activity, heart rate (RR intervals), breathing rate,
and temperature. Bioharness was used consecutively for 44
days during sleep only.
TABLE II.
DISTRIBUTION OF THE MISSED DATA SEQUENCES
ON THE SMARTWATCH
Sequence
length [min]
Total
duration
[min]
Number of
seq
Total
percent
1
1,598
1,598
0.92%
2
675
1,350
0.78%
3
296
888
0.51%
4
178
712
0.41%
SOMNOscreen+ is a clinical level polysomnographic
monitoring system used in sleep studies. The system records
data overnight and stores records on a flashcard or transmits
wirelessly to a workstation. SOMNOscreen+ system was
used for three nights as a standard for validation of
measurements.
5
92
460
0.27%
6
49
294
0.17%
7
39
273
0.16%
8
19
152
0.09%
9
13
117
0.07%
III. RESULTS
10
6
60
0.03%
2,965
5,904
3.40%
Fig. 1. Typical representation of daily activities on Basis portal.
We collected total of 173,410 records (120.42 days). We
used iPhone Basis application to download all records in the
experiment. Individual measurements missed because of the
lack of proper contact, or because the watch was not used, are
represented as empty fields in the downloaded CSV file.
Analysis of the data sequences is represented in Table I.
TABLE I.
Error
sequence
[min]
No missed
data
Total
No missed data
1-10 min missed
ANALYSIS OF THE DATA SEQUENCES
Number
of seq
Total
duration
[min]
Mean
duration
[min]
3,090
154,126
49.88
88.88%
1-10
2,965
5,904
1.99
3.40%
11-60
73
1,621
22.21
0.93%
> 60
53
11,759
221.87
6.78%
6,181
173,410
Short breaks
Total
percent
[%]
Charging
Fig. 2.
Distribution of data sequences
34
33
Overall, 11.12% of records were missing. Length of
correct sequences is important for some applications, such as
assessment of physiological rhythms. Distribution of missed
sequences of different length is provided in Table II. Missed
sequences of up to 10 minutes can be attributed to missed
data due to the poor contact or motion artifacts. It is clear that
3.4% measurements have missed data sequences of less than
10 minutes, and half of them are sequences of 1-2 lost
samples that can be corrected by interpolation between
known samples without significant influence on signal
processing and analysis. Relative contribution of data
sequences is represented in Fig. 2. Typical records and
monthly averages are presented in Fig 3 (skin temperature)
and Fig. 4 (heart rate). SOMNOscreen+ record had
significantly smaller sequences of lost data. Total ratio of lost
data for the whole night recording (7 hours and 52 minutes)
is 1.43%. Fig. 5 represents a segment of recordings from
SOMNOscreen+ and Basis Peak.
Zephyr Bioharness 3 has been used for monitoring during
sleep for 44 consecutive nights. Average length of the lost
data records was 417.28 seconds, or 1.74% with standard
deviation of 1.4 % and maximum of 5.87% of the record.
32
Skin Temp [deg C]
100.00%
31
30
Temp day#6
Temp 30 day mean
29
28
0
5
10
15
20
Time [h]
Fig. 3. An example of the daily change of skin temperature measured on
the Basis Peak smartwatch and a 30 day average for the same period.
140
Heart Rate day#6
Heart Rate 30 day mean
130
120
110
Heart Rate [bpm]
Total
100
90
80
70
60
50
40
0
5
10
15
20
Time [h]
Fig. 4.
An example of the daily change of heart rate measured on the Bais
Peak smartwatch and a 30 day average for the same period.
75
70
HR [bpm]
65
60
55
50
45
120
130
140
150
Time [min]
160
170
Fig. 5. Heart rate from the PPG sensor of SOMNOscreen+ (blue line,
Fs=4Hz) and Basis Peak (red line, Fs=1/60 Hz);
We analyzed the difference between average heart rate
measured by Basis Peak smartwatch and Zephyr
Bioharness 3 belt measured at 4 am for 5 minutes, for 30
days. The average difference was 0.89 bpm with standard
deviation of 1.03 bpm, and maximum difference was 3.13
bpm.
IV. DISCUSSION AND CONCLUSION
Introduction of continuous physiological monitoring on
smartwatches may revolutionize the field of mHealth and
longitudinal
monitoring.
Smartwatches
provide
unprecedented opportunity for collection of large data sets
that can be used for individual monitoring as guidance and
monitoring of specific patient populations. In this paper we
presented preliminary results of analysis of the use and
reliability of physiological records collected with the
smartwatch. We conclude that the current state of the
smartwatch technology provides sufficient performance for
longitudinal monitoring of health status and analysis of
health and wellness trends.
In this paper we analyzed the performance and use of the
Basis Peak, as the first smartwatch featuring continuous
monitoring of heart rate. Apple Watch is expected to provide
similar functionality, but it was not commercially available
at the time of writing.
Continuous monitoring and analysis of different
conditions require different features and sensor performance.
The most important features of the smartwatch based
physiological monitoring systems include:
 Wearability. Several factors influence wearability of
sensors: size, weight, skin irritation, and
tightness/pressure.
o Size and weight of the smartwatch significantly
influence the frequency of use of the smartwatch.
This is particularly critical for fragile population,
such as elderly. Early smartwatch products were
relatively heavy and bulky, although new
generations feature much more acceptable size and
weight of the smartwatch. This is certainly the
case with Basis Peak.
o Skin irritation. The main causes of skin irritation
are the belt and elevated part of the light sensor.
Belt width and softness are extremely important
for the prolonged use. Optical sensor features an
elevated, dome like, structure to allow better
contact with the skin. However, the sensor creates
more pressure at the contact with the skin.
Moreover, reliable monitoring requires tight
contact with the skin, much tighter than the regular
watch. We experienced limited irritation after 1224 hours of the use of Basis Peak, depending on
the tightness of the belt. Most manufacturers
advise users to make breaks and change watch
location to the opposite hand periodically. This
could be an important issue for the elderly
population, which is an important long term
monitoring application. A softer and more flexible
belt would improve user comfort. A variety of
alternative belts have been introduced at the time
of writing.
 Immunity to artifacts. Wrists are not very convenient
for physiological sensors, mostly because of the
motion artifacts, access to the skin (e.g. hairy skin), or
skin complexion (darker skin provides less reliable
measurements). However, embedding sensors in
objects that are regularly used, such as watch, is a
great idea. There are several ways to improve
precision of the watch sensors:
o Additional sensors can provide more robust
measurements at less noisy locations. Typical
example would be the use of Bioharness belt
around the chest. The belt would provide more
reliable measurements in most cases; however,
certain body positions, such as laying on the side
during sleep, may cause poor electrode contact
with the belt and lost measurements. Therefore,
robust monitoring might integrate several sensing
modalities. Measurements can be integrated on the
phone or the smartwatch.
 Sampling frequency. Required sampling frequency is
application dependent. Basis Peak saves by default
average heart rate every minute (1440 measurements
per day). However, new monitors allow “near realtime” monitoring of heart rate, that is of interest for
most exercise monitoring applications. It is important
to emphasize that exercise increases motion artifacts
and reduces reliability of heart rate monitoring;
however, at least one or two good measurements
every 3-5 seconds provide sufficient information
about the intensity of measurement. Basis Peak
automatically switches display to “exercise
monitoring” if walking or running is detected. By
default the watch still saves heart rate only every
minute, even in the case of exercise.
o Streaming of RR intervals is very important for
some applications, such as biofeedback
applications. Basis Peak allows streaming of heart
rate in near real-time. Accuracy of optical sensors
is currently not sufficient for the heart rate
variability analysis, but real-time monitoring of
heart rate could be useful in a number of
applications.
o Current performance of smartwatches is sufficient
for monitoring of some long term parameters, such
as resting heart rate, or early morning resting heart
rate.
 Personalization of monitoring and presentation of
information is very important for most users. The
system should provide option for configuration and
presentation of information.
 Seamless data integration is crucial for future
analysis,
personalization,
and
data
mining
applications. Repositories, such as Apple Health,
allow automatic storage of records from the
smartwatch, according to user’s preferences.
Currently, both Basis Peak and Apple Watch are
directly supported. After enabling access, selected
information is automatically stored and seamlessly
integrated into the personal record. Android Wear and
Google Fit also support seamless integration.
 User identification is currently limited to password
protected access to data and configuration. Several
groups experimented with the use of data stream for
user identification, mostly in laboratory settings.
Advisable features include:
 Connectivity. Range of Bluetooth, commonly used for
communication between smartwatch and a
smartphone, could be a limiting factor for some
applications. An alternative wireless interface, such as
WiFi, or alternative gateway, such as home router,
can significantly improve the performance for some
applications.
 Multisensory integration may include other
physiological sensors or integration with ambient
sensors, particularly with emerging Internet of Things
(IofT) systems.
 Ambient temperature/barometric pressure can
provide very important context of measurements,
such as detection of step climbing, or environmental
conditions. Some smartwatches already provide
additional sensors.
 Context awareness could be very important for data
analysis. For example, sudden change of heart rate
might be discarded if the motion artifact is detected
using accelerometer on the smartwatch; also, stable
increase of heart rate can be attributed to the increase
of ambient temperature if the user goes outside. Other
sources of information, such as calendar, can also
provide valuable insights about the context of
collected measurements.
Continuous monitoring and unstructured data collection
create significant challenges for long term monitoring
applications. Open research issues include:
 Signal processing. Typical problems include removal
of artifacts, filtering, and processing of incomplete
records caused by missed samples. Acceptable data
loss is application dependent.
 Sampling frequency or minimum set of signals is very
important for the overall performance of the
application. Lower sampling frequency can
significantly reduce power consumption and extend
battery life, allowing longer uninterrupted montirong
and improved wearability of the system.
 User input/notes and annotations. Many applications
depend on user input, specialized questionnaires, or
simple notes that can help with the annotation of the
records. Typical example is reporting “chest pain” or
“cold sweat” with time stamp that should be
seamlessly integrated with physiological record in the
database and used during analysis. Currently, this is
responsibility of individual applications.
With the mass production of smartwatches with
physiological sensors we expect a variety of models,
features, and configuration options with decreasing price and
extended battery life. Availability of technology and
widespread use will facilitate collection of massive
databases at the scale not possible even several years ago.
New sensors inspire new startups that dramatically change
diagnostic procedures and facilitate personalized and
preventive healthcare [9]. However, it is very important to
understand limitations of sensors and measured data sets.
This paper provides early insights into tradeoffs between
user’s convenience and reliability of measurements, and
their applicability for the long term monitoring. This paper
demonstrates that the current generation of smartwatches
with physiological sensors already provides sufficient
performance for some long term monitoring applications.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
Smartwatch Group, http://www.smartwatchgroup.com/
Basis, http://www.mybasis.com/
Apple Watch, http://www.apple.com/watch/
Zephyr Bioharness 3,
http://zephyranywhere.com/products/bioharness-3/
SOMNOmedics, http://somnomedics.eu/
R. Rawassizadeh, B. A. Price, M. Petre, “Wearables: Has the Age of
Smartwatches Finally Arrived?,” Communication of the ACM, 58(1),
January 2015, pp. 45-47. DOI 10.1145/2629633
Quantified Self, http://quantifiedself.com/
K. Wac, “QuantifiedSelf: empowering patients, are we getting there?,”
Careum Congress 2014 – 2nd ENOPE Conference, Basel,
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Ackerman, E., “The race to build a real-life tricorder,” IEEE
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