Novel Human Computer Interaction Principles

for Cardiac Feedback using Google Glass and Android Wear

Robert Richer1 , Tim Maiwald2 , Cristian Pasluosta1 , Bernhard Hensel2 and Bjoern M. Eskofier1
1
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science
2
Max-Schaldach-endowed Professorship for Biomedical Engineering
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
Email: robert.richer@fau.de

Abstract—This work presents a system for unobtrusive allow an ubiquitous and pervasive monitoring of vital signs
cardiac feedback in daily life. It addresses the whole pipeline in the patients’ daily environment without any restrictions in
from data acquisition over data processing to data visualization activity or modification in behavior, during physical activity
including wearable integration. ECG signals are recorded
with a novel ECG sensor supporting Bluetooth Low Energy, or at rest.
which is able to transmit raw ECG data as well as estimated Several research groups presented mobile applications for
heart rate. ECG signals are processed in real-time on a ECG recording. Oresko et al. [3], Yen et al. [4], and Makki
mobile device to automatically classify the user’s heart beats. et al. [5] proposed ECG analysis for detecting cardiac abnor-
A novel application for Android-based mobile devices was malities, whereas Valchinov et al. [6] and Richer et al. [7]
developed for data visualization. It offers several modes for
cardiac feedback, from measuring the current heart rate to used ECG analysis for sports and fitness assessment. Gradl
continuously monitoring the user’s heart status. It also allows et al. [8] presented an application for Android-based mobile
to store acquired data in an internal database as well as devices with an algorithm for real-time ECG monitoring and
in the Google Fit platform. Further, the application provides automated arrhythmia detection. These approaches mainly
extensions for wearables like Google Glass and smartwatches focused on the algorithm development, and did not provide
running on Android Wear. Hardware performance evaluation
was performed by comparing the course of heart rate between a solution for daily life conditions. There is definitely a
the novel ECG sensor and a commercial ECG sensor. The mean need for developing a user-friendly application that does not
absolute error between the two sensors was 4.83 bpm with a distract the user during its everyday activities. Nowadays, it
standard deviation of 4.46 bpm, and a Pearson correlation of is possible to display information about the user’s vital signs
0.922. A qualitative evaluation was performed for the Android as well as interacting between wearable and mobile phone
application with special emphasis on the daily usability and the
wearable integration. When the Google Glass was integrated, at the same time.
the subjects rated the application as 2.8/5 (0 = Bad, 5 = The purpose of this paper is to develop a solution that
Excellent), whereas when the application was integrated with incorporates novel human computer interaction principles
a smartwatch the rating increased to 4.2/5. with cardiac feedback that is usable in daily life situations.
Keywords-Body Sensor Networks; Electrocardiography; This work presents a novel low-power data acquisition
Wearable Computing; Android Application; Human Computer ECG hardware using Bluetooth Low Energy. The main
Interaction contribution however is the implementation of an application
called DailyHeart for Android-based mobile devices with
I. I NTRODUCTION special focus on human computer daily life usability and
Changes in the demographic structure are characterized wearable integration.
by lower reproduction rates, higher life expectancy, and a
II. M ETHODS
decrease in mortality [1], leading to an increase of chronic
diseases. Therefore, a need to continuously monitor the indi- A. Data Acquisition
vidual’s cardiac functions throughout the day for preventing The sensor module presented here measures the user’s
fatal disorders becomes increasingly important. ECG, which is sampled with a 12-bit resolution using the
Because the influence of the environment on the measure- integrated ADC of a TI MSP430-FR5969 microcontroller
ment of patients’ physiology (commonly manifested as the (Texas Instruments, Dallas, TX, USA), whose clock fre-
“white coat syndrome” [2]) is not negligible, mobile devices quency was dropped to 1 MHz due to energy saving aspects.
like smartphones or tables are an ideal platform, given their The sensor system was configured to measure the user’s
integration into daily life. ECG, but it can moreover be configured for measuring
Wearable healthcare technology is a promising way to physiological signals like EMG or respiratory rate [9].
improving the quality of life for chronic disease patients and ECG data was transmitted to the mobile device by a
elderly people as well as healthy individuals. These solutions TI CC2541 system-on-chip (SoC), which is an integrated
 QRS Detection The raw ECG signal was processed with
a pipeline of digital filters proposed by Pan & Tompkins
RA [13]. It consists of a cascaded Bandpass filter, a five-point
-
DRL derivative filter, a squaring operation, and a moving window
•
integrator. QRS complexes were then isolated from the
Lead II algorithm output.
Template Formation In order to allow an automatic
classification of heart beats, a feature computation using two
LL QRS complex templates which were automatically found in
+
the ECG signal and adapted over time was implemented [8].
Feature Extraction The features for the heart beat classi-
fication were extracted from 400 ms windows centered on
Figure 1. Lead positioning of the BLE-ECG Stamp. Schematic drawing every isolated R peak [8]: The difference in absolute area
of the sensor placement and the lead positioning; RA: Right Arm, LL: Left
Leg, DRL: Driven Right Leg
and the maximal Pearson correlation between the templates
and the current beat as well as the the width of the QRS
complex, and the R-R interval between the last two QRS
solution for Bluetooth Low Energy (also referred as BLE, complexes.
or Bluetooth LE) applications, combining a high power Beat Classification The isolated heart beats were classi-
Bluetooth antenna with an improved 8051 microcontroller. fied according to the decision tree proposed by Gradl et al.
The Bluetooth Low Energy Application of the sensor [8], distinguishing between abnormalities in waveform and
system named BLE-ECG Stamp provides two different Blue- abnormalities in rhythm/pace.
tooth Services for transferring data to a mobile device: an
EcgService and a HeartrateService. The EcgService streams C. Data Visualization
the sampled ECG signal directly. In order to reduce the The data acquired by the BLE-ECG Stamp was transmit-
power consumption of the CC2541 SoC, the raw values are ted to the proposed application for Android-based mobile
wrapped into packages with a size of 10 samples before devices called DailyHeart. Android (Google Inc., Mountain
transmission. By performing a heart rate calculation on the View, CA, USA) was used because of its open source char-
sensor side and only transmitting the current heart rate acteristics, the portability of code since the programming
for the HeartrateService, the data rate (and thus the power language is Java, and because it is the most widely spread
consumption) is significantly reduced. The HeartrateService mobile operating system up today [14] with a market share
implements the Heart Rate Profile (HRP) as proposed by of 83.1 %. Furthermore, the BLE APIs of Android provide
the Bluetooth SIG, and hence provides compatibility for support for Bluetooth LE since software version 4.3 (API
other applications supporting this Profile [10]. Here, the
EcgService was used since the entire ECG signal was needed Lowpass
Highpass
for subsequent heart beat classification. Band
R-Peak Detector
The sensor recorded a 1-channel-ECG at a sampling Differentiation
Squaring
frequency of 256 Hz with 3 electrodes that were placed ac- Wnd-Int [150 ms]
Yes Peak
cording to Lead II of Einthoven’s triangle [11]. The negative Int

Mean [150 ms]

electrode was placed on the sternum and the positive was Above?

placed on the left costal arch (Figure 1). The third electrode Thr R

was placed on the right breast and used for the driven right
leg circuit, which is often added to physiological signal
amplifiers in order to eliminate electromagnetic interference
[12]. Template
plate
pla te 1
Template 2 Waveform Window

B. Data Processing
MaxCorr ArDiff R-R QRS
QRSwidth
The algorithm used for data processing was proposed by
Gradl et al. [8]. It is able to perform a real-time detection of Thresholds
QRS complexes in an ECG signal, followed by an automated
Abnormal Classes Normal Classes
classification of normal and abnormal heart beats. The
pipeline is visualized in Figure 2 and consists of four stages: Figure 2. Algorithm pipeline. Overview of the pipeline that is used for
QRS Detection, Template Formation, Feature Extraction, QRS detection and heart beat classification (modified with permission from
and Beat Classification. [8])
Figure 3. User interface of DailyHeart. Different screens of the application. From left to right: Main menu for selecting the recording mode; Activity
to measure the user’s current heart rate; Activity consisting of two tabs to monitor the user’s ECG.

level 18). The DailyHeart application was implemented ECG. In addition, a DailySimulate mode was implemented
using the Android SDK 5.0 (API level 21), which was for re-playing pre-recorded data. All activities plot the live
released with Android 5.0 Lollipop in November of 2014. ECG signal and display the current heart rate to the user. The
It introduced a completely new design language called DailyMonitor mode provides also the average heart rate, the
material design that is characterized by Google as bold, course of heart rate since the beginning of recording, as well
colorful, and responsive, providing a new and intuitive user as further features obtained from the ECG processing. At the
experience [15]. end of the measurement, a summary of the acquired data is
shown as well as buttons to restart the measurement or save
The application consists of several components: a BLE the results to the internal database and/or Google Fit. The
Service for data delivery, a User Interface for informing history screen accesses the database and lists all entries.
the user and receiving input, a Data Storage unit for saving
the recording results, and a Wearable Extension for Google Data Storage The Android operating system is shipped
Glass and Android Wear-based smartwatches. with a SQLite database that allows to store results into the
database for retrieving it again at a later time. The Dai-
BLE Service This component is implemented as an An- lyHeart application uses this feature for local data storage
droid background service. It provides ECG data for the and also provides support for Google Fit, a health-tracking
processing unit and delivers the results to the user interface. platform where users can access their fitness and health data
The data can either be obtained via live mode or simulation acquired and uploaded by different devices and applications.
mode. In live mode, the service establishes a connection
to the BLE-ECG Stamp and receives the acquired data Wearable Extension Android Wear is a modified version
via Bluetooth LE, whereas in simulation mode, real-time of the Android operating system for smartwatches and
sampling is simulated by reading data from the external other wearables by integrating smartphone features with a
storage. It is possible to simulate pre-recorded data from watch form factor. Information on smartwatches running
DailyHeart, or data from the MIT-BIH Arrhythmia databases on Android Wear can be displayed either by extending
[16]. Handlers are used to pass data to the service’s signal regular Android system notifications for wearables, that are
processing unit and the user interface. By using a service, then synchronized between smartphone and wearable, or
data delivery and processing runs independently from any by creating custom applications for Android Wear that can
foreground process, which allows the user to switch between communicate with the smartphone application. The extended
applications without terminating the connection. notifications allow the user to fully control the application
after it has been launched. They furthermore continuously
User Interface The application was designed following update the screen with new information. In this work, a
the material design guidelines in order to provide a clear, custom application for smartwatches was implemented in
intuitive and appealing user interface for cardiac feedback. order to enable voice capabilities. This allows the user to
It features modes that were designed for different daily life start the application by saying the phrase “Okay Google,
situations (Figure 3 shows screen examples): a CurrentHR start DailyHeart!”, or to directly measure the current heart
mode to perform a snapshot heart rate measurement, a rate by saying “Okay Google, what’s my heart rate?” or
AnalyzeECG mode to analyze the ECG over a period of time, “Okay Google, what’s my bpm?”.
and a DailyMonitor mode to continuously monitor the user’s
 Table I
Google Glass is a wearable device with an optical head- R ESULTS OF THE S ENSOR E VALUATION . MAE = M EAN A BSOLUTE
mounted display (OHMD), a camera, and several sensors E RROR , SD = S TANDARD D EVIATION , PC = P EARSON C ORRELATION
like IMU, ambient light sensor, proximity sensor, and a GPS
MAE SD PC
receiver [17]. It also includes chips for Wi-Fi and Bluetooth (bpm) (bpm)
communication. Google Glass can be controlled by voice Subject 1 2.43 2.91 0.957
commands or by swipe-and-click gestures performed on a Subject 2 6.54 6.58 0.920
touchpad located at the right of the frame. By using the same Subject 3 3.85 3.45 0.912
Subject 4 5.64 4.29 0.913
extended notifications as for smartwatches, the DailyHeart Subject 5 5.68 5.05 0.906
application also provides support for Google Glass. The Mean 4.83 4.46 0.922
OHMD offers cardiac feedback by overlaying information
directly into the user’s field of view. 50
40

diff(BLE-ECG Stamp, Shimmer)

III. E VALUATION 30
20
A. Sensor Evaluation +1.96 SD: +14
10
The BLE-ECG Stamp was evaluated to assure that the 0 Mean: 0.18 [p=0.093]
provided signal was accurate and reliable enough for sub- −10
−1.96 SD: -13
sequent data processing. Therefore, a Shimmer (Shimmer −20
−30
Inc., Dublin, Ireland) ECG sensor was used as gold standard
−40
hardware. This sensor system has already been used in
−50
earlier work [7], [8] providing a validated ground truth. 0 20 40 60 80 100 120 140 160
mean(BLE-ECG Stamp, Shimmer)
A study involving a physical exercise on a stationary
bicycle was conducted with five healthy male subjects (age Figure 4. Bland-Altman plot for the sensor evaluation [19].
22.2 ± 1.3 years (mean ± SD)). Their suitability was assured
by means of the Physical Activity Readiness Questionnaire
(PAR-Q) [18]. All subjects passed the PAR-Q and signed for a duration of one hour (including a short run of one
a written consent to perform the following protocol: two minute). To evaluate the functionality of the application,
minutes of resting, two minutes of low-intensity cycling, each subject answered a questionnaire1 .
one minute of heavy-intensity cycling, two minutes of low-
IV. R ESULTS
intensity cycling, two minutes of resting. The study was ap-
proved by the ethics committee of the Friedrich-Alexander- A. Sensor Evaluation
Universitt of Erlangen-Nrnberg. The results of the sensor evaluation for each subject
Both sensor systems were worn simultaneously with the are listed in Table I. The Bland-Altman plot in Figure 4
same lead configuration. The ECG data was transmitted via visualizes the relationship between both sensor systems [19].
Bluetooth and saved as CSV-formatted files, which were im- The evaluation achieved a mean cross correlation coefficient
ported into the DailyHeart application using the simulation of 0.922 with a mean absolute error of 4.83 bpm ± 4.46 bpm
mode. The course of heart rate during the measurement was (MAE ± SD).
exported to MATLAB R
(The Mathworks, Inc., Natick, MA, B. App Evaluation
USA) for comparing the results.
The results of the app evaluation are visualized in Figure
B. App Evaluation 5. Higher ratings as well as lower standard deviations
Five male and five female subjects (age 21.0 ± 1.7 years were obtained for Android Wear compared to Google Glass
(mean ± SD)) participated in a study for evaluating the throughout the entire evaluation. In addition, 30 % of the
DailyHeart application with emphasis on daily life usability participants were afraid that Google might misuse the health
and wearable integration. All subjects were healthy and data stored in Google Fit. Every participant would use the
signed a written consent approved by the ethics committee DailyHeart application in combination with a smartwatch
of the Friedrich-Alexander-Universitt of Erlangen-Nrnberg. for cardiac monitoring, but no one prefered the combination
The participants were asked to wear the BLE-ECG Stamp with Glass.
with the default lead configuration and a Nexus 5 mobile V. D ISCUSSION
device (LG Electronics, Seoul, South Korea) running on
A. Data Acquisition
Android 5.0.1 “Lollipop”. They furthermore wore a LG G
Watch (LG Electronics, Seoul, South Korea) running on In this work, the hardware components and the wireless
Android Wear, and a Google Glass (Google Inc., Mountain communication were chosen to keep power consumption as
View, CA, USA). The participants were then asked to test the 1 The questionnaire can be accessed here: http://www5.cs.fau.de/
features of the application and perform daily life activities fileadmin/groups/ds/dailyheart.pdf
 5 C. Data Visualization

Average Rating
4
The application layout of DailyHeart was reduced to only
3
the essentials in order to provide clarity and to prevent
2 unnecessary loss of time by getting used to the handling,
1 which was the main purpose of introducing the material
0
design. Future work could use more detailed queries that
are compiled against the SQLite database implemented for
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that provides information about about location, speed, or

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energy expenditure. It would also be feasible to improve the

Figure 5. Results of DailyHeart evaluation. The bar chart presents the
answers to the questionnaire that was filled out by the participants of the
clinical aspect of the application by incorporating heart rate
study, with 1 being the most negative answer and 5 the most positive answer. variability analysis.
One key intention of this work was the incorporation
of novel human computer interaction concepts like Google
low as possible, for providing a sensor system that enables Glass and smartwatches with an application for cardiac feed-
a long-term monitoring without having to charge the device back. Adding wearable-specific functionality to notifications
in between. and enabling voice capability are an important part of the
The study design for validating the correct functionality of wearable experience. It provides better user experience and
the BLE-ECG Stamp was chosen to challenge the recording allows the user to interact with the application quickly and
system by conducting a protocol that includes strong motion hands-free. The original intention of Google Glass was to
and muscle artifacts. The results show an error of 4.83 bpm present a standalone wearable system since it has enough
± 4.46 bpm (MAE ± SD) compared to the Shimmer sensor. computational resources and sensor technology to work
The high Pearson correlation of 0.922 proved that the sensor independently from a handheld device. However, excessive
provides a solid wearable solution for continuous cardiac usage of Google Glass rapidly leads to overheating and
monitoring. significant battery loss. Therefore, it is not recommended
However, the evaluation reveals a key problem for wear- to perform complex calculations on Glass, which makes
able ECG sensors: Motion and muscle artifacts result in a standalone version of DailyHeart for Google Glass not
a distorted ECG signal, which can be especially observed feasible.
during heavy-intensity cycling periods. In comparison to the The results of the app evaluation show that the mean
BLE-ECG Stamp, the Shimmer uses shorter leads, which is usability rating for smartwatches (4.2) is higher than the
a reason for the lower noise level of the Shimmer signal. Google Glass rating (2.8). The OHMD of Google Glass is
Therefore, shorter leads (and perhaps fixed to the body) placed in the least comfortable area of the human field of
should be used for the BLE-ECG Stamp as well, so that vision [20]. Due to that, staring at the display for a longer
the sensor is more tightened to the body and hence more period of time is counterintuitive and causes discomfort in
robust in the face of artifacts. Furthermore, a version with the eye muscles [20]. In contrast, wristwatches have been
a digital ECG amplifier is part of future work, which may worn for a long time by a major part of the population and
result in an improved signal quality. therefore are an integral part in daily life. Since Glass is an
entirely new technology, smartwatches receive higher public
acceptance and need lower habituation periods. Privacy
B. Data Processing
concerns against Google Glass have been raised by various
The implemented algorithm provides high QRS detection sources due to the possibility of videotaping people in public
rates, high sensitivity and a low number of false negatives, without their awareness and permission [21]. Concerns do
which is an important basis for adequate cardiac monitoring not only exist due to the integrated camera of Google Glass,
[8]. The algorithm ran in real-time on the testing mobile but also about the privacy policy of Google in general. 30 %
device but created high computational load which could be of all study participants are afraid that their health data
observed by a slight warming of the mobile phone. Given the stored in Google Fit could be misused by third parties. The
flexibility of the app design, the data processing unit could intention of the Google Fit integration into DailyHeart is to
easily be replaced, as long as it implements the interface of exclusively upload the user’s heart rate when the user desires
the application. so – it is not intended for uploading all collected cardiac data
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