E-Health Telecommunication Systems and Networks, 2013, 2, 49-57
http://dx.doi.org/10.4236/etsn.2013.23007 Published Online September 2013 (http://www.scirp.org/journal/etsn)
BluetoothTM Enabled Acceleration Tracking (BEAT)
mHealth System: Validation and Proof of Concept for
Real-Time Monitoring of Physical Activity
Aleksey Shaporev1*, Mathew Gregoski2, Vladimir Reukov1, Teresa Kelechi2,
David Morgan Kwartowitz1, Frank Treiber2, Alexey Vertegel1
1Bioengineering Department, Clemson University, Clemson, USA
2College of Nursing, Medical University of South Carolina, Charleston, USA
Received June 20, 2013; revised July 15, 2013; accepted July 31, 2013
Copyright © 2013 Aleksey Shaporev et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Physical activity is critical to improve the condition of patients with chronic leg and foot ulcers, especially those who
are obese and experienced multiple co-morbid conditions. Unfortunately, these individuals are unable to engage in
guideline based physical activity (PA) programs. A prototype of BluetoothTM enabled acceleration tracking (BEAT)
mHealth system was developed and manufactured for remote monitoring and stimulation of adherence to PA in decon-
ditioned patients. The system consists of a miniature accelerometer-based sensor, smartphone application, and a net-
work service. Validation testing showed high reliability and reproducibility of the BEAT sensors. Pilot study with hu-
man subjects demonstrated high accuracy of the BEAT system in recognition of different exercises and calculating
overall outcomes of PA. Taken together, these results indicate that BEAT system could become a valuable tool for
real-time monitoring of PA in deconditioned patients.
Keywords: mHealth; Accelerometer; Sensor; Physical Activity; Monitoring
1. Introduction
Annually leg ulcers cost the US healthcare system in
excess of $20 billion, additionally leading to an estimated
2 million lost workdays per year; one ulcer can cost more
than $40,000 for medical care, possibly exceeding $1000
in patient out-of-pocket expenses [1,2].
Patients with chronic leg and foot ulcers, especially
those who are obese and experienced multiple co-morbid
conditions, are often physically inactive causing them to
develop deconditioned legs that are weak and have re-
duced ranges of motion, especially of the ankle. Many
leg and foot ulcer patients do not walk; they “shuffle”
and generally move no more than a few steps at a time,
and at distances of less than 5 feet. The majority use
walkers, scooters, or wheelchairs leading to social isola-
tion and lack motivation to engage in regular physical
activity on their own. Often, these same patients have
previously tried physical therapy but continued to suffer
from deconditioned legs and ulcers due to poor compli-
ance.
Physical activity is critical to improve the condition of
their legs and promote wound healing. Unfortunately,
these individuals are unable to engage in guideline based
physical activity programs (i.e., supine cycle ergometry,
walking, treadmill exercises offered through community
exercise groups, etc.) most commonly recommended for
ulcer patients. They are “left out” of typical physical ac-
tivity programs due to inability to participate; most pro-
grams have problems with accommodation of patients
with leg and foot ulcers. Furthermore, because de-condi-
tioned individuals with leg and foot ulcers are unable to
engage in these types of activities, personalized programs
need to be developed.
In a recent physical therapy intervention study at
Medical University of South Carolina (MUSC), results
demonstrated that the lower legs of patients with ulcers
have significantly reduced range of motion and strength.
A statistically significant improvement in ankle range of
motion, specifically dorsiflexion and leg strength (p =
0.03) was found in patients with a history of leg ulcers
who participated in a videoconferencing physical activity
coaching intervention delivered over the internet [3].
*Corresponding author.
C
opyright © 2013 SciRes. ETSN

A. SHAPOREV ET AL. 50
While the goal of the study was to test the feasibility of
using the internet for the virtual face-to-face intervention,
the “signals” of significance in function suggested that
even with small doses of physical activity (one week),
patients who were motivated and engaged (there was a
statistically significant increase in self-efficacy during
the study) were more likely to exercise. In order to prop-
erly examine a behavioral intervention to increase physi-
cal activity in a group of regular patient wound care an
establishing outcome to objectively monitor movement,
particularly small toe and forefoot movements is neces-
sary; integration using a technology-assisted Mobile
Health (mHealth) can help to monitor physical activity
adherence and enhance healing.
In studies that measure adherence to physical activity,
accelerometers are widely used. Researchers have dem-
onstrated the reliability and validity as well as the utility
of accelerometers to increase physical activity in patient
programs using step count as a surrogate measure for
energy expenditure (EE) among ambulatory patient po-
pulations [4,5]. Moreover, accelerometers were used for
motion and gesture recognition [6-9] and even were
proven to be able to record fairly small movements (such
as tremor [10]).
Nevertheless, developed PA monitors are typically de-
signed to be stand-alone devices (with thus reduced mo-
tivation and control functionality), and only in rear cases
researchers develop comprehensive feedback-oriented
systems for remote patient monitoring.
A breakthrough in patients monitoring in terms of en-
hanced motivation and control functionality with rea-
sonable solution cost can be achieved using smartphones
[11] (widely spreading over cellular network users [12]).
Such an mHealth solution can be useful and efficient for
insufficiently motivated deconditioned patients with lim-
ited mobility to achieve improvement of PA adherence
and enhance healing. Considering specific aspects of this
population it has to comply with the following require-
ments:
 High sensitivity—sensor must be able to record
small accelerations corresponding to minute foot mo-
tions;
 Small size—sensor must be small enough to be
nested at foot or shoes;
 Recognition of foot motions—system must be able
to recognize prescribed exercise patterns based on
forefoot movements proven to be efficient in wound
healing;
 Power efficiency—sensor battery lifetime must be
reasonably long, so that under normal usage condi-
tions patient could use sensor for at least a week on a
single battery set;
 Interactivity—device must be able to analyze pa-
tient’s physical activity patterns, assess their compli-
ance, and encourage them to exercise more in the case
of poor compliance;
 Telecommunication capabilities—device must be
able to transmit physical activity data to their care
provider, and retranslate clinician’s recommendations
to the patient.
mHealth solutions were shown to be efficient for re-
mote monitoring of patients and to provide enhanced
feedback and motivation options [13]; however, person-
alized mHealth systems that fulfill all of the above re-
quirements are not readily available. In response to the
lack of monitoring devices specifically targeting lower
leg activity for minimally active population, we report
here a comprehensive mHealth system consisting of a
wireless BluetoothTM-enabled accelerometer tracking
device (BEAT) with an application for Android-based
smartphones and integrated web-services (Figure 1). Use
of smartphone allows us to simplify BEAT sensor (and
thus significantly reduce solution cost), miniaturize it,
increase battery lifetime and enhance system functions
since BEAT sensor mainly performs accelerometer data
acquisition and their transmission to the smartphone
while smartphone carries the load of data analysis, net-
working and communication between the patient and
clinician.
The purpose of the current study was to design and test
a prototype mHealth BEAT system and provide evidence
of its reliability and validity. Furthermore, smartphone-
based software algorithms for recognition of specific
exercises were developed. Finally, the capabilities for
participant motivation and feedback using a smartphone
program are also described.
2. Materials and Methods
The BEAT (Bluetooth-enabled accelerometer tracking
device) sensor consists of a 3-axis accelerometer
(ADXL335, Analog devices Inc., MA, USA), micro-
processor (PIC18F14K22, Microchip Technology Inc.,
Figure 1. Schematic of developed mHe a lth BEAT system.

A. SHAPOREV ET AL. 51
AZ, USA) and a BluetoothTM module (RN-42, Roving
Networks, CA, USA). The accelerometer has dynamic
range of −3 g - 3 g and resolution of 0.006 g, and those
parameters were found sufficient for foot minute move-
ment detection application. A custom printed circuit
board (PCB) was produced using Surface Mount tech-
nology to reduce physical footprint.
In the BEAT sensor (see Figure 2), acceleration data
produced by 3-axis accelerometer are acquired by the
microprocessor using on-board 10-bit analog-to-digital
converter with a desired sampling rate which can be ad-
justed accordingly to experiment conditions. As a rule,
sampling frequency should be 2 times higher than target
frequency of measured events (5 - 10 times higher for
better detalization), so in this study for exercises (foot
movements) with target frequency of 0.5 - 1 Hz we
picked sampling frequency of 10 Hz. The microprocessor
also performs primary data processing and sends the
processed data to the BluetoothTM module, which in its
turn transmits the data to the receiving Bluetooth-capable
device (the smartphone). Power supply was provided by
a CR2032 battery (Panasonic Industrial Company Applied
(a)
(b)
Figure 2. Scheme of the BEAT sensor (a) and a photograph
of the manufactured device (b).
Technologies Group, NJ, USA) with nominal voltage of
3 V and nominal capacity of 225 mAh.
Custom firmware was developed for the BEAT device
to perform data collection and initial data processing.
The developed firmware provides variable processor
operation frequency, data acquisition rate, data buffering
parameters and allows Bluetooth module operation at
data transfer rates up to 57.6 KB/s.
In order to minimize volumes of data transmitted from
BEAT sensor to the smartphone, a protocol for acceler-
ometer data transmission over BluetoothTM was devel-
oped. Assuming 30 bits of acceleration data per single
data point (3 axes, 10 bits per each), it uses 6 bytes of
data to transmit acceleration data, a checksum and a
timestamp (to provide availability to synchronize the data
in the case of partial data loss) to a smartphone. This
protocol provides significant benefits compared to tran-
smission of uncompressed data (~20 - 30 bytes per data-
point) and thus decreases amount of energy used for data
transmission.
Motorola Droid2 smartphone (Motorola Mobility Inc.,
IL, USA) operated on Android OS v.2.2 was used in the
study. A special BEAT application was developed for
this smartphone (see Figure 3) using Android SDK
(Google, Inc., CA, USA). The application performs the
following main functions:
 Data acquisition from BEAT sensor via BluetoothTM
(using developed data transmission protocol);
Figure 3. BEAT application for Android OS-based smart-
phones.

A. SHAPOREV ET AL. 52
 Analysis of the data acquired from the BEAT sensor,
recognition of specific exercise patterns and determi-
nation of major quantitative parameters (exercise in-
tensity, duration, frequency, angles etc.);
 Communication with the user (display exercise pro-
gress, output of exercise results, statistical data, trans-
mission of incoming messages from the server and/or
from clinician);
 Compilation of exercise summary and uploading
these data to a server using HTTP protocol. Data buf-
fering and storage was implemented in order to pre-
vent data loss in case of absence of Internet connec-
tion.
The exercise recognition mechanism implemented in
the app is based on determination of acceleration extrema,
and comparison of “target” acceleration with signal thres-
hold value, as well as “non-target” with noise threshold
values. Sequence of “target” acceleration events was set
based on exercise geometry. Additional correction for
sensor displacement based on principal component ana-
lysis was added to increase signal and suppress noise
caused by minor sensor displacement, and thus reduce
the number of erroneous recognitions. Despite the rela-
tive simplicity of this analysis method, it was found to
work well for recognition of two exercises used in the
study.
The application is in an active state only when exercise
session is in progress (~5 - 10 sessions per day), it does
not consume much energy. No significant influence of
the app work on smartphone battery was noted during
test.
A server for physical activity monitoring results stor-
age and processing was deployed at Clemson University.
It is based on Apache HTTP server (The Apache Soft-
ware Foundation, USA), supports relational database
(MySQL 5, Oracle, CA, USA) and runs PHP interpreter
(PHP 5, The PHP group) that was used to perform data
analysis and output results to clinicians. Developed
server provides access for authorized users (clinicians) to
patients’ results, and can present these results in a variety
of ways, including personal patient’s progress, total/av-
eraged exercise frequencies and durations and patient’s
compliance with prescribed training plan.
BEAT sensor reliability was tested with two main tests.
First one was continuous (60 seconds) data acquisition
from motionless BEAT sensor. Data acquisition was
performed using MATLAB (The MathWorks, Inc., MA,
USA), 3 data channels (x-, y-, z-components of accel-
eration) were recorded and signal standard deviations
were calculated. This test was used to estimate level of
noises (e.g., RF noises) and pick up proper data acquisi-
tion parameters.
Second test was used to evaluate inter-device repro-
ducibility. A rotary shaker (MaxQ Mini 4000, Barnstead
International, IA, USA) with 4 BEAT sensors attached
was used. BEAT sensors were mounted in similar or dif-
ferent (random) orientations on the platform and placed
onto a shaker plate (shaking frequency was varied from
45 to 75 rpm). MATLAB (The MathWorks, Inc., MA,
USA) was used to acquire data from all participating
sensors (via Bluetooth) simultaneously. Coefficients of
variations (CV) were calculated for all 4 devices. In case
of similar sensor orientation raw x-, y-, z-acceleration
components were used for CV calculations, while in case
of random BEAT sensors orientation accelerations ob-
tained from different sensors were transformed into a
uniform coordinate system using principal component
analysis method thus eliminating difference in orienta-
tions of the devices, and thus transformed acceleration
components were used for CV calculations.
General purpose of BEAT is recognition of rotational
motions of patients’ foot since these exercises can be
done even by overweight patients with limited mobility
and because these motions can be effective in restoring
normal blood flow in a foot. Two exercises recommend-
ed to ulcer leg patients were chosen as the model ones for
this pilot study. Exercise 1 consists of a horizontal rota-
tion of foot (floor wiping) with target frequency of ~0.5
Hz and amplitude of ~80˚. Exercise 2 consists of a verti-
cal rotation of foot in x-z plane (where z is “up” and x is
“front” for the patient) of accelerometer with target fre-
quency of ~0.5 Hz and amplitude of ~45˚. Patient is
supposed to sit during both exercises and his heal should
be pressed to the floor. These exercises were chosen be-
cause they include two key foot movements (longitudal
and lateral rotations) and are recommended for leg ulcer
patients.
A pilot validation of the BEAT system was performed
with two healthy volunteers (MUSC IRB protocol
#Pro00013314). BEAT sensor was attached to a slipper
worn by a healthy volunteer (Figure 4). The volunteer
was asked to perform exercise 1 or 2 with BEAT sensor
on and simultaneous video recording to determine accu-
racy of motion recognition by the BEAT application.
Initially volunteers were supposed to do 60 one-way mo-
tions to simulate 1 minute training session (with target
frequency of 1 motion/second) for both exercises, but
during the study it was found that not all volunteers were
able to do exercise 1 for 60 times, and target number of
one-way motions for exercise 1 was cut to 50 to maintain
same amount of exercise motions for all volunteers. To
check for potential false positives, in some exercise ses-
sions BEAT software was set to recognize the incorrect
exercise type (e.g., set to recognize exercise 2 while the
subject was doing exercise 1). Thus, four different exer-
cise sessions were used (see Table 1). After the end of
every exercise session results were automatically uploaded
by the BEAT app to a server and BEAT app motion rec-

A. SHAPOREV ET AL. 53
improvements of battery life. Increased buffer size would
however lead to higher device latency, or lag between
data acquisition and transmission. Variable buffer size
implemented here allows BEAT to be used in different
monitoring modes, depending on application. For exam-
ple, choosing the buffer size of 120 B results in the la-
tency of 2 sec and battery life of approximately 24 h of
continuous use, appropriate for real time recording of
short-term user-initiated sessions (assuming the session
length of 15 min daily, the device would run without the
need to change battery for up to 96 days). If 24/7 moni-
toring in passive mode is desired, increasing the buffer
size to 10 kB would allow continuous monitoring for up
to 10 days with latency time of 133 sec.
ognition results were compared to those obtained from
the video records. Each exercise set was performed three
times by each volunteer. Results of the recognition by
BEAT software were then recorded for each volunteer.
Additional test was performed to determine if BEAT
app will provide any false positives in resting volunteers.
Volunteers were asked to sit still for 30 seconds, with
BEAT sensor on and BEAT app set to recognize either
exercise 1 or 2. Data obtained from BEAT app was tested
for any false positives. Three replicates of this test were
done by each volunteer.
3. Results and Discussion
The BEAT system was designed to be a convenient tool
for remote monitoring of patients. Thus, all major parts
of the system were designed and optimized to fulfill the
requirements discussed above. The BEAT sensor was
designed to have the following features: small size, high
sensitivity and power efficiency.
Exercises for the target population generally include
various feet rotations. Typically, small accelerations
(both lateral and tangential) are required to perform these
types of motions, so reliable detection of corresponding
accelerations may be limited by poor accelerometer sen-
sitivity. Thus, we performed evaluation of sensitivity and
reliability of the BEAT sensor. Data acquisition accuracy
Small sensor size was achieved by using small-size
components. For instance, the accelerometer dimensions
are 4 mm × 4 mm × 1.45 mm, and the microprocessor
dimensions are 7.8 mm × 7.2 mm × 2 mm. This allowed
us to design the PCB layout (and final device) with a size
(from 20 mm × 28 mm) comparable to that of a quarter
coin (see Figure 2(b)).
Power efficiency was one of our priorities during de-
sign of the device. Thus, power-efficient parts were cho-
sen to minimize on-board power consumption (acceler-
ometer with approx. 0.3 mA operating current and mi-
croprocessor with 0.6 - 2 mA operating current). The
most power-consuming part of the sensor is the Blue-
toothTM module, which consumes up to 25 mA in work-
ing mode. To reduce power consumption by the Blue-
toothTM module, data storage/buffering by the micro-
processor was implemented, to ensure that BluetoothTM
module runs only when a package of data is acquired and
ready to be sent, instead of power-inefficient continuous
datum-by-datum transmission. For small data packages
less than 30 kB (corresponding to 5000 of recorded
events, 500 sec @ 10 Hz sampling rate), energy being
consumed by the BluetoothTM module to transmit a
package of data is almost independent on the package
size; thus increase in the buffer size lead to considerable
Figure 4. BEAT sensor attached to a slipper worn by a
healthy volunteer.
Table 1. List of exercise sets performed by volunteers in BEAT validity study.
Exercise set Exercise done by
the volunteer
Exercise set up to be recognized
by BEAT app
Number of one-way
motions
Purpose of
experiment
1 Ex. 1 Ex. 1 50 Exercise 1 true positives
2 Ex. 1 Ex. 2 50 Exercise 1 false positives
3 Ex. 2 Ex. 1 60 Exercise 2 false positives
4 Ex. 2 Ex. 2 60 Exercise 2 true positives

A. SHAPOREV ET AL. 54
is determined by accelerometer parameters and the de-
vice design (including device firmware). According to
the accelerometer specifications, its accuracy is 0.01 g.
10-bit analog-to-digital (ADC) conversion performed by
the microprocessor was adjusted to have ADC accuracy
same as that of the accelerometer (3 mV, equal to 0.01 g).
Actual accelerometer data can be (and typically are) af-
fected by different noises (most of them are RF-noise
picked up by the circuit and voltage deviations due to
irregular power consumption by the BluetoothTM module
during data transmission). To eliminate this factor we
implemented multiple acceleration acquisitions for every
time-point with averaging thus obtained values. Increas-
ing the number of replicates improves the accuracy but
reduces the battery life. To determine optimal number of
replicates, we performed experiments with motionless
sensors. We found that when the number of replicates
was 20 or more, signal deviations for x, y and z compo-
nents of acceleration did not exceed 0.01 g thus corre-
sponding to the intrinsic accuracy of the accelerometer.
Further increase of the number of replicates would only
lead to more power consumption, and therefore 20 was
chosen as the optimal number of replicates and fixed in
the firmware for further experiments.
A rotary shaker plate test with 4 devices was per-
formed to evaluate reliability of the BEAT sensor. At all
frequencies used (45, 60 and 75 rpm) sensors in similar
orientations were found to give almost identical data (see
Figure 5), and coefficient of variation calculated for all 4
devices (75 rpm) was found to be 0.8%. It should be
noted, that observed error can originate from both sensor
acceleration acquisition error and differences in device
orientation. Therefore, a second test was carried out with
devices mounted in different orientations. Acceleration
Figure 5. Acceleration data obtained by two sensors during
shaker plate test (x axes of accelerometers, 45 Hz rotational
speed).
values were processed to be comparable (moved into
similar coordinate system) and coefficient of variation in
this case was 1.1%. Therefore, reliability testing demon-
strated that BEAT devices generate consistent accelera-
tion data with sufficient (0.01 g) accuracy. These ex-
periments also showed that consistent acceleration data
can be obtained from BEAT sensors independently of
their initial orientation. This finding is important because
initial orientation of the devices during the exercise is
expected to be variable for different patients and for the
same patient in different PA sessions.
It was important to prove that BEAT has sufficient
sensitivity to detect minute motions corresponding to
both chosen exercises, so such estimations are provided
below.
To recognize exercise 2 BEAT must be able to detect
vertical rotary motions with desired parameters (−45˚ -
45˚). To achieve that, the orientation angles of the device
were calculated in the points corresponding to the maxi-
mal and minimal z acceleration according to Equation (1):
arcsin
xz
x
az



, (1)
where αxz is an angle between z-axis of accelerometer and
gravity vector projection to xz plane of accelerometer,
x—x-axis acceleration acquired by accelerometer, g—
gravity acceleration. Similar equation can be used to de-
rive yz orientation of the sensor. Here and further—x axis
was determined as patient “front” direction, y as “left”,
and z as “vertical” direction
The amplitude of the motion was then determined
from difference between the maximal and minimal ac-
celeration, and averaged over the length of the exercise
session. Angular resolution (and angular accuracy) for
the angle range of −45˚ - 45˚ (typical for our exercises)
assuming 0.01 g accuracy of acceleration is less than one
degree (ranged from 0.57˚ to 0.81˚) accordingly to (1).
Acceleration diapason size for exercise with angular am-
plitude of 60˚ is expected to be ~0.9 g so it is signifi-
cantly higher that accelerometer accuracy. The Equation
(1) is only accurate when sensor movement acceleration
is small in comparison with gravity, and it was found to
be the case for foot movements during exercises de-
scribed above, so this approach allows us to determine
vertical rotations of patients feet with high accuracy in
case of small accelerations and moreover, determine ab-
solute orientations of feet during the exercises. Similar
results could be achieved by using a gyroscope instead of
accelrometer; however, use of a gyroscope would sig-
nificantly reduce battery lifetime (typical current needed
to supply a gyroscope exceeds 3.5 mA while entire
BEAT sensor consumes only 1 - 1.5 mA to supply both
the microprocessor and accelerometer).
To ensure that accelerometer sensitivity enables it to

A. SHAPOREV ET AL. 55
pick up minute accelerations produced by foot motion,
we performed the test in which healthy volunteers were
asked to perform Exercise 1 relatively slowly. Accelera-
tions recorded in this test ranged from −0.25 g to +0.25 g
giving signal-to-noise ratio of ~25.
Thus calculations, simulations and tests performed
shows that accelerations produced during both types of
foot exercises are well within the range of the BEAT
susceptibility.
A smartphone application (BEAT app) was developed
to collect and analyze the data from BEAT sensors (Fig-
ure 3). It allows a user to connect smartphone to the
BEAT sensor via Bluetooth. Then patient is then pro-
mpted to choose the exercise they will do (according to
the schedule prescribed by their care provider and stored
by the BEAT app). After pressing the “Start” button,
BEAT app begins acquisition and analysis of the data
from the BEAT sensor. Results of the data analysis
(number of exercises and confirmed session duration) are
displayed on the smartphone screen so that the patient
could monitor their progress in real-time and adjust their
exercise technique if the BEAT app determines that the
exercise is performed incorrectly. After pressing the
“Stop” button the BEAT app discontinues data acquisi-
tion, calculates combined session outcomes, displays them
to the user and attempts to send them to the secure server.
If this attempt is unsuccessful, BEAT app stores the ses-
sion outcomes in the smartphone memory until connec-
tion is available and the data are successfully transmitted.
The developed BEAT system (BEAT sensor + BEAT
app) is able to perform adherence measurements for
minimally ambulatory patients in real-time and thus dif-
fers from the majority of traditional commercially avail-
able accelerometers. In typical accelerometer designs,
epochs (with typical values of 30 - 60 seconds) are estab-
lished that represent time-intervals of information. The
realtime approach implemented in the BEAT system is
expected to be more efficient by providing immediate
feedback to the patient and providing the capability of
automated encouraging messages from the centralized
server as the PA therapy progresses.
A motion recognition algorithm was developed for
BEAT app to distinguish between different exercises, to
make sure correct exercise is performed and to determine
cumulative outcomes, including the number of exercises
done, average frequency, total duration, and motion am-
plitudes. The algorithm first adjusts the coordinate sys-
tem and eliminates errors introduced by variability of the
BEAT sensor orientation (sensor displacement correc-
tion). In the next step, BEAT app analyzes acceleration
patterns and matches their parameters with the prere-
corded ones for currently selected exercise. If they
matched, program records exercise parameter and keeps
monitoring until next exercise is observed or user fin-
ishes the session. The following recognition parameters
are used for analysis: threshold intensities, maximally
acceptable noise/incorrect signal ratios, exercise interval/
frequency limits (both minimal and maximal). In case of
incorrect exercise (e.g. patient performs exercise 2 in-
stead of exercise 1 set in the app) acceleration pattern
parameters do not match, and no exercise is recorded,
even though acceleration extremas are observed and
found. Using the same algorithm, BEAT app is able to
recognize pauses during the exercise and exclude them
from the total exercise duration.
A pilot validation of the BEAT system was performed
with two healthy volunteers as described in the Methods
section. No false positives were recorded in resting vol-
unteers. Exercise sets were designed assess accuracy of
recognition by BEAT software. The outcomes reported
by BEAT app were compared to those determined from
reviewing the video records. Notably, acceleration pat-
terns from different volunteers doing the same exercise
were very similar (Figure 6). It is possible however, that
variability will be much larger in a larger scale study
with deconditioned patients. Should that happen, we plan
to perform orientation session with the participants prior
to the beginning of the study. In these sessions, we will:
1) Teach the participants to perform the prescribed exer-
cises correctly, and 2) Individually adjust the parameters
in the recognition software (acceleration thresholds and
target exercise frequencies/durations) for each of the par-
ticipants to recognize patient-specific patterns for each of
the exercises.
The results of the validation study are presented in Ta-
ble 2. It can be clearly seen, that developed BEAT system
showed high accuracy of distinguishing between differ-
ent exercises for each of the subjects. If needed, accuracy
can be further improved by application of individually
adjusted filters for each of the subjects. Number of false
positives was found to be below 3% for exercise 1, and
below 1% for exercise 2. Detailed analysis of the accel-
eration components showed that these false positives
originate from incorrect foot orientation during the exer-
cise and in some cases because of misalignment of the
sensor on the slipper. If number of false positives in-
creases in a larger scale study, it can also be improved by
individual adjustment of parameters in the recognition
software.
Testing also showed that the data were uploaded to the
server almost instantly after the end of each session if the
smartphone had a regular 3G or Wi-Fi Internet connec-
tion indicating that the BEAT system is applicable for
real-time monitoring of the subjects.
4. Conclusion
To conclude, we developed and manufactured a prototype

A. SHAPOREV ET AL. 56
(a)
(b)
Figure 6. Exercise accelerations recorded for two healthy
volunteers, exercise 1 (a) and exercise 2 (b) patterns (scale is
similar for both volunteers).
Table 2. Results of the validation study. Positive recognition
—percentage of recognized exercises when BEAT app was
re cog niz ing exercise of same type as user was actually doing;
False positive recognition—percentage of exercises errone-
ously recognized as correct while user was doing incorrect
exercises. Actual number of exercises was determined using
video recording.
Average for two volunteers
Exercise 1 Exercise 2
True positives, counts 309/300 363/360
False positive, counts 7/360 1/ 300
mHealth system for remote monitoring and stimulation
of adherence to PA in deconditioned patients. The system
consists of a miniature accelerometer-based sensor, smart-
phone application, and network service. Validation test-
ing showed high reliability and reproducibility of the
BEAT sensors. Pilot study with human subjects demon-
strated high accuracy of the BEAT system in recognition
of different exercises and calculating overall outcomes of
PA. Taken together, these results indicate that BEAT
system could become a valuable tool for real-time moni-
toring of PA in deconditioned patients.
5. Acknowledgements
This work was supported by NIH Grant No. 2P20RR-
016461-10, awarded to the South Carolina Research
Foundation, NIH Grant No. P20RR021949, seed grant
form Clemson Cyberinfrastructure institute, grants from
the South Carolina Clinical & Translational Research
Institute, with an academic home at the Medical Univer-
sity of South Carolina, Clinical & Translational Science
Award NIH/National Center for Research Resources
(NCRR), Grant UL1RR029882.
REFERENCES
[1] R. G. Frykberg, T. Zgonis, D. G. Armstrong, et al., “Dia-
betic Foot Disorders. A Clinical Practice Guideline (2006
Revision),” Journal of Foot and Ankle Surgery, Vol. 45,
No. 5 Suppl, 2006, p. S1-S66.
K. G. Harding, H. L. Morris and G. K. Patel, “Science,
Medicine and the Future: Healing Chronic Wounds,”
BMJ, Vol. 324, No. 7330, 2002, pp. 160-163.
[2] T. J. Kelechi, A. Green, B. Dumas and S. S. Brotherton,
“Online Coaching for a Lower Limb Physical Activity
Program for Individuals at Home with a History of Ve-
nous Ulcers,” Home Healthc Nurse, Vol. 28, No. 10,
2010, pp. 596-605. doi:10.1097/NHH.0b013e3181f7e99d
[3] R. W. Motl, E. McAuley and D. Dlugonski, “Reactivity
in Baseline Accelerometer Data from a Physical Activity
Behavioral Intervention,” Health Psychology, Vol. 31, No.
2, 2012, pp. 172-175. doi:10.1037/a0025965
[4] K. Saremi, J. Marehbian, X. Yan, et al., “Reliability and
Validity of Bilateral Thigh and Foot Accelerometry Meas-
ures of Walking in Healthy and Hemiparetic Subjects,”
Neurorehabilitation and Neural Repair, Vol. 20, No. 2,
2006, pp. 297-305. doi:10.1177/1545968306287171
[5] R. Z. Xu, S. L. Zhou and W. J. Li, “MEMS Accelerome-
ter Based Nonspecific-User Hand Gesture Recognition,”
IEEE Sensors Journal, Vol. 12, No. 5, 2012, pp. 1166-
1173. doi:10.1109/JSEN.2011.2166953
[6] R. C. Wagenaar, I. Sapir, Y. Zhang, et al., “Continuous
Monitoring of Functional Activities Using Wearable,
Wireless Gyroscope and Accelerometer Technology,”
Conference Proceedings of the IEEE Engineering in
Medicine and Biology Society, Vol. 2011, 2011, pp. 4844-
4847.
[7] D. Vybiral, M. Augustynek and M. Penhaker, “Devices
for Position Detection,” Journal of Vibroengineering, Vol.
13, No. 3, 2011, pp. 531-535.

A. SHAPOREV ET AL. 57
[8] M. Benocci, C. Tacconi, E. Farella, et al., “Accelerome-
ter-Based Fall Detection Using Optimized ZigBee Data
Streaming,” Microelectronics Journal, Vol. 41, No. 11,
2010, pp. 703-710. doi:10.1016/j.mejo.2010.06.014
[9] N. Mamorita, T. Iizuka, A. Takeuchi, M. Shirataka and N.
Ikeda, “Development of a System for Measurement and
Analysis of Tremor Using a Three-Axis Accelerometer,”
Methods of Information in Medicine, Vol. 48, No. 6, 2009,
pp. 589-594. doi:10.3414/ME9243
[10] S. I. Doherty and P. Oh, “A Multi-Sensor Monitoring
System of Human Physiology and Daily Activities,” Tele-
medicine and E-Health, Vol. 18, No. 3, 2012, pp. 185-
192. doi:10.1089/tmj.2011.0138
[11] O. I. Franko and T. F. Tirrell, “Smartphone App Use
among Medical Providers in ACGME Training Pro-
grams,” Journal of Medical Systems, Vol. 36, No. 5, 2012,
pp. 3135-3139. doi:10.1007/s10916-011-9798-7
[12] B. H. Dobkin and A. Dorsch, “The Promise of mHealth:
Daily Activity Monitoring and Outcome Assessments by
Wearable Sensors,” Neurorehabilitation and Neural Re-
pair, Vol. 25, No. 9, 2011, pp. 788-798.
doi:10.1177/1545968311425908