IMPLEMENTATION AND EVALUATION

OF A PHYSICAL ACTIVITY AND ENERGY EXPENDITURE

ALGORITHM IN A SENSIUM™-BASED BODY-WORN DEVICE

M. Hernandez-Silveira, S.-S. Ang, T. Mehta, B. Wang

and A. Burdett

Toumaz UK Ltd, Building 3, 115 Milton Park, OX14 4RZ, Abingdon, United Kingdom

Keywords: Activity energy expenditure, Accelerometer, Heart rate, Calibration.

Abstract: It is well known that sedentary life style lead to conditions such as obesity and diabetes. In recent years,

there has been increasing interest in devices capable of measuring activity energy expenditure (AEE) and

physical activity intensity (PAI) without disrupting the activities of daily living. In this paper we introduce a

portable and light-weight device based on our Sensium

technology. Unlike existing commercially

available devices, the latter can measure both AEE and PAI in a real-time basis and convey the resultant

calculations wirelessly to a remote PC and/or sever. Such calculations are carried out by means of a

mathematical model, which combines heart rate and accelerometer information to produce PAI and AEE

estimations. The model was calibrated against a reference indirect calorimetry system. In particular,

simulated annealing was used to adjust the model parameters so as to allow a closer match between the

predicted and reference values. The resulting model was tested using a separate dataset with reference to

indirect calorimetry. The 95% prediction interval and the Spearman’s correlation coefficient (r) for PAI

were found to be [-0.1307, 0.171] kJ/kg/min and 0.903 (p<0.001) respectively. In addition, the results

revealed that there is agreement between Sensium

and a similar reference (validated) device.

1 INTRODUCTION

Sedentary individuals are more susceptible to a wide

range of diseases. Different studies have made

associations between the lack of physical activity,

diabetes as well as obesity and heart conditions

(Marchand et al., 1997).

Consequently, there has been substantial interest

in affordable and portable devices to enable accurate

estimations of Physical Activity (PA) on a routine

basis. An example of such a device is the

Actiheart®. It comprises a portable, light-weight

device that measures activity energy expenditure

using a group-calibrated set of equations. This

mathematical approach is known as the Branch

Equation Model (Brage et al., 2004). It has been

validated against a number of well-established

techniques, including direct and indirect calorimetry

(Brage et al., 2005, Brage et al., 2004). The

Actiheart® is mounted on the chest region of the

subject, and data that is acquired from the subject is

stored locally. Unfortunately, the Actiheart® is a

data-logger that has to be removed from the subject

to download the data for analysis. This restricts the

possibility of continuous monitoring and feedback

by medical experts in real-time, which is desirable in

some clinical and sports contexts.

In contrast, the wireless device proposed here is

capable of processing the acquired data and

streaming it wirelessly in real-time to a base station.

Thus, the results can be relayed to a central server

that can be accessed by medical professionals, who

can potentially provide information to subjects to

allow changes to lifestyles at any time.

Therefore, the aim of this work was to adapt the

branched equation model to our system, and

evaluate its performance against two valid reference

devices – an indirect calorimetry system and the

Actiheart® when tested in normal individuals.

2 RELATED WORK

In 2004, Brage and colleagues developed and

evaluated a method (Branched Equation Model -

BEM) for measuring levels of PA and EE by

219

Silveira M., Ang S., Mehta T., Wang B. and Burdett A..

IMPLEMENTATION AND EVALUATION OF A PHYSICAL ACTIVITY AND ENERGY EXPENDITURE ALGORITHM IN A SENSIUM

-BASED

BODY-WORN DEVICE.

DOI: 10.5220/0003786902190223

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2012), pages 219-223

ISBN: 978-989-8425-91-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

combining accelerometry with HR monitoring,

demonstrating improved estimation of these

parameters when tested in 12 male normal subjects.

The approach relies on a set of rules, regression

equations and thresholds to estimate the PA or EE.

Thus, these parameters are estimated by means of the

selected piecewise function (i.e. one out of the four

available in the branched model) which best suit to the

level and intensity of the activity currently performed.

Later, the BEM approach was implemented in

the ActiHeart® (CamNTech, Cambridge, UK). This

device was shown to be reliable in estimating PA

intensity reliably in several studies (Brage et al.,

2005, Crouter et al., 2007), particularly for activities

such as walking and running. However, one

important drawback of this device is its inability to

transmit data in real time. The latter has a negative

impact on different aspects. First of all, the device

may need to be applied to and removed from the

patient several times until downloaded data reflects

the adequacy of electrode placement. Secondly, the

data logging capability is limited. For these reasons,

this device is neither an option for long-term follow-

up studies requiring several weeks or months; nor for

a final product intended for continuous real-time

feedback of physical activity and calorie expenditure.

3 SENSIUM PA-EE ESTIMATION

The approach adopted for PA-EE estimation can be

explained from Figure 1 as follows:

Figure 1: Block diagram of the Sensium™ PA-EE

algorithm.

Raw ECG and tri-axial accelerometer data are

collected by the Sensium

body worn device at

sampling frequencies of 250 and 50 Hz respectively,

whereas patient information (i.e. age, gender, weight

and height) is manually entered by the user into the

system. The ECG and accelerometer data are fed to

the HR and AAC modules in fixed epoch durations

of 15 seconds.

The HR module was based on the Open Source

ECG Algorithm (OSEA) (Hamilton and Tompkins,

1986, Pan and Tompkins, 1985). The authors of

OSEA have reported high reliability and accuracy of

OSEA when tested using ECG data from the MIT-

BIH database. Nevertheless, a number of

modifications were necessary to adapt the algorithm

to the Sensium™. Firstly, the Sensium™ is

positioned at a non-standard position (lower chest

region, parallel to the Lead 1 position).

Subsequently, the threshold that is used in QRS peak

detection has been adjusted accordingly. Secondly,

extra rules have been included to reject signals

corrupted by motion artefacts. A preliminary

evaluation indicated that these changes did not affect

the efficacy of this algorithm. These results are

available on request to the authors.

The AAC algorithm is based on previous work

by Bouten and colleagues. Firstly, the signal is

filtered using a Butterworth fourth-order band-pass

filter (0.25-6Hz), designed for rejecting spurious

noise without distorting the information

corresponding to physical activities associated with

the intended user population. Of particular interest,

the upper limit of the filter bandwidth was chosen to

attenuate high frequency disturbances occurring

when the swinging foot impacts the ground during

walking at initial contact. This frequency band is in

the region of 15 Hz (Antonsson and Mann, 1985).

After filtering, the accelerometer data corresponding

to each axis is individually rectified and integrated

over 15 seconds to obtain AAC.

In the final stage of the algorithm, the HR and

AAC information are used to estimate the physical

activity intensity. As discussed above, such

estimation is possible by means of a rule-based

algorithm that relies on a set of pre-defined

thresholds, regression equations and weights,

expressing the existing relationship between the

duple HR/AAC and energy expenditure derived

from oxygen consumption (VO2).



BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices

220

4 EXPERIMENTAL METHODS

AND CALIBRATION

To calibrate the BEM, and to assess the reliability of

this model, experiments were conducted to collect

two separate datasets. In both experiments, the

activities undertaken by the subjects include

stepping exercises, cycling on a stationary bicycle,

walking and running on a treadmill. These

experiments have been approved by the Toumaz

internal ethics committee.

In the calibration dataset, the accelerometer,

ECG and VO2 data were simultaneously collected

from 8 healthy participants (6 males and 2 females;

age 26.11 ± 11.45 years old; weight 72.01 ± 9.35

Kg; and height 157.72 ± 59.47 cm) using the

Sensium

and a indirect calorimeter (CPX-express,

Medgraphics, USA). The indirect calorimeter

automatically converts the VO2 data into normalised

PAI units (cal/kg/min) using the widely accepted

Weir formulation (Weir, 1949).

The calibrated Sensium™ algorithm was tested

using a dataset collected from three systems:

Sensium

, Actiheart®, and indirect calorimetry.

The experimental subjects involve 6 additional

healthy volunteers (1 female, 5 male), of weight

69.62 ± 11.25 Kg, height 174.95 ± 9.36 cm, and

26.67 ± 4.32 years old.

4.1 Calibration Process

In the calibration process, four piece-wise functions

together with a set of thresholds and coefficients are

required to determine PAI at low-moderate and

moderate-high intensities using the BEM approach.

These regression functions describe the relationships

between the PAI and HR, as well as between PAI

and AAC. Data from only treadmill activity was

used to obtain the equations, as treadmill exercise is

the best controlled part of the experiment.

First, the transition points between piece-wise

functions were selected by means of visual

inspection. Specifically, this was performed by

manually adjusting the value of the transition point

thresholds for both HR and AAC data; and then re-

running the regression procedure repeatedly to

generate the curves that best fit to the treadmill data.

The resultant HR-PAI polynomials for low-moderate

(PHL) and moderate-high PAI (PHH) are shown in

(1) and (2).

0376.02475.0 HsHsPHL +−=

(1)

2588.431364.1 +

HsPHH

(2)

‘Hs’ corresponds to the HR above sleeping, and it

was obtained by subtracting 10 bpm from the resting

heart rate (RHR), as shown in (3). This is consistent

with the procedure found in (CamNTech, 2009).

10−

RHRHs

(3)

Likewise, the AAC-PAI expressions for low-

moderate (PAL) and moderate-high (PAH) levels of

activity are found using (4) and (5).

AACPAL 167.0

(4)

9311294.811921572.0

0002832.0 +−= AACAACPAH (5)

Subsequently, the HR flex-points for low-moderate

and moderate-high activity levels were determined

by applying regression analysis over all the data

points collected from different types of exercises

except resting, since HR is not a reliable parameter

for estimation of EE at low activity levels (Andre

and Wolf, 2007). The heart rates (above sleeping)

corresponding to 3.5 and 5.5 METs were then

derived as initial HR flex-points. Likewise, the

initial AAC flex-point between moderate and high

levels of activity was found using regression,

involving only the cycling data. This made possible

the selection of a threshold value which was low

enough to reject the majority of noise, but

sufficiently high to account for the low ground-

impact of some strenuous activities such as cycling,

rowing and cross training.

The initial weights towards the AAC-PAI and

the HR-PAI relationships were chosen from (Brage

et al., 2004). Further refinement to the model is

carried out by Simulated Annealing (Bertsimas and

Tsitsiklis, 1993). Using this technique, the weights,

and threshold values were adjusted to minimize the

absolute error of the model. The optimized model is

shown in Figure 2.

Figure 2: Branch equation model after the application of

simulated annealing.

IMPLEMENTATION AND EVALUATION OF A PHYSICAL ACTIVITY AND ENERGY EXPENDITURE

ALGORITHM IN A SENSIUM™-BASED BODY-WORN DEVICE

221

Figure 3: Activity energy expenditure results for different activity types, derived from Actiheart®, Sensium™, and indirect

calorimetry.

4.2 Discussion

Inferential statistics using ANOVA (Analysis of

Variance) was carried out on the experimental

datasets. Table 1 shows the results. These results

indicate that the differences between the Indirect

Calorimetry, Actiheart, and Sensium measurements

are statistically significant.

Table 1: ANOVA and t-test results from indirect

calorimetry, Sensium™, and the Actiheart®.

F-test

Mean sum of

squares

p-value

16.96 0.801 <0.001

t-test results from Sensium™ and Actiheart®

Degrees of

freedom

95% CI

(kJ/kg/min)

Difference of the

means (kJ/kg/min)

2209 [0.0170, 0.0234] 0.0202 (p<0.001)

The Bland-Altman plot corresponding to the

Sensium

and indirect calorimetry (Figure 4)

reflects a bias and the 95% PI of 0.0179 kJ/kg/min

(0.26 MET) and [-0.134, 0.170] kJ/kg/min ([-1.922,

2.438] METs) respectively. These results indicate

that the differences for the Sensium

and

Actiheart®, with reference to indirect calorimetry,

are similar. Also, by comparing the Actiheart® with

the indirect calorimeter, the 95% PI was found to be

[-0.170, 0.246] kJ/kg/min. This range is consistent

with the a previous study done by (Brage et al.,

2004). In addition, a two-tailed t-test was carried out

between the Actiheart® and Sensium™, in order to

confirm the similarity between these two devices.

The results of this test are summarised in Table 1,

and revealed statistically significant (although small)

differences.

Finally, the results from the second experiment

were grouped into different categories of activities,

as shown in Figure 3. From the chart, it can be

observed that the average activity expenditure for

the Sensium

and Actiheart are similar for most of

the activities. For the step test and cycling activities,

the Sensium™ algorithm produced results closer to

indirect calorimetry than the Actiheart®. Overall,

the results from the Sensium

were found to be

closer to the ones obtained from Indirect

Calorimetry system. This can be expected as the

Sensium

algorithm was calibrated with data

obtained from this particular reference system.

Figure 4: Bland-Altman plot of indirect calorimetry vs

Sensium™.

5 CONCLUSIONS

This paper reported on the incorporation of an

BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices

222

algorithm for estimation of physical activity

intensity and energy expenditure as part of a

wireless body-worn device. The algorithm was

calibrated for a Sensium

device, embedded with a

triaxial accelerometer and ECG sensors.

The results for the evaluation of the algorithm

revealed that statistically significant differences

between indirect calorimetry, Actiheart, and the

Sensium™. However, these differences were small

and similar to those found in a separate study

(Crouter et al., 2007). In addition, it was found that

with reference to indirect calorimetry, the mean

error for the Sensium

was lower for certain

activities, including the step test exercise and

cycling on a stationary bicycle.

In this work, the authors found that the use of

simulated annealing was successful in adapting the

Branch Equation Model to the Sensium™ platform,

indicating the generality of this model. Future work

include the use of automatic activity classification,

to reduce the errors caused by different activity

types. Another limitation of this investigation is the

limited scope of activities considered. Therefore,

future directions will consider the inclusion of

further and more representative activities of daily

living and exercise.

ACKNOWLEDGEMENTS

The authors will like to thank the volunteers who

took part in the experiments.

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ALGORITHM IN A SENSIUM™-BASED BODY-WORN DEVICE

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