Data Format for Storing ANT+ Sensors Data

Petr Je

zek and Roman Mou

cek

Department of Computer Science and Engineering, New Technologies for the Information Society,

Faculty of Applied Sciences, University of West Bohemia, Plzen, Czech Republic

Keywords:

ANT+, NIX, Sensor, Data/metadata Storage, EEGBase, Data Format, eHealth.

Abstract:

Medical treatment of sudden and especially chronic diseases has become more expensive. People suffering

from a variety of diseases had been traditionally treated in hospitals for a long time. Fortunately, the current

situation has been changing also thanks to relatively cheap body sensors and development of systems for

home treatment. It brings inconsiderable cost savings and improves patients’ comfort. On the other hand,

it puts demands on the used technical infrastructure and home treatment system developers who must solve

integration of different systems. A crucial point is a deﬁnition of uniﬁed data formats facilitating transfer and

storage of data to/in remote databases. There are standards and APIs such as Zigbee, Bluetooth low energy or

ANT+ that deﬁne a protocol for data transfer. However, they do not deﬁne a suitable format for long term data

storing. In this paper, data coming from ANT+ sensors have been studied and metadata related to all kinds of

body sensors and raw data and metadata speciﬁc to individual sensors have been deﬁned. Then a framework

organizing data and metadata obtained from ANT+ sensors into an open and general data format suitable for

long term storage of sensor data is introduced. Finally, a sample use-case showing the transfer of data from

a sensor into a data storage is presented.

1 INTRODUCTION

Medical treatment of sudden and especially chronic

diseases has become more expensive, especially with

aging population. For instance, there were around 23

million people in the world affected with heart failure

in 2011 (Bui et al., 2011). These people had been usu-

ally treated in hospitals for a long time. The situation

has been changing at present because relatively cheap

solutions for home treatment have appeared in the

market (Surie et al., 2008), (Kyriacou et al., 2009) and

patients can be moved from hospitals to their homes

sooner. It brings advantages of a better comfort for

patients and makes treatment generally cheaper.

Home treatment systems use a set of wearable sen-

sors, usually powered from batteries, for monitoring

of health or ﬁtness level. They have to operate for

a long time period without possibility to change bat-

teries frequently. That is why new protocols with low

energy consumption such as ZigBee (Farahani, 2008),

Bluetooth Low Energy (Heydon, 2012) or ANT (Za-

loker, 2014) have been developed. Data from these

sensors are transferred to remote servers where they

are processed and visualized. Body Area Network

(BAN) is an integration of sensors providing a large

data collection of body parameters. When the number

of sensors connected to BAN increases, requirements

for the management, long term storage and sustain-

ability of acquired data also increases.

Although there are some low energy consumption

standards for data transfer, these are too fragmented

to allow easy manipulation with obtained data. Of

course, these standards also do not provide means

for long term storage and management of transferred

data. As a solution this paper presents how to use

a general data format called NIX (Stoewer et al.,

2014) for encapsulating and storing ANT+ sensor

data.

The paper is organized as follows. Section 2 deals

with sensor infrastructure and description of data ob-

tained from sensors. Section 3 describes existing

ANT+ proﬁles; the most suitable proﬁles for eHealth

domain are selected. Section 4 introduces a frame-

work that facilitates conversion of sensor data to the

NIX format. Section 5 presents the usage of proposed

transformation, a simple use-case is provided. Sec-

tion 6 summarizes the work and provides an outlook

to the future.

396

JeÅ¿ek P. and MouÄ ek R.

Data Format for Storing ANT+ Sensors Data.

DOI: 10.5220/0006229103960400

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 396-400

ISBN: 978-989-758-213-4

2 STATE OF THE ART

There are several approaches aiming to manage Body

Area Networks data. These solutions use semantic

web technologies or cloud infrastructures. Three lay-

ers ontology describing data from different sensors is

presented in (Mehmood et al., 2014). This ontology

facilitates development of tools used for processing of

sensor data. A Sensor-Cloud infrastructure (Yuriyama

and Kushida, 2010) represents physical sensors as vir-

tual sensors stored in a cloud infrastructure. Semantic

Sensor Web (Sheth et al., 2008) is based on annota-

tion of sensors data by means of Semantic Web. Such

annotated data can be distributed on the Internet.

3 ANT+ PROFILES DISCUSSION

The ANT protocol is popular because of its low en-

ergy consumption and existence of suitable means

for description of body parameters. ’Device proﬁles’

(called ANT+) deﬁne data over the network in a con-

sistent way (Innovations, 2013) and facilitate devel-

opment of sensors and management of sensor data in

application programs.

ANT+ proﬁles support a large scale of activities

such as cycling, walking, or measurement of body pa-

rameters such as heart rate, blood pressure, weight, or

muscle oxygen. When browsing individual proﬁles

in a detail we ﬁnd the attributes that are common for

all proﬁles, for example a device name, device status,

manufacturer, signal strength or battery status. Then,

there are attributes varying for individual proﬁles. In-

dividual proﬁle attributes represent raw sensor data

or domain speciﬁc metadata while common attributes

describe general metadata (see Figure 1).

4 PROPOSED FRAMEWORK

4.1 Prerequisites

Due to the absence of a suitable format/data struc-

ture for sensor data representation we have designed

a framework for collection and storage of sensor raw

data and metadata in a deﬁned structure. The used

data structure/format has to be robust, ﬂexible and

widely accepted by scientiﬁc community to cover

a heterogeneous nature of sensor data, provide a long

term data sustainability, and ensure its re-usability in

third-party systems.

4.2 Format Discussion

Within a working group of International Neuroin-

formatics Coordinating Facility (INCF) (Bjaalie and

Grillner, 2007) and its Task Force on Electrophys-

iology

there were introduced two approaches to-

wards deﬁning a standard on electrophysiology data.

The ﬁrst one uses the Hierarchical Data Format

(HDF5) (HDF5 Group, 2013). HDF5 is portable

and extensible format supporting an unlimited vari-

ety of datatypes that is designed for ﬂexible and ef-

ﬁcient I/O operations with high volume and com-

plex data. The second approach uses odML (Grewe

et al., 2011) as a free form tree-like structure of

sections, properties and values suitable for meta-

data description. This simple, platform-independent

and human-readable format also ensures compatibil-

ity with other systems developed within the commu-

nity such as (Zehl et al., 2014), (Le Franc et al.,

2014), and (Davison et al., 2013). The next step of the

task force, merging of these two approaches (Teeters

et al., 2013), resulted in the proposal of the NIX for-

mat (Stoewer et al., 2014) that provides a data model

for storing experimental data in HDF5 together with

its metadata internally organized in the odML format.

The NIX format is currently used in Helmholtz (Davi-

son et al., 2013) and EEGBase (Jezek and Moucek,

2012) projects.

Although the NIX format was intended to be used

in electrophysiology, its general deﬁnition makes it

suitable for any time series data.

4.3 Proposed Mapping

We selected ANT+ proﬁles relating to person health

and/or ﬁtness level. Figure 1 shows common meta-

data (see the central circle) and domain speciﬁc raw

data and metadata (see other circles). The NIX model

consists of several main elements: Block, DataAr-

ray, Tag, MultiTag, Source, Group, and Dimension.

Each element includes a set of attributes (such as id,

name, speciﬁc attributes) and link to metadata orga-

nized in the odML structure. We used a simpliﬁed

NIX model for mapping ANT+ elements. The Source

element represents an ANT+ device, DataArray rep-

resents raw data, Dimension represents description of

graph axes, and Block wraps a complete record.

http://www.incf.org/programs/datasharing/

electrophysiology-task-force

Data Format for Storing ANT+ Sensors Data

397

ANT+ device

name, ANT+

device status,

ANT+ device

number,

Battery status,

Manufacturer

identiﬁcation,

Manufacturer

speciﬁc data,

ANT+ signal

strength,

Product

information

Bicykle

power

Torque

Speed

Torque

Cadence

Torque

Eﬀective-

ness

and

Pedal

Smooth-

ness

Bicycle

Speed

and

Cadence

Speed

Sensor

Latest

Speed

Event

Time

Cumu-

lative

Wheel

Revolu-

tions

Stopped

ﬂag

Cadence

Sensor

Latest

Cadence

Event

Time

Cumu-

lative

Pedal

Revolu-

tions

Stopped

ﬂag

Blood

Pres-

sure

Systolic

Diastolic

Time-

stamp

Mean

Ar-

terial

Morning

evening

Heart

Rate

Heart

Rate

Mon-

itor

Last

Heart

Beat

Time

of Pre-

vious

Heart

Beat

Heart

Beat

Count

Heart

Rate

Muscle

Oxygen

Mon-

itor

Hemo-

globin

Con-

cen-

tration

Satu-

rated

Hemo-

globin

Capa-

bilities

Weight

Scale

Hydra-

tion

Body

fat

Active,

Basal

Metabolic

Rate

Muscle

Mass

Bone

Mass

Stride,

Speed,

Dis-

tance

Distance

Speed

Calories

Stride

Count

Figure 1: ANT+ Proﬁles Network.

5 USE CASE

The presented framework serves mainly to designers

and programmers of the systems for home monitor-

ing. Let’s assume the following use-case. A program-

mer wants to implement a system for heart rate moni-

toring of elderly people. There are the following sys-

tem requirements: sensors have to be easy to use and

sensors data must be easily transferred to a computer

where they are stored and evaluated. Moreover, dur-

ing regular medical checks a physician uses long term

records to check health condition of the patient and

eventually starts a treatment. It means that both the

patient and physician have access to the infrastructure

that ensures data storing and management as well as

data security, consistency and sustainability.

In this use-case we used the Garmin Premium

Strap Heart Rate Monitor as a representative of ANT+

supporting devices. The Android SDK

was used to

read ANT+ data into Android smart phones. We in-

tegrated this SDK into a custom mobile application

MoBio

that reads data from ANT+ sensors and store

http://developer.android.com/sdk/

https://github.com/NEUROINFORMATICS-GROUP-

FAV-KIV-ZCU/MoBio

Section

name = heart rate

type = metadata

Properties

Device name = Strap heart rate

monitor

Device number = 1

Product Information = Garmin

Premium Strap Heart Rate Monitor

Figure 2: Metadata from the heart rate sensor in odML

structure.

them on a SD card. The user of MoBio can pair avail-

able sensors, record data and visualize them. This

solution is available to a large number of users due

to the existence of cheap Android smart phones and

heart rate monitor straps on the market.

Since our framework was also integrated into Mo-

Bio, recorded data can be stored in the NIX format.

MoBio parses the record, metadata are transferred

into a structure with one section and several proper-

ties (see an example in Figure 2) and continuously

read heart beats data are stored into the DataArray el-

ement (see an example in Figure 3). The Source ele-

ment has an attribute metadata that contains a link to

HEALTHINF 2017 - 10th International Conference on Health Informatics

398

Figure 3: Heart rate record in the NIX format.

Figure 4: Metadata stored in EEGBase.

the odML structure.

Once the data and metadata are stored they can be

transferred to a suitable database. Figure 4 shows the

metadata stored and visualized in EEGBase. A com-

plete description of the experiment contains metadata

from the Heart Rate strap. The raw data are stored as

well.

6 CONCLUSIONS AND FUTURE

WORK

Together with raising popularity of sensors for home

treatment several low energy standards have been de-

ﬁned. These standards enable data to be transferred

from body sensors into common computers where

they are processed and visualized. ANT+ supported

by signiﬁcant sensors producers is one of the most

used standards. Although transfer protocols and sev-

eral APIs for working with sensors are deﬁned, an

open standard for storing sensors data are not substan-

tially provided. Since home treatment systems use

proprietary data formats, they cannot be easily inte-

grated with variety of sensors.

In this paper we overcome these difﬁculties by

designing a framework that maps data from ANT+

sensors into the open and generally applicable NIX

format. The format brings advantages of two layers

structure, metadata are structured using the ﬂexible

odML format and data are organized using the HDF5

format. Two layered organization of ANT+ sensors

data is also a signiﬁcant contribution of this work.

The functionality of the framework is shown on

a simple use case. Within our future work the test-

ing of a large collection of sensors followed by data

transfer to a few databases is supposed. We also plan

to invite developers of home treatment systems to in-

tegrate the framework into their solutions. When the

framework is fully tested, we start to work on the

Data Format for Storing ANT+ Sensors Data

399

transformation of data using the Bluetooth low energy

standard into the NIX format as well.

ACKNOWLEDGEMENTS

This publication was supported by the project

LO1506 of the Czech Ministry of Education, Youth

and Sports under the program NPU I.

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