A Telecare System on Smart Watches that Communicate with Wireless
Bio Sensors
Akio Sashima, Mitsuru Kawamoto and Koichi Kurumatani
Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST),
2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Keywords:
Telecare System, Wireless Bio-sensor, Wristwatch-type Device, Smart Watch, Peer-to-peer, Tactile Interface.
Abstract:
In this paper, we propose a prototype of telecare system by using wristwatch-type devices, so called smart
watches. In the prototype system, the smart watch receives physiological data of a user, such as heart beats
and body movements, by communicating with a wireless bio-sensor worn by the user. The smart watch sends
the physiological data to the other users’ smart watches connected to the Internet. The sensed data are shared
among family members in a peer-to-peer manner so as to remotely monitor the physical health status of the
other members. We have designed a user interface which visually shows the remote user’s current body posture
and enables the others to tactilely feel his/her heart beats. We describe the overview of this prototype system
and its user interface implemented with the smart watches. We show experimental results of communication
performances of the system.
1 INTRODUCTION
Recently, wristwatch-type information devices, so
called “smart watches”, have drawn attention of in-
dustrial and research communities. In general, the
wristwatch-type information device is regarded as a
peripheral device of smartphone because it is a small
sensing device attached with the user’s wrist to sense
her/his activity and shows alerts of the connected
smartphone, such as when a mail is received. How-
ever, some of them can be used as stand alone devices
which do not need to work with smartphones because
they have wireless communication modules, such as
Wi-Fi and LTE (4G), to independently communicate
with other devices on the Internet.
In addition, as a user interface device, wristwatch-
type devices have distinguished features from the
smartphones. For example, a feature is intimacy of
the devices: they are attached with the users wrists
and stay close with the users at all times; they can
directly provide tactile information for the users by
using their vibration. By utilizing this feature, we
can create a new service that directly and successively
provides the telecare information with care givers. An
example of such intimate service is “Mediated social
touch,” (Haans and IJsselsteijn, 2006). It is an impor-
tant issue in teleacare researches how we can mediate
social touch and tactile information between family
members.
In this paper, we describe a prototype of peer-to-
peer telecare system by using wristwatch-type infor-
mation devices instead of smartphones. The system
is implemented with small wireless bio-sensors and
wristwatch-type devices. Physiological information
of a service user, such as electrocardiogram and body
movements, are sensed by the bio-sensor and shared
with the others’ devices in a peer-to-peer manner. Uti-
lizing the above features of wristwatch-type device,
we have developed a user interface which visually
shows the remote user’s current body movements and
enables the others to tactilely feel his/her heart beats.
The user interface has been designed to realize intu-
itive communication in family members. First, we de-
scribe overview of the system and its user interface.
Then experimental results to investigate its communi-
cation performances are shown.
2 BACKGROUND
Mobile sensor devices have been used in telecare
services to remotely monitor statuses of older per-
sons who live alone (Lin, 2012)(Triantafyllidis et al.,
2015)(Sashima et al., 2008). The services provide
their health and activity statuses, which are obtained
by the wearable devices, for their trustworthy persons
Sashima A., Kawamoto M. and Kurumatani K.
A Telecare System on Smart Watches that Communicate with Wireless Bio Sensors.
DOI: 10.5220/0006248404290434
In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 429-434
ISBN: 978-989-758-213-4
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
429
Internet
(Wi-Fi)
Connection
Management
Server
Connection
Management
Smart Watch
Sender
Smart Watch
Receiver
Sensor Data
Internet
(Wi-Fi)
Connection
Data
Internet
Wi-Fi)
Connection
Data
Cared person
Ex. elderly person
Care giver
Ex. family member
Wireless
Bio-Sensor
Figure 1: Overview of telecare service for elderly person.
who take care about them, such as caregivers, family
members, doctors, by using smart phones.
So far, in order to implement such telecare ser-
vices, most of them have been built based on server-
client communication model; all of sensing data of
the older person are sent to a central data server and
the server manages and provides the data of the users.
However, the approach has a drawback to start up and
maintain a practical service in reality. It is about man-
agement costs of the physiological sensing data in a
central data server; the management costs rise as in-
creasing personal health data in the server because it
requires careful handling.
In order to prevent the management issue aris-
ing from the server-client model, we have proposed
peer-to-peer communication model (P2P communi-
cation model) for telecare services (Sashima and Ku-
rumatani, 2016). The P2P communication model as-
sumes that the smartphones of the users directly com-
municate with each other in a peer-to-peer manner.
The sensing data are directly sent to a smartphone of
a caregiver not going through a central data server so
that the management cost is less.
In this paper we apply the P2P communication
model to a telecare system on wristwatch-type de-
vices. We have implemented a prototype of telecare
system based on the P2P communication model and
evaluated the validity of the system. In addition, we
propose a user interface that fits for wristwatch-type
devices.
3 TELECARE SERVICE MODELS
We propose two service models using the peer-to-peer
telecare system implemented on wristwatch-type de-
vices;one is for elderly person is shown in the figure
1; the other is for family members is shown in the
figure 2.
Internet
(Wi-Fi)
Connection
Management
Server
Connection
Management
Smart Watch
Smart Watch
Sensor Data
Internet
(Wi-Fi)
Connection
Data
Connection
Data
Family Member
Family Member
Wireless
Bio-Sensor
Internet
Wi-Fi)
Wireless
Bio-Sensor
Figure 2: Overview of P2P telecare service for family mem-
bers.
3.1 Telecare Service for Elderly Person
In “telecare service for elderly person” model, we as-
sume that the service consists of a wireless bio-sensor,
a connection management server, a wristwatch-type
device of sensed user and a wristwatch-type device
of remote user. The service user, who wears a
wristwatch-type device and a wireless bio-sensor, is
an elderly person living in the nursing care facilities
which has Wi-FI access points. The other remote
users are care givers (e.g., family member, medical
staffs). The bio-sensor senses the user’s physiological
data, such as electrocardiograph data, and wirelessly
sends the data to the his or her wristwatch-type de-
vice. The data which represents the user’s physical
status are sent to the other remote wristwatch-type de-
vices connected to the Internet. The network address
of the remote devices, which is used for establishing
a communication channel among devices, are sent by
the connection management server. Hence, it is pos-
sible to share the latest physical status of the user by
using the wristwatch-type device.
3.2 P2P Telecare Service for Family
Members
In “P2P telecare service for family members” model,
we assume that the service consists of a connection
management server, two wireless bio-sensors, and
two wristwatch-type devices. Each service user has a
wristwatch-type device and a bio-sensor. This model
is a symmetrical telecare service model for family
members; each member can be take care of other
members. The difference between “telecare service
for elderly person” model and this model is that the
data flows in both directions between the wristwatch-
type devices.
HEALTHINF 2017 - 10th International Conference on Health Informatics
430
4 IMPLEMENTATION
4.1 System Components
The system consists of the following components.
4.1.1 Wireless Bio-sensor Device
A wireless bio-sensor device is shown at the left side
in Figure 3. The sensor device is attached to a user’s
chest by sticking electrodes with a peel-off sticker.
The device consists of 5 kinds of sensors (electro-
cardiograph, 3-axis accelerometer, barometer, ther-
mometer, hygrometer), a lithium ion battery, and a
Bluetooth
1
module which communicates with a smart
watch. In the prototype, the device continuously
senses electrocardiographic data and 3-axes acceler-
ation data of the user and wirelessly sends the data to
the smart watch.
4.1.2 Wristwatch-type Information Device
(Smart Watch)
We have implemented the system on the Android
Wear smart watches
2
. The wristwatch-type informa-
tion device is shown at the right side in Figure 3. It
commutates with a connection management server to
know the network address of remote user’s device. By
knowing the latest address of the device, it relays the
physiological data obtained by the bio-sensor to the
remote user’s device in a peer-to-peer manner. Us-
ing a graphical user interface of the system, the other
user can know the his or her physical status visually.
We can configure the system settings e.g., specifying
a opponent service user, by the user interface. The
system can inform the user’s status by control the in-
ner vibrator. In this research, we use the vibrator to
provide remote user’s heart beats.
4.1.3 Connection Management Server
A connection management server stores the network
address (IP address and port number) of each user’s
smart watch, and dynamically updates them when the
smart watch connects from a different network envi-
ronment.
The network address of wristwatch-type informa-
tion device is dynamically changed according to its
network environments. To enable two devices com-
municate with each other, the network address of the
peer is required.
1
https://www.bluetooth.com/
2
https://www.android.com/wear/
Figure 3: Wireless bio-sensor (left) and wristwatch-type in-
formation device (right).
To solve this issue, we have introduced a connec-
tion management server in the system. A connec-
tion management server stores the network address of
each user’s watch devices, and updates them when the
device starts to connect to the server. It also tells the
updated address to other devices. By knowing the lat-
est address, the devices can communicate each other
even if the addresses are dynamically changed.
Notice that the server does not handle sensing data
but just initiates the communication of users.
4.2 Communication
The current wristwatch-type devices have Wi-Fi com-
munication facilities and directly communicate with
the other devices using Internet Protocols, such as
UDP. We use the communication facilities to imple-
ment the telecare system that can send and receive
UDP packets between the devices.
4.2.1 Communication Protocol
We adopt a protocol which has been proposed for fast
communications of mobile devices (Sashima and Ku-
rumatani, 2016). Figure 4 shows an outline of the
protocol. It is a right-weight protocol, which does
not have a mechanism to handle lost packets, so as
to be suitable for the limited computational resources
of wristwatch-type devices. In addition, as it does not
wait to receive acknowledge packets, it can immedi-
ately send the packet that represents the latest user
status. However, as losing packets is a weak point
of this protocol, we describe experimental results of
communication performances of the system in a later
section.
To establish a communication channel between a
wristwatch-type information device in a private net-
work and the device in the outside of the network, we
apply the NAT traversal technique (Ford et al., 2005)
that enable peer to peer communications over a NAT
router.
A Telecare System on Smart Watches that Communicate with Wireless Bio Sensors
431
Sender Server Receiver
Log on
Log on
ACK
ACK
Telling the peer logs on
Telling the peer’s UDP
socket address.
ACK
Data Request
ACK
Sending Data
Log off
ACK
ACK
Start
Start
End
End
Sending Data
Go to Start
Telling the peer logs off
Start
Repeat
Figure 4: Overview of the communication protocol
(Sashima and Kurumatani, 2016).
4.3 Data Representation
To share the sensing data between the devices in the
service, the data has two data representations for the
service models: a) “raw data representation” which
includes raw sensing data and b) “abstract data repre-
sentation” which includes the data analyzed at a wrist-
watch device connecting to wireless bio-sensor.
4.3.1 Raw Data Representation
This representation includes raw data obtained by the
bio-sensor for the application that requires to know
detailed data of the sensed user. We assume that typ-
ical usage of this representation is for medical ap-
plications, such as remote monitoring of electrocar-
diograph. Thus the communication is one way from
a sensed user to a care giver, such as medical staff.
This raw representation data is synchronously sent at
a constant sensing interval. For example, if the sam-
pling period of the sensor is 5 msec, the data is sent at
each 5 msec interval.
4.3.2 Abstract Data Representation
two way communication assumed in “P2P telecare
service for family members” requires more compu-
tational power for handling traffics. We have devel-
oped the abstract representation to reduce the traffics
in the two way communication. The representation
includes an abstract data summarized by a data se-
quence of raw sensing data: a “posture” data of the
abstract representation is derived by analyzing a se-
quence of 3-axes acceleration data; and a “heart beat”
data is derived by analyzing a sequence of electrocar-
diographic data. In addition, the abstract represen-
tation data is asynchronously sent when the system
detects a change of the status. For example, the data
is sent as soon as possible when the posture of the
user is changed. Hence, using this representation, the
amount of traffic data becomes 10–100 times smaller
than using the raw data representation in our proto-
type system.
4.4 User Interface
The wristwatch type device is tactilely attached with
the user’s wrist and stay close with the user at all
times. To utilize the features of the device, we have
designed a user interface which directly and succes-
sively provide telecare service to the user. The user in-
terface visually shows the users’ current body move-
ments and enables the user to tactilely feel other user’s
heart beats.
4.4.1 Graphical User Interface for Showing
User’s Status
Because the wristwatch-type device has a small dis-
play, we have designed a simple graphical user inter-
face showing the status of the remote user. Figure 5
shows display images of the user interface. In the fig-
ure, each image shows a physical status of the remote
user. Currently, the system classifies the following 6
statuses: standing, moving, laying-up, laying-down,
walking, unclassified. The system automatically clas-
sifies the status based on the 3-axis acceleration data
obtained by a wireless bio-sensor. The sampling rate
of the data is 100 Hz. For the classification, we have
used a rule-based algorithm using predefined thresh-
old values.
To share the classification with the raw data repre-
sentations, the devices share the raw acceleration data
and do the classification based on the data indepen-
dently. Because the classification process is done by
each device, the analysis results can be slightly differ-
ent between the devices.
To share the classification with the abstract data
representations, the devices connected to the bio-
sensor analyses the raw acceleration data and send the
abstract posture data to other devices. Because the
classification process is done by a device, the same
analysis results are shared by the devices.
4.4.2 Tactile User Interface for Feeling Heart
Beats
Based on electrocardiographic data obtained by the
wireless bio-sensor, the system automatically detects
the pase of heart beats of sensed user. To detect
the pase of heart beats, the system monitors peak lo-
cations of the R-wave by calculating differentiation
values of the electrocardiographic data, and detect
sharply changing points as the peaks of the R-wave.
HEALTHINF 2017 - 10th International Conference on Health Informatics
432
Figure 5: Graphical user interface of the prototype system. Each image shows a physical status of a remote user: standing
(left), moving (center), laying up (right). HR field shows the pase of heart beats of the user.
The sampling rate of the data is 200 Hz. It calculates
“beats per minute (BPM), which stands for the pase
of heart beats. The BPM is based on the latest R-R
period, and shown at the heart rate (HR) field in the
graphical user interface (see Figure 5).
To share the pase of heart beats with the ab-
stract data representations, the device is connected
to a wireless bio-sensor. It detects an R-peak from
the electrocardiographic data and repeatedly sends a
packet to remote user’s wristwatch device. We call
the packet corresponding to the detected R-peaks R-
peak packet. When the remote user’s device receives
the R-peak packet from sensed user’s device, it imme-
diately vibrates for a short time (30 msec). Hence, it
vibrates in synchronism with the heart beat of sensed
user so as to others can tactilely feel his/her heart beat
by the vibrations. Strictly speaking, as there are de-
lay times to receive the R-peak packets and the times
fluctuate with network conditions, others can tactilely
feel ”his/her heart beat like pattern” by the vibrations.
To share the pase of heart beats with the raw data
representations, the devices sends the raw electrocar-
diographic data to the other users. The others calcu-
late the BPM based on the raw data independently.
5 EVALUATIONS
We have experimentally evaluated communication
performances by measuring the packet loss and end-
to-end delays of the prototype system.
The experimental network environments are a Wi-
Fi network (LAN) in our laboratory. We have mea-
sured the communication performance of two devices
which connect to the same Wi-Fi access point that
might be used by the other users. To prevent the ef-
fects of the sleep mode of the devices, we have ap-
plied power to USB ports of them.
We have evaluated communication performance
in the following two conditions assuming the differ-
ent service models: one-way communication used
for “telecare service for elderly person, and two-
way communication used for “P2P telecare service
for family members. For the above two conditions,
we have compared the performance between the ab-
stract data representation and raw data representation.
We have analyzed the performance based on the data
recorded by each device in each condition. We have
used the data recorded within ten minutes from the
starting time of the communication.
5.1 Packet Losses
Experimental results about packet losses between the
wristwatch-type devices are shown in Table 1. Total
samples means a total number of the data sent by the
devices. The “condition” column of Table 1 shows
combinations of communication styles and data rep-
resentations. For example “Two Way/Raw” stands
for two way communication with raw data represen-
tation. Using the abstract representation, the total
numbers of data became small in this experiment be-
cause the data is sent asynchronously when the user
changed his or her posture.
While many packets were lost by using the raw
representation especially in the two way communica-
tion, few packets were lost by using the abstract rep-
resentation. Because the wristwatch-type device has
limited computational resources, the abstract repre-
sentation with asynchronous communication is more
suitable for the prototype than the raw representation.
5.2 End-to-end Delay
We have measured delay times of the communication
in the conditions. The results are shown in Figure 6.
Before the experiments clocks of the devices are syn-
chronized based on a clock of the connection server.
The average delay time of “Two Way/Raw” condition
is omitted in the graph because it took for a long time
A Telecare System on Smart Watches that Communicate with Wireless Bio Sensors
433
Table 1: Experimental results of packet losses.
condition total
samples
lost
samples
loss
rate
(%)
One Way/Raw 180000 677 0.4
One Way/Abstract 870 0 0
Two Way/Raw 360000 149845 41.6
Two Way/Abstract 3267 4 0.1
0
50
100
150
200
250
300
350
One Way/Raw One Way/Abstract Two Way/Abstract
One Way/Raw
One Way/Abstract
Two Way/Abstract
Time
(msec)
Figure 6: End-to-end delay times (msec).
(over 30sec). Average delay times of the other con-
ditions are under 200 msec. Although more careful
investigations are required in various network condi-
tions, we believe that the result shows the feasibility
of the telecare services by using wristwatch-type de-
vices.
6 CONCLUSIONS
We have described a prototype of peer-to-peer tele-
care system by using wristwatch-type information de-
vices not using smartphones. Physiological informa-
tion of service user, such as electrocardiogram and
body movements, sensed by the wireless bio-sensor
are shared among the service users’ wristwatch-type
information devices in a peer-to-peer manner. To re-
alize intuitive communication between family mem-
bers, we have developed a user interface which visu-
ally shows the sensed user’s current body movement
and enables the others to tactilely feel his or her heart
beats. We believe that the system opens up a new de-
sign space in which all users in a social group can take
care of each others at the same time.
ACKNOWLEDGEMENTS
This work was supported in part by JSPS KAKENHI
Grant Number 26330125.
REFERENCES
Ford, B., Srisuresh, P., and Kegel, D. (2005). Peer-to-peer
communication across network address translators. In
Proceedings of the Annual Conference on USENIX
Annual Technical Conference, ATEC ’05, pages 13–
13, Berkeley, CA, USA. USENIX Association.
Haans, A. and IJsselsteijn, W. (2006). Mediated social
touch: a review of current research and future direc-
tions. Virtual Reality, 9(2-3):149–159.
Lin, C.-F. (2012). Mobile telemedicine: A survey study. J.
Med. Syst., 36(2):511–520.
Sashima, A., Inoue, Y., Ikeda, T., Yamashita, T., Ohta,
M., and Kurumatani, K. (2008). Toward mobile
healthcare services by using everyday mobile phones.
In Proceedings of the First International Conference
on Health Informatics, HEALTHINF 2008, Funchal,
Madeira, Portugal, January 28-31, 2008, Volume 1,
pages 242–245.
Sashima, A. and Kurumatani, K. (2016). Towards a peer-
to-peer communication model for mobile telecare ser-
vices. In Proceedings of the 9th International Joint
Conference on Biomedical Engineering Systems and
Technologies (BIOSTEC 2016) - Volume 5: HEALTH-
INF, Rome, Italy, February 21-23, 2016., pages 542–
549.
Triantafyllidis, A., Velardo, C., Salvi, D., Shah, S.,
Koutkias, V., and Tarassenko, L. (2015). A survey of
mobile phone sensing, self-reporting and social shar-
ing for pervasive healthcare. Biomedical and Health
Informatics, IEEE Journal of, PP(99):1–1.
HEALTHINF 2017 - 10th International Conference on Health Informatics
434