An Ethernet motion-sensor based alarm system for epilepsy

IRBM 32 (2011) 155–157
Original article
An Ethernet motion-sensor based alarm system for epilepsy monitoring
EPIMOUV : système de détection de crises nocturnes d’épilepsie par capture de mouvement
S. Bonnet a,∗ , P. Jallon a , A. Bourgerette a , M. Antonakios a , R. Guillemaud a , Y. Caritu b , G. Becq c ,
P. Kahane c , P. Chapat d , B. Thomas-Vialettes e , F. Thomas-Vialettes e , D. Gerbi f , D. Ejnes f
a
CEA LETI, MINATEC CAMPUS, DTBS, 38054 Grenoble, France
b MOVEA SA, 7 Parvis Louis Néel, 38000 Grenoble, France
c CHU GRENOBLE, LNPPE, 38043 Grenoble, France
d CTR Medical, 38500 Voiron, France
e Association EPI, 69290 Craponnes, France
f FCEs, 38590 Saint-Étienne de Saint-Geoirs, France
Available online 25 February 2011
Abstract
The EPIMOUV project aims at providing a place to live and autonomy for drug-resistant epileptic young adults while attempting to provide
them a high level of security. In this framework, subjects will be monitored during night using body-worn motion sensors and an alarm will be
triggered if an on-going seizure is detected by processing the accelerometer readings. In this paper, we describe an innovative architecture for such
alarm system: the wireless inertial sensors are positioned on the subject’s body; the RF transceiver is located in the subject’s room and linked to an
Ethernet gateway, and the remote motion capture acquisition system is interfaced with a medicalized alarm system. The EPIMOUV system will
be evaluated in a specialized institution from December 2010.
© 2011 Elsevier Masson SAS. All rights reserved.
Keywords: Accelerometer; Alarm System; Detection; Epilepsy; Ethernet
Résumé
L’objectif global du projet EPIMOUV est d’augmenter l’autonomie des personnes souffrant d’une épilepsie pharmaco-résistante tout en préservant
leur sécurité. Pour cela, le projet vise à développer un système d’alarme en cas de crise épileptique nocturne par analyse du mouvement. Dans ce
papier, nous décrivons une architecture innovante de système d’alarme médicalisé : les accéléromètres sans fil sont placés sur le corps ; le récepteur
RF est situé dans la chambre du résident et relié au réseau par connexion Ethernet ; et le système déporté d’acquisition de capture de mouvement
et de détection de crises est interfacé à un système professionnel d’alarme médicalisé. Le système EPIMOUV sera évalué en institution spécialisé
à partir de décembre 2010.
© 2011 Elsevier Masson SAS. Tous droits réservés.
Mots clés : Accéléromètre ; Alarme ; Détection ; Épilepsie ; Ethernet
1. Introduction
Nowadays, body-mounted inertial/magnetic sensors are
increasingly used in biomedical applications. Their main advantages are miniaturization, autonomy, low intrusiveness and
unrestricted application range. In environment-controlled laboratory setups, they have shown to be reliable for upper-limb
(kinematic analysis) and lower-limb (gait analysis), and they do
∗
Corresponding author.
E-mail address: [email protected] (S. Bonnet).
1959-0318/$ – see front matter © 2011 Elsevier Masson SAS. All rights reserved.
doi:10.1016/j.irbm.2011.01.021
fare well against optical tracking devices. In specialized institutions, there is definitely a need for monitoring the patient in his
room and for collecting data remotely at a network scale either
for offline analysis, e.g. sleep monitoring or online movement
detection. This issue is especially relevant in the EPIMOUV
project, dedicated to the monitoring of patients with pharmacoresistant epilepsy.
2. Materials and methods
The proposed Ethernet-based solution is build upon watchsized MotionPod® technology from Movea. MotionPod® is a
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S. Bonnet et al. / IRBM 32 (2011) 155–157
Fig. 1. MotionPod 3-axis accelerometer and 3-axis magnetometer (3a3m) sensors + MotionPod Ethernet Controller (81 × 56 × 40 mm).
patented solution of motion sensing technology for accurate
human body orientation or motion measurement. This is done by
using miniaturized high-tech motion sensing MEMS containing
3-axis accelerometers (3a) and 3-axis magnetometers (3m) in a
space the size of a standard wristwatch. Each MotionPod is timesynchronized by a controller and a proprietary TDMA protocol
is used for data transmission. In each room, two wireless 3-D
accelerometers, located on the subject’s torso and wrist, communicate with a dedicated RF receiver, connected to a server via
Ethernet. The MotionPod Ethernet Controller has been developed and validated during the project and is shown in Fig. 1.
The main application, named EPILOG, is driven from a
remote PC for configuration, data acquisition and online processing. The supervision is limited to five rooms. The seizure
detection algorithm is interfaced with a traditional nurse-call
system: when an abnormal movement is detected by the software, a message is sent to the nurse’s pager, in order to give
assistance to the resident in due time. The EPILOG software
is coupled to the CTR Medical alarm system via an ASCOM
Fig. 2. Nocturnal epilepsy monitoring system in specialized institution. Rooms
under supervision are located in Building B while the acquisition/detection EPILOG system is located in Building A. Ethernet protocol is used to transmit data
between both buildings. Wireless motion sensors and RF receiver are located in
each resident’s room.
platform and relays. Furthermore, for validation purposes, residents’ rooms are equipped with two wireless buttons for the staff
to acknowledge the occurrence of a seizure or to indicate a false
alarm. The components of the nocturnal seizure alarm system
are described in Fig. 2.
Different systems have been proposed in the literature for
seizure detection. The most widespread ones are based on undermattress sensors but lack of specificity. Accelerometer is a better
candidate for this task since tremor and abnormal limb orientation can easily be identified. The seizure detection algorithm has
been patented and described in [1,2]. It is based on a Bayesian
modelling of patient-specific seizures and also normal nocturnal
movements using hidden Markov models. The sensor located at
torso is mainly intended to detect a standing posture in order
to inhibit the detection and prevent false alarms. The CHU
Grenoble LNPPE, has also contributed to this task by a fine
characterization of motor epileptic seizures using epilepsy unit
Fig. 3. Example of a 3-axis accelerometer waveforms during a seizure. The sensor is positioned on the left wrist. This is a focal seizure evolving to a generalized
convulsive one. Evolution of the waveforms can be observed during the succession of events: t0 - start of electroencephalographic seizure; t1 - start of clinical symptoms,
t2: end of seizure; ATF - artefacts generated by caregivers; TONIC - Tonic manifestations; TONICO CLONIC (CONVULSIVE) - convulsive manifestations.
S. Bonnet et al. / IRBM 32 (2011) 155–157
observations [3,4]. This work was achieved by first collecting
synchronized video-EEG and motion data, and then annotating
in a supervised manner the temporal distribution of seizures, as
demonstrated in Fig. 3.
157
of the approach and the proposed system is now deployed in
an epilepsy-specialized institution and validation is currently
underway.
Conflict of interest statement
3. Results
In EPIMOUV project, the seizure detection algorithm has
been first evaluated in home environment with the help of
EPI association. Good performances have been reported on
two epileptic persons, where 46 seizures were acquired during 39 nights. 90% of seizures have been correctly detected
with an average number of false alarms per night of 0.7 [5].
Also the system was found acceptable by the epileptic young
adults.
Data acquisition is being performed from June 2010 to
June 2011 in a FCEs medical institution according to a protocol approved by a local Ethical Committee. This institution
is located in Saint-Étienne de Saint-Geoirs, Isère, France and it
has been inaugurated in November 30, 2009 during the course
of the project.
Today, the whole acquisition processing chain has been
validated from the resident to the caregiver and the medical
validation will start from December 2010 to June 2011. Furthermore, a validation protocol has been defined with the Healthcare
professionals who will test and validate the seizure detection
system at night. The sensitivity and sensibility of the proposed
solution will then be assessed in real conditions.
4. Conclusion
This paper has presented an innovative remote motioncapture acquisition system. The system is based on wireless
motion sensors and an Ethernet-based RF receiver. Data are then
centralized into a server where dedicated processing can be efficiently performed in a multi-threading way, here for epilepsy
detection. This kind of system is new and can meet other biomedical applications in hospital or nursing homes. Experiments in
the first phase of the project have validated the proof-of-concept
S. B., employed by CEA-Léti.
P. J., employed by CEA-Léti.
A. B., employed by CEA-Léti.
R. G., employed by CEA-Léti.
Y. C., co-funder of MOVEA.
G. B., post-doctorant in CHU Grenoble.
P. K., employed by CHU Grenoble.
P. C., CEO of CTR Medical, network installer for FCEs
institution in Saint-Étienne de Saint-Geoirs.
B. T-V., no conflict of interest.
F. T-V., no conflict of interest.
D. G., employed by FCEs.
Acknowledgments
This research project was financially supported by the
National Research Agency (ANR) through the call for proposals
TecSan.
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