395 research outputs found
On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification
Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.This work was supported by the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks) and by the MINECO grant TIN2016-79095-C2-2-R (SMOG-DEV—Security mechanisms for fog computing: advanced security for devices)
A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments
The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Utilizing ECG Waveform Features as New Biometric Authentication Method
In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%
Central monitoring system for ambient assisted living
Smart homes for aged care enable the elderly to stay in their own homes longer. By means of various types of ambient and wearable sensors information is gathered on people living in smart homes for aged care. This information is then processed to determine the activities of daily living (ADL) and provide vital information to carers. Many examples of smart homes for aged care can be found in literature, however, little or no evidence can be found with respect to interoperability of various sensors and devices along with associated functions. One key element with respect to interoperability is the central monitoring system in a smart home. This thesis analyses and presents key functions and requirements of a central monitoring system. The outcomes of this thesis may benefit developers of smart homes for aged care
Cybersecurity in implantable medical devices
Mención Internacional en el título de doctorImplantable Medical Devices (IMDs) are electronic devices implanted within
the body to treat a medical condition, monitor the state or improve the
functioning of some body part, or just to provide the patient with a capability
that he did not possess before [86]. Current examples of IMDs
include pacemakers and defibrillators to monitor and treat cardiac conditions;
neurostimulators for deep brain stimulation in cases such as epilepsy
or Parkinson; drug delivery systems in the form of infusion pumps; and a
variety of biosensors to acquire and process different biosignals.
Some of the newest IMDs have started to incorporate numerous communication
and networking functions—usually known as “telemetry”—,
as well as increasingly more sophisticated computing capabilities. This
has provided implants with more intelligence and patients with more autonomy,
as medical personnel can access data and reconfigure the implant
remotely (i.e., without the patient being physically present in medical facilities).
Apart from a significant cost reduction, telemetry and computing
capabilities also allow healthcare providers to constantly monitor the patient’s
condition and to develop new diagnostic techniques based on an
Intra Body Network (IBN) of medical devices [25, 26, 201].
Evolving from a mere electromechanical IMD to one with more advanced
computing and communication capabilities has many benefits but
also entails numerous security and privacy risks for the patient. The majority
of such risks are relatively well known in classical computing scenarios,
though in many respects their repercussions are far more critical in the case
of implants. Attacks against an IMD can put at risk the safety of the patient
who carries it, with fatal consequences in certain cases. Causing an intentional
malfunction of an implant can lead to death and, as recognized by the
U.S. Food and Drug Administration (FDA), such deliberate attacks could
be far more difficult to detect than accidental ones [61]. Furthermore, these
devices store and transmit very sensitive medical information that requires
protection, as dictated by European (e.g., Directive 95/46/ECC) and U.S.
(e.g., CFR 164.312) Directives [94, 204].
The wireless communication capabilities present in many modern IMDs
are a major source of security risks, particularly while the patient is in open
(i.e., non-medical) environments. To begin with, the implant becomes no
longer “invisible”, as its presence could be remotely detected [48]. Furthermore,
it facilitates the access to transmitted data by eavesdroppers who
simply listen to the (insecure) channel [83]. This could result in a major privacy breach, as IMDs store sensitive information such as vital signals,
diagnosed conditions, therapies, and a variety of personal data (e.g., birth
date, name, and other medically relevant identifiers). A vulnerable communication
channel also makes it easier to attack the implant in ways similar
to those used against more common computing devices [118, 129, 156],
i.e., by forging, altering, or replying previously captured messages [82].
This could potentially allow an adversary to monitor and modify the implant
without necessarily being close to the victim [164]. In this regard,
the concerns of former U.S. vice-president Dick Cheney constitute an excellent
example: he had his Implantable Cardioverter Defibrillator (ICD)
replaced by another without WiFi capability [219].
While there are still no known real-world incidents, several attacks on
IMDs have been successfully demonstrated in the lab [83, 133, 143]. These
attacks have shown how an adversary can disable or reprogram therapies
on an ICD with wireless connectivity, and even inducing a shock state to
the patient [65]. Other attacks deplete the battery and render the device
inoperative [91], which often implies that the patient must undergo a surgical
procedure to have the IMD replaced. Moreover, in the case of cardiac
implants, they have a switch that can be turned off merely by applying a
magnetic field [149]. The existence of this mechanism is motivated by the
need to shield ICDs to electromagnetic fields, for instance when the patient
undergoes cardiac surgery using electrocautery devices [47]. However, this
could be easily exploited by an attacker, since activating such a primitive
mechanism does not require any kind of authentication.
In order to prevent attacks, it is imperative that the new generation of
IMDs will be equipped with strong mechanisms guaranteeing basic security
properties such as confidentiality, integrity, and availability. For example,
mutual authentication between the IMD and medical personnel is
essential, as both parties must be confident that the other end is who claims
to be. In the case of the IMD, only commands coming from authenticated
parties should be considered, while medical personnel should not trust any
message claiming to come from the IMD unless sufficient guarantees are
given.
Preserving the confidentiality of the information stored in and transmitted
by the IMD is another mandatory aspect. The device must implement
appropriate security policies that restrict what entities can reconfigure the
IMD or get access to the information stored in it, ensuring that only authorized
operations are executed. Similarly, security mechanisms have to
be implemented to protect the content of messages exchanged through an insecure wireless channel.
Integrity protection is equally important to ensure that information has
not been modified in transit. For example, if the information sent by the
implant to the Programmer is altered, the doctor might make a wrong decision.
Conversely, if a command sent to the implant is forged, modified,
or simply contains errors, its execution could result in a compromise of the
patient’s physical integrity.
Technical security mechanisms should be incorporated in the design
phase and complemented with appropriate legal and administrative measures.
Current legislation is rather permissive in this regard, allowing the
use of implants like ICDs that do not incorporate any security mechanisms.
Regulatory authorities like the FDA in the U.S or the EMA (European
Medicines Agency) in Europe should promote metrics and frameworks for
assessing the security of IMDs. These assessments should be mandatory
by law, requiring an adequate security level for an implant before approving
its use. Moreover, both the security measures supported on each IMD
and the security assessment results should be made public.
Prudent engineering practices well known in the safety and security domains
should be followed in the design of IMDs. If hardware errors are
detected, it often entails a replacement of the implant, with the associated
risks linked to a surgery. One of the main sources of failure when treating
or monitoring a patient is precisely malfunctions of the device itself.
These failures are known as “recalls” or “advisories”, and it is estimated
that they affect around 2.6% of patients carrying an implant. Furthermore,
the software running on the device should strictly support the functionalities
required to perform the medical and operational tasks for what it was
designed, and no more [66, 134, 213].
In Chapter 1, we present a survey of security and privacy issues in
IMDs, discuss the most relevant mechanisms proposed to address these
challenges, and analyze their suitability, advantages, and main drawbacks.
In Chapter 2, we show how the use of highly compressed electrocardiogram
(ECG) signals (only 24 coefficients of Hadamard Transform) is enough
to unequivocally identify individuals with a high performance (classification
accuracy of 97% and with identification system errors in the order of
10−2). In Chapter 3 we introduce a new Continuous Authentication scheme
that, contrarily to previous works in this area, considers ECG signals as
continuous data streams. The proposed ECG-based CA system is intended
for real-time applications and is able to offer an accuracy up to 96%, with
an almost perfect system performance (kappa statistic > 80%). In Chapter 4, we propose a distance bounding protocol to manage access control of
IMDs: ACIMD. ACIMD combines two features namely identity verification
(authentication) and proximity verification (distance checking). The
authentication mechanism we developed conforms to the ISO/IEC 9798-2
standard and is performed using the whole ECG signal of a device holder,
which is hardly replicable by a distant attacker. We evaluate the performance
of ACIMD using ECG signals of 199 individuals over 24 hours,
considering three adversary strategies. Results show that an accuracy of
87.07% in authentication can be achieved. Finally, in Chapter 5 we extract
some conclusions and summarize the published works (i.e., scientific
journals with high impact factor and prestigious international conferences).Los Dispositivos Médicos Implantables (DMIs) son dispositivos electrónicos
implantados dentro del cuerpo para tratar una enfermedad, controlar
el estado o mejorar el funcionamiento de alguna parte del cuerpo, o simplemente
para proporcionar al paciente una capacidad que no poseía antes
[86]. Ejemplos actuales de DMI incluyen marcapasos y desfibriladores
para monitorear y tratar afecciones cardíacas; neuroestimuladores para la
estimulación cerebral profunda en casos como la epilepsia o el Parkinson;
sistemas de administración de fármacos en forma de bombas de infusión; y
una variedad de biosensores para adquirir y procesar diferentes bioseñales.
Los DMIs más modernos han comenzado a incorporar numerosas funciones
de comunicación y redes (generalmente conocidas como telemetría)
así como capacidades de computación cada vez más sofisticadas. Esto
ha propiciado implantes con mayor inteligencia y pacientes con más autonomía,
ya que el personal médico puede acceder a los datos y reconfigurar
el implante de forma remota (es decir, sin que el paciente esté
físicamente presente en las instalaciones médicas). Aparte de una importante
reducción de costos, las capacidades de telemetría y cómputo también
permiten a los profesionales de la atención médica monitorear constantemente
la condición del paciente y desarrollar nuevas técnicas de diagnóstico
basadas en una Intra Body Network (IBN) de dispositivos médicos
[25, 26, 201].
Evolucionar desde un DMI electromecánico a uno con capacidades de
cómputo y de comunicación más avanzadas tiene muchos beneficios pero
también conlleva numerosos riesgos de seguridad y privacidad para el paciente.
La mayoría de estos riesgos son relativamente bien conocidos en los
escenarios clásicos de comunicaciones entre dispositivos, aunque en muchos
aspectos sus repercusiones son mucho más críticas en el caso de los
implantes. Los ataques contra un DMI pueden poner en riesgo la seguridad
del paciente que lo porta, con consecuencias fatales en ciertos casos.
Causar un mal funcionamiento intencionado en un implante puede causar
la muerte y, tal como lo reconoce la Food and Drug Administration (FDA)
de EE.UU, tales ataques deliberados podrían ser mucho más difíciles de
detectar que los ataques accidentales [61]. Además, estos dispositivos almacenan
y transmiten información médica muy delicada que requiere se
protegida, según lo dictado por las directivas europeas (por ejemplo, la Directiva 95/46/ECC) y estadunidenses (por ejemplo, la Directiva CFR
164.312) [94, 204].
Si bien todavía no se conocen incidentes reales, se han demostrado con
éxito varios ataques contra DMIs en el laboratorio [83, 133, 143]. Estos
ataques han demostrado cómo un adversario puede desactivar o reprogramar
terapias en un marcapasos con conectividad inalámbrica e incluso
inducir un estado de shock al paciente [65]. Otros ataques agotan
la batería y dejan al dispositivo inoperativo [91], lo que a menudo implica
que el paciente deba someterse a un procedimiento quirúrgico para reemplazar
la batería del DMI. Además, en el caso de los implantes cardíacos,
tienen un interruptor cuya posición de desconexión se consigue simplemente
aplicando un campo magnético intenso [149]. La existencia de este
mecanismo está motivada por la necesidad de proteger a los DMIs frete
a posibles campos electromagnéticos, por ejemplo, cuando el paciente se
somete a una cirugía cardíaca usando dispositivos de electrocauterización
[47]. Sin embargo, esto podría ser explotado fácilmente por un atacante,
ya que la activación de dicho mecanismo primitivo no requiere ningún tipo
de autenticación.
Garantizar la confidencialidad de la información almacenada y transmitida
por el DMI es otro aspecto obligatorio. El dispositivo debe implementar
políticas de seguridad apropiadas que restrinjan qué entidades
pueden reconfigurar el DMI o acceder a la información almacenada en él,
asegurando que sólo se ejecuten las operaciones autorizadas. De la misma
manera, mecanismos de seguridad deben ser implementados para proteger
el contenido de los mensajes intercambiados a través de un canal inalámbrico
no seguro.
La protección de la integridad es igualmente importante para garantizar
que la información no se haya modificado durante el tránsito. Por ejemplo,
si la información enviada por el implante al programador se altera, el
médico podría tomar una decisión equivocada. Por el contrario, si un comando
enviado al implante se falsifica, modifica o simplemente contiene
errores, su ejecución podría comprometer la integridad física del paciente.
Los mecanismos de seguridad deberían incorporarse en la fase de diseño
y complementarse con medidas legales y administrativas apropiadas.
La legislación actual es bastante permisiva a este respecto, lo que permite
el uso de implantes como marcapasos que no incorporen ningún mecanismo
de seguridad. Las autoridades reguladoras como la FDA en los Estados
Unidos o la EMA (Agencia Europea de Medicamentos) en Europa deberían
promover métricas y marcos para evaluar la seguridad de los DMIs.
Estas evaluaciones deberían ser obligatorias por ley, requiriendo un nivel
de seguridad adecuado para un implante antes de aprobar su uso. Además,
tanto las medidas de seguridad implementadas en cada DMI como los resultados
de la evaluación de su seguridad deberían hacerse públicos.
Buenas prácticas de ingeniería en los dominios de la protección y la
seguridad deberían seguirse en el diseño de los DMIs. Si se detectan errores
de hardware, a menudo esto implica un reemplazo del implante, con
los riesgos asociados y vinculados a una cirugía. Una de las principales
fuentes de fallo al tratar o monitorear a un paciente es precisamente el
mal funcionamiento del dispositivo. Estos fallos se conocen como “retiradas”,
y se estima que afectan a aproximadamente el 2,6 % de los pacientes
que llevan un implante. Además, el software que se ejecuta en el
dispositivo debe soportar estrictamente las funcionalidades requeridas para
realizar las tareas médicas y operativas para las que fue diseñado, y no más
[66, 134, 213].
En el Capítulo 1, presentamos un estado de la cuestión sobre cuestiones
de seguridad y privacidad en DMIs, discutimos los mecanismos más relevantes
propuestos para abordar estos desafíos y analizamos su idoneidad,
ventajas y principales inconvenientes. En el Capítulo 2, mostramos
cómo el uso de señales electrocardiográficas (ECGs) altamente comprimidas
(sólo 24 coeficientes de la Transformada Hadamard) es suficiente para
identificar inequívocamente individuos con un alto rendimiento (precisión
de clasificación del 97% y errores del sistema de identificación del orden
de 10−2). En el Capítulo 3 presentamos un nuevo esquema de Autenticación
Continua (AC) que, contrariamente a los trabajos previos en esta
área, considera las señales ECG como flujos de datos continuos. El sistema
propuesto de AC basado en señales cardíacas está diseñado para aplicaciones
en tiempo real y puede ofrecer una precisión de hasta el 96%,
con un rendimiento del sistema casi perfecto (estadístico kappa > 80 %).
En el Capítulo 4, proponemos un protocolo de verificación de la distancia
para gestionar el control de acceso al DMI: ACIMD. ACIMD combina
dos características, verificación de identidad (autenticación) y verificación
de la proximidad (comprobación de la distancia). El mecanismo de autenticación
es compatible con el estándar ISO/IEC 9798-2 y se realiza utilizando
la señal ECG con todas sus ondas, lo cual es difícilmente replicable
por un atacante que se encuentre distante. Hemos evaluado el rendimiento
de ACIMD usando señales ECG de 199 individuos durante 24 horas, y
hemos considerando tres estrategias posibles para el adversario. Los resultados
muestran que se puede lograr una precisión del 87.07% en la au tenticación. Finalmente, en el Capítulo 5 extraemos algunas conclusiones
y resumimos los trabajos publicados (es decir, revistas científicas con alto
factor de impacto y conferencias internacionales prestigiosas).Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Arturo Ribagorda Garnacho.- Secretario: Jorge Blasco Alís.- Vocal: Jesús García López de Lacall
A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors
Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD.1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD.1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication
Tele-cardiology sensor networks for remote ECG monitoring
One of today’s most pressing matters in medical care is the response time to patients in need. The scope of this thesis is to suggest a solution that would help reduce response time in emergency situations utilizing wireless sensor networks technology. Wireless sensor network researches have recently gained unprecedented momentum in both industries and academia, especially its potential applications in Emergency Medical Services and Intensive Care Units. The enhanced power efficiency, minimized production cost, condensed physical layout, as well as reduced wired connections, presents a much more proficient and simplified approach to the continuous monitoring of patients’ physiological status. This thesis focuses on the areas of remote ECG feature extraction utilizing wavelet transformation concepts and sensor networks technology. The proposed sensor network system provides the following contributions. The low-cost, low-power wearable platforms are to be distributed to patients of concern and will provide continuous ECG monitoring by measuring electrical potentials between various points of the body using a galvanometer. The system is enabled with integrated RF communication capability that will relay the signals wirelessly to a workstation monitor. The workstation is equipped with ECG signal processing software that performs ECG characteristic extractions via wavelet transformation. Lastly, a low-complex, end-to-end security scheme is also incorporated into this system to ensure patient privacy. Other notable features include location tracking algorithms for patient tracking, and MATLAB Server environment for internal communication
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