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Fachbereich Informatik und Elektrotechnik

Ambient Intelligence, Helmut Dispert

AmI

Ambient Intelligence



BAN – BSN - WBSN

Ambient Intelligence• Body Area Network - BAN• Body Sensor Network – BSN• Wireless Body Sensor Network - WBSN

Wireless body-area sensor networks (WBSNs) are key components of e-health solutions. Wearable wireless sensors can monitor and collect many different physiological parameters accurately, economically and efficiently.



BAN – BSN - WBSN

Body sensingTo address these issues, the concept of Body Sensor Networks (BSN) was first proposed in 2002 by Prof. Guang-Zhong Yang from Imperial College London.

The aim of the BSN is to provide a truly personalised monitoringplatform that is pervasive, intelligent, and invisible to the user.

Example BSN system:represents a patient wearing a number of sensors on his body, each of which consists of a sensor connected to a small processor, wireless transmitter, and battery pack, forming a BSN node.

The BSN node captures the sensor data, processes the data and then wirelessly transmits the information to a local processing unit, shown as a personal digital assistant (PDA) in the diagram. All this has been made possible by rapid advances in computing technology.



Literature

Yang, Guang-Zhong (Ed.):Body Sensor Networks,Springer, 2006ISBN: 978-1-84628-272-0



BSN



BSN

Ref.: www.golem.de

Wireless Body Sensor Network

Fraunhofer Institute

?

http://www.golem.de



BSN

A 'typical' architecture for a Body Sensor Network System



Braunschweig BSN

Ref.: Technical University of Braunschweig Body Sensor Network



BAN – BSN - WBSN

Body Area Networks – Average power consumption, sustained data rate

1000 mW500 mW100 mW50 mW10 mW

1 Gbit/s

100 kbit/s

1 Mbit/s

10 Mbit/s

100 Mbit/s

1 kbit/s

10 kbit/s

Wireless USB

IEEE 802.11 a/b/g

Bluetooth

ZigBee

200 mW20 mW

Body Area Network

5 mW2 mW

Ref.: Stefan Drude, Philips



BAN – BSN - WBSN



Ubiquitous Health Monitoring

WBAN: Motivation• Goal: ubiquitous and affordable healthcare• Conditions: demographic and technology trends• Solution: 3-tier ubiquitous monitoring system

– Tier 1: Wireless Body Area Network (WBAN)– Tier 2: Personal Server– Tier 3: Healthcare Provider Servers

• Opportunities: – Ambulatory health monitoring– Computer-assisted rehabilitation– Augmented reality systems

• Long-term benefits:– Promote healthy lifestyle– Seamless integration of data into personal medical records and

research databases – Knowledge discovery through data mining

Ref.: Emil Jovanov, Chris Ott, Aleksandar Milenkovic, Wireless Body Area Network for Health Monitoring



BAN – BSN - WBSN

BAN Requirements - Draft

• Distance 2 m std, 5 m special

• Piconet density 2 - 4 nets / m2

• Devices per network max. 100

• Net network throughput 100 Mbit/s max.

• Power consumption ~ 1mW / Mbps(@ 1 m distance)

• Startup time < 100 us, or< 10% of TX slot

• Latency (end to end) 10 ms

• Network setup time < 1 sec(after initial setup, per device)

Ref.: Stefan Drude, Philips



IEEE 802.15

IEEE 802.15 WPAN™Task Group 6 (TG6)Body Area Networks

The IEEE 802.15 Task Group 6 (BAN) is developing a communication standard optimized for low power devices and operation on, in or around the human body (but not limited to humans) to serve a variety of applications including medical, consumer electronics / personal entertainment and other.

IEEE 802.15 definition:"a communication standard optimized for low power devices and operation on, in or around the human body (but not limited to humans) to serve a variety of applications including medical, consumer electronics / personal entertainment and other"



BAN – BSN - WBSN

Applications in Healthcare

BANs have grown as a refinement of BSN. As such, BSN remain the most thought out applications of BAN.

BSN devices refine the general requirements by restricting themselves to a much smaller range (< 0.01 - 2.00 m). This limited range allows developers to take advantage of several aspects of the human body.

First, the human body itself can become a channel for short range communication, thus removing the need for a traditional antenna.By removing the requirement of an additional antenna, the power consumption of BSN devices shrinks to 0.1 - 1.0 mW.

At this low power, the human body is actually capable of generating enough excess energy that the devices could "scavenge" the required energy directly from the host's body, removing the restriction on traditional power sources (like batteries).



BAN – BSN - WBSN

Managed Body Sensor Networks - MBSN A managed body sensor network is defined as a system in which the third party makes decisions based on the data collected from one or many BSN.

Background:increasing demand of resources placed on the medical community, the rising costs of in-patient care, and the relative lack of out-patient monitoring.

Examples:

• MobiHealth (2003, Telemedicine Group at the University of Twente) and• CodeBlue (Harvard University), two managed BSN that are approaching

development of managed BSN from two different perspectives.

MobiHealth approach: Continuous monitoring of vital constants for mobile users (Remote monitoring and treatment services).

"Extra-BAN communication" (EBAN):Communication between a BAN and another network.



BAN – BSN - WBSN

Managed Body Sensor Networks - MBSN

MobiHealth:BSN with EBAN connectivity to a 2.5/3G networks to provide out-patient monitoring of patients vital signs. Through this infrastructure the MobiHealth designers were able to provide sensor information to qualified medical professionals, where multiple patients data could be monitored in an aggregate form.

Harvard University's Code Blue:represents another example of BSN currently in the trial stages. Like MobiHealth, CodeBlue provides an infrastructure for multiple patient monitoring through EBAN communication. However, CodeBlue takes a more middleware approach to BSN instead of the packaged solution thatMobiHealth provides. By providing a middleware layer, the CodeBlue project allows developers to specify the modules to use. In this way, CodeBlue is rather flexible at runtime. Two examples given by the MobiHealth team are emergency response and monitoring limb movement in stroke patient rehabilitation. Both scenarios have very different requirements both from a sensor perspective, and a timeliness perspective however the platform is able scale to accommodate both accordingly.



BAN – BSN - WBSN

Autonomous Body Sensor Networks - ABSN

Autonomous body sensor networks (ABSN) and MBSN share the same goals, but they accomplish them in different ways.

While a MBSN relies on reading sensor information and deliveringit to a third party for decision making and intervention, ABSN take a more proactive approach. ABSN introduce actuators in addition tothe sensors to allow the BSN to effect change on the users body.

In addition to the actuators, ABSN contain more intelligent sensors that contain enough intelligence to complete their own tasks independently.

Human++ is a project developed in Belgium that aims to bring ABSN to the mainstream. The design of Human++ is relatively simple, any node in the mesh-network are able to talk to any other node in the network. There is a predefined "central" node that is designated for all EBAN communication. The central node also publishes information on any services that the ABSN provides external access to.



BAN – BSN - WBSN

Example ABSN Diagram



BAN – BSN - WBSN

Ref.: Val Jones, Valerie Gay, Peter Leijdekkers:Body sensor networks for Mobile Health Monitoring: experience in Europe and Australia

Health BANA health BAN is defined as a network of communicating devices worn on, around or in the body which provides mobile health services to the user.

A BAN consists of a Mobile Base Unit (MBU) and a set of BAN devices (e.g. sensors, actuators or other ‘wearable devices’).

The MBU acts as a processing platform and communications gateway and is currently realised as a software application running on a handheld device. BAN data may be processed locally within the BAN and/or remotely, the latter implying transmission of data to a remote location. Front-end supported sensors are powered by a sensor front end which also digitizes and filters the raw analogue signal before transmitting the data over a wireless link to the MBU.

In general, a health BAN may act as a standalone device providing personal local services to the patient. At the other end of the spectrum, all BAN data may be transmitted onward for processing, or a combination of local and remote processing may be used. If a remote user (human or software) is involved and extraBAN communication occurs then the m-health service can be regarded as a telemedicine service.



BAN – BSN - WBSN


Generic architecture for m-health



BAN – BSN - WBSN

Several variants of the system were developed during a number ofprojects, e.g.

MYOTEL BAN

Europe System: IST ProgrammeProject Number: IST-2001-36006“Information Society Technologies”MobiHealth System

Shaping The FutureOf Healthcare



BAN – BSN - WBSN


Myotel teletreatment BAN



BAN – BSN - WBSN


Personal Health Monitor



IEEE 802.15.6

BAN – BSN - WBSN



Interest Group on BAN in 802.15Conclusions on low data rate applications

• Operates on, inside, or in the vicinity of the body

• Limited range (< .01 – 2 meters)

• The channel model will include human body effects. (absorption, health effects)

• Extremely low consumption power (.1 to 1 mW) for each device

• Capable of energy scavenging / battery-less operation

• Support scalable Data Rate: 0.01 – 1,000 kbps (optional 10 Mbps)

IEEE 802.15.6



• Support different classes of QoS for high reliability, asymmetric traffic, power constrained

• Needs optimized, low complexity MAC and Networking layer

• High number of simultaneously operating piconetsrequired

• Application specific, security/privacy required

• Small form factor for the whole radio, antenna, power supply system

• Locating radios (” find me”) mode

Interest Group on BAN in 802.15Conclusions on low data rate applications

IEEE 802.15.6



BAN – BSN - WBSN

Sensor nodes:

• e-AR (ear-worn Activity Recognition) sensor (for capturing the posture, gait and activity of the patient)

• Wound healing sensor(for monitoring the progress of healing for burn patients)

• Blood pressure sensor(for measuring the blood pressure for patients with hypertension)

• EMG (electromyography) sensor(for capturing the muscle activities of orthopaedic patients)

• Blood glucose sensor(for measuring the glucose level of diabetic patients)

• Heart rate sensor(for detecting abnormal heart conditions of cardiac patients).




Tier 1: WBAN

Tier 2: Personal Server

Tier 3: Medical Servers







ActiS: Activity Sensor

ADXL202Accelerometer

ADXL202Accelerometer

ECG SignalConditioning

TI MSP430F1232

CC2420(ZigBee)

Flash

USB Interface

TI MSP430F149

TelosISPM

ECG electrodes

ActiS as Motion Sensor



Historical Overview

WHRM

MOGUL

Signal Conditioning RS232 Interface

MicrocontrollerFinger Probe Programmable Logic

Basic WISE (Y00)

Distributed Stress Monitoring (Y03)

Reconfigurable SpO2 Sensor (Y04)



BAN - Fraunhofer

BAN – BSN - WBSN



BAN - Fraunhofer

Scenario No. 1: Clinical use and mobile application• Patient is equipped with sensors• Central unit transmits data• Stationary base station distributes data• Data called from PC



BAN - Fraunhofer

Scenario No. 2: Internet-based homecare application• Patient stays at home• Sensors are carried under the cloths• Data first transmitted to home network• Data transmitted via Internet• Medical doctor can analyse data via PC, mobile phone, etc.



BAN - Fraunhofer

Typical Implementation: WBAN with sensors (BSU)

• BSU:Body Sensor Unit

• BCU:Body Central Unit

• NAU:Network Access Unit

BSU:• Each BSU fulfills a dedicated task• Communicates with BCU via built-in RF-modules

BCU:• Attached to body• Forms central interface to all sensor nodes• Communicates to external stationary base-station (NAU)



BAN - Fraunhofer

NAU - Network Access Unit• Connection to Internet (Ethernet)• Integrated into standard network infrastructure• Data analysis using PC, mobile phone, PDA,

work station, etc.• Doctor or carer can access data from anywhere



BAN - Fraunhofer

BCU – Body Central Unit:

• Built up using same components as BSU• No sensors• BAN Master: RF-Module to communicate with the BSUs • Additional RF-module for external communication (Access Com)• BAN Master and BAN Slave form the BAN Com



Sensors

Sensors

in Body Sensor Networks



VeryLow

Uses a silicon integrated circuit to detect the temperature changes bymeasuring the resistance

Temperature

LowUses two electrodes, cathode and anode covered by a thin membrane tomeasure the oxygen dissolved in a liquid

Respiration

LowMeasures ratio of changing absorbance of the red and infrared light passingfrom one side to the other of a thin part of the body’s anatomy

Pulse oximetry

VeryLow

Measures the conductivity changes of humidity levelHumidity

HighMeasures the orientation, based on the principles of angular momentumGyroscope

HighMeasures potential difference across electrodes put on corresponding parts ofthe body

ECG/EEG/EMG

LowUses the infrared light and measures the absorption of the gas presentedCarbon dioxide

LowTraditionally analyzes drops of blood from a finger tip, recently, uses non-invasive method including a near infrared spectroscopy, ultrasound, opticalmeasurement at the eye, and the use of breath analysis

Blood sugar

LowMeasures the systolic (peak) pressure and diastolic (minimum) pressureBlood pressure

HighMeasures the acceleration relative to freefall in three axesAcceleration

Data

Rate

Operating methodType of

measurement

Sensors

Charalampos Liolios, Charalampos Doukas, Ilias Maglogiannis, George Fourlas:An Overview of Body Sensor Networks in Enabling Perva-sive Healthcare and Assistive Environments



Sensors


Interconnection of BSN, WBAN, WLAN, and WMAN.



Sensors

800 MHz, 1900 MHz, 2100Mhz m - km 14.4 Mbps/ 5.8 Mbps Cellular 3G

2.4 GHz ISM Local area 1 Mbps ANT

131 KHz 30 9.6 Kbps RuBee (IEEE 1902.1)

860 – 960 MHz 1 – 100 m 10 – 100 Kbps RFID (ISO/IEC 18000-6)

131.65 KHz (powerline) 902 –924 MHz

Home area 13 Kbps Insteon

IR (0.90 micro-meter)2 m 4Mbps (IrDA-1.1) IrDA

900 MHz ISM 30 m 9.6 Kbps Z-Wave

2.4 GHz ISM, 868 MHz,915MHz ISM

100 m - 300 m 250 kbps, 20 kbps, 40 kbps

IEEE 802.15.4 (low data rate wireless personal area network), Zigbee

2.4GHz ISM 1 m - 50 m 11-55 Mbps IEEE 802.15.3 (high data rate wireless personal area network)

3.1 – 10.6 GHz < 10 m 480 Mbps UWB (ECMA-368)

1880-1900 MHz 100 m 32 kbps DECT

2.4GHz ISM 50 m 1.6 Mbps (10 ?bps for Ver.2)

HomeRF (Shared Wireless Ac-cess Protocol, SWAP)

5 GHz 150 m 54 Mbps HiperLAN2

2.4 GHz ISM 10 m 3 – 24 Mbps Bluetooth 3.0 + High Speed

2.4 GHz ISM 10 m 1 Mbps Bluetooth Low Energy

2.4 GHz ISM 10 m - 150 m 721 Kbps Bluetooth (IEEE 802.15.1)

2.4 GHz ISM 150 m 11 Mbps IEEE 802.11b

5 GHz150 m 54 Mbps IEEE 802.11a

Frequency bandCoverage areaData rateTechnology

Most commonly acknowledged technologies for BSNs and WBANs




Electrocardiogram

Ref.: S. C. Jocke, J. F. Bolus1, S. N. Wooters, A. D. Jurik, A. C. Weaver, T. N. Blalock, and B. H. Calhoun:A 2.6- W Sub-threshold Mixed-signal ECG SoC, 2009, University of Virginia

ECG mixed-signal SoC block diagram

0.13- m CMOS mixed-signal system-on-chip (SoC) that acquires and processes an electrocardiogram (ECG) signal for wireless ECG monitoring.

The SoC uses a sub-threshold digital microcontroller ( C) for adaptive control of the sub-VT biased analog components and for processing the ECG data. The SoC consumes only 2.6 W while providing either heart rate or ECG data.

The SoC can reduce the wireless data rate by 500x by computing the heart rate intervals from the ECG signal.



Accelerometers

Ref.: Analog Devices

Accelerometer Basics:Linear acceleration causes mass m to move, varying capacitance C, which is changed into a voltage by the sensor electronics. This voltage is then digitized and used by the system processor.

m

C

k

acceleration



Accelerometers

2

2

t

x

t

va

∂∂

=∂∂

=

Acceleration Fundamentals

Definition:the time rate of change of velocity:

Units:Acceleration is measured in:

What is a “g”?A “g” is a unit of acceleration equal to Earth’s gravity at sea level or 9.81 m/s2

2s

m



Accelerometers

Material resistivity changes in presence of magnetic field

Magnetoresistive

Location of heated mass tracked during acceleration by sensing temperature

Heat Transfer

Motion converted to electrical signal by sensing of changing magnetic fields

Hall Effect

Beam or micromachined feature whose resistance changes with acceleration

Piezoresistive

Piezoelectric crystal mounted to mass –voltage output converted to acceleration

Piezoelectric

Metal beam or micromachined feature produces capacitance; change in capacitance related to acceleration

Capacitive

Key TechnologiesSensor Category

Common Types of Accelerometers

Ref.: Texas Instruments



Accelerometers

Analog Devices ADXL330Small, Low Power, 3-Axis ±3 g i MEMS Accelerometer

GENERAL DESCRIPTION:

The ADXL330 is a small,thin, low power, complete3-axis accelerometer withsignal conditioned voltage outputs, all on a single monolithic IC. The product measures acceleration with a minimum full-scale range of ±3 g. It can measure the static acceleration of gravity in tilt-sensing applications, as well as dynamic acceleration resulting from motion, shock, or vibration.




Accelerometers


Axes of acceleration sensitivity, corresponding output voltage increases when accelerated along the sensitive axis

Output Response vs. Orientation to Gravity



Accelerometers

Analog DevicesADXL330

Package X-Ray

Decapsulated Chip

Ref.: Chipworks Inc.

MEMS sensor fabricated as a single chip (iMEMS technology).

MEMS structure (three layers of polysilicon) in the center, ASIC circuitry (BiCMOS) around the outside edge.



Accelerometers

Ref.: Chipworks Inc.

MEMS Structure

Analog Devices ADXL330

Analog Devices decided to abandon the integrated iMEMS technology, and adopt the more common strategy of using a separate MEMS die and ASIC die wire bonded together in a single package.Example: ADXL345Advantage: Easy implementation of functionalities, including SPI and I2C digital outputs

SeparateASIC die and MEMS die

ADXL345

Moving Plate



Accelerometers

Analog Devices ADXL330 Applications

Ref.: www.electronic-data.com

http://www.electronic-data.com



Gyroscopes, Gyrometers

DescriptionGyroscopes are used to measure angular rate—how quickly an object turns. The rotation is typically measured in reference to one of three axes: yaw, pitch, or roll.

Gyro axes of rotational sensitivity.Depending on how a gyro normally sits, its primary axis of sensitivity can be one of the three axes of motion: yaw, pitch, or roll. The ADXRS150 and ADXRS300 are yaw-axis gyros, but they can measure rotation about other axes by appropriate mounting orientation.For example, at the right: a yaw-axis device is positioned to measure roll.

Analog Devices:ADXRS150, ADXRS300




Gyroscopes


Coriolis AccelerationAnalog Devices’ ADXRS gyros measure angular rate by means of Coriolis acceleration (after the French mathematician Gaspard G. de Coriolis, 1792-1843).

The ADXRS gyros take advantage of the Coriolis effect by using a resonating mass analogous to a person moving out and in on a rotating platform. The mass is micromachined from polysilicon and is tethered to a polysilicon frame so that it can resonate only along one direction.



Gyroscopes


Schematic of the gyro’smechanical structure.

The frame and resonating mass are displaced laterally in response to the Coriolis effect. The displacement is determined from the change in capacitance between the Coriolis sense fingers on the frame and those attached to the substrate.



Gyroscopes


Operation:

The ADXRS450 operates on the principle of a resonator gyro-scope.

Each sensing structure contains a dither frame that is electrostatically driven to resonance. This produces the necessary velocity element to produce a Coriolis force when experiencing angular rate.

Simplified Gyroscope Sensing Structure(simplified version of one of four polysilicon sensing structures)



Gyroscopes


Photograph of ADXRS gyro die, highlighting the integration of the mechanical rate sensor and the signal conditioning electronics.

Photograph of mechanical sensor. The ADXRS gyros include two structures to enable differential sensing in order to reject environmental shock and vibration.



Gyroscopes


ADXRS450 - High Performance, Digital Output Gyroscope

The ADXRS450 is an angular rate sensor (gyroscope) intended for industrial, medical, instrumentation, stabilization, and other high performance applications. An advanced, differential, quad sensor design rejects the influence of linear acceleration, enabling the ADXRS450 to operate in exceedingly harsh environments where shock and vibration are present. The ADXRS450 is capable of sensing angular rate of up to ±300°/sec. Angular rate data is presented as a 16-bit word, as part of a 32-bit SPI message.



Gyroscopes


ADXRS450

RATE SENSITIVE AXIS The ADXRS450 is available in two package options.

• The SOIC_CAV package configuration is for applications that require a Z-axis (yaw) rate sensing device. The device transmits a positive going LSB count for clockwise rotation about the axis normal to the package top. Conversely, a negative going LSB count is transmitted for counterclockwise rotation about the Z-zxis.

• The vertical mount package (LCC_V) option is for applications that require rate sensing in the axes parallel to the plane of the PCB (pitch and roll). The same principles of LSB count transmission for clockwise and counterclockwise rotation about the parallel axes apply to the LCC_V option.



Gyroscopes

ITG-3200 IntegratedTriple-Axis Digital-Output Gyroscope

Single-chip,digital-output,3-axis MEMS motion processing gyro.

Typical Applications:

• Motion-enabled game controllers • Motion-based portable gaming • 3D remote controls for Internet

connected DTVs and Set Top Boxes • Motion-based 3D mice • Remote health monitoring • Sports and fitness

Ref.: InvenSense

Orientation of Axes of Sensitivity and Polarity of Rotation.



Gyroscopes

Ref.: InvenSense

ITG-3200 Features

• Digital-output X-, Y-, and Z-Axis angular rate sensors (gyros) on one integrated circuit with a sensitivity of 14 LSBs per °/sec and a full-scale range of ±2000°/sec

• Three integrated 16-bit ADCs provide simultaneous sampling of gyros while requiring no external multiplexer

• Digitally-programmable low-pass filter

• Fast Mode I²C (400kHz) serial interface

• Low 6.5mA operating current for long battery life

• Digital-output temperature sensor (for temperature compensation information)



Gyroscopes

Ref.: InvenSense

ITG-3200 Block Diagram



Gyroscopes

Ref.: InvenSense

Connecting ITG-3200 to ARM Board

ARM Board used as bridge between PC and IMU-3000 Evaluation Board.



Humidity, Temperature

Ref.: SENSIRION

SHT21 - Digital Humidity Sensor (RH&T)

Smallest relativehumidity sensor.

The SHT21digital humidity and temperature sensorintegrates sensors, calibration memoryand digital interface on 3 x 3 mm footprint.Additionally the humidity sensor provides electronic read-out of tracking information. The complete over-moulding of the sensor chip – with the exception of the humidity sensor area – protects the fully calibrated and reflow solderable humidity and temperature sensor against external impact and leads to an excellent stability against aging, shock and volatile chemicals.

HumidityTemperature



Humidity, Temperature

Ref.: SENSIRION

SHT21 - Digital Humidity Sensor (RH&T)

Features

8 sec (tau63%)RH response time

-40 – +125°C (-40 – +257°F)T operating range

0 – 100% RHRH operating range

3.2uW (at 8 bit, 1 measurement / s)Energy consumption

I2C digital, PWM and SDM/analog Volt interface

Output

Problem

Using the sensor more then 10% in active state may cause internal heating which results in a temperature and humidity deviation.



SHT21

Ref.: SENSIRION

Communication with SensorSHT21 communicates with true I2C protocol.For information on I2C beyond the information in the following Sections please refer to the following website:

Serial SDA (SDA)The SDA pin is used to transfer data in and out of the sensor. For sending a command to the sensor, SDA is valid on the rising edge of SCL and must remain stable while SCL is high. After the falling edge of SCL the SDA value may be changed.



Sensors

MIMOSAMicrosystems platform for MObile Services and Applications

Microsensors and –actuatorsfor Ambient Intelligence applications

Ref.: Jörg Eichholz, ISIT - Fraunhofer Institute for Silicon Technology, Itzehoe

A European project supported within the IST priority of theSixth Framework Programme for Research and Technological Development.



Sensors

Lactate sensor

- Physiological monitoring the lactate in the interstitial fluid under the skin

- Buildup with a microneedle array a painless lactate monitoring device used as a plaster

Concept of plaster

- Silicon based enzyme sensor technology (ISIT)- Low-cost polymer microneedle array(Åmic)



Sensors

Concept of polymermicro-needle array:

• Sharp needles for penetrating the skin• Narrow via hole for high capillary force



Sensors

-Needle process results

Ref.: Jörg Eichholz, ISIT - Fraunhofer Institute for Silicon Technology, Itzehoe



Ear Sensor

Ref.: Guang-Zhong Yang, Imperial College.

The e-AR (ear-worn Activity Recognition) sensor



Ear Sensor

Ref.: Guang-Zhong Yang, Imperial College.



Sensor Fusion

Sensor Data FusionforContext-Aware Computing



Sensor Fusion

Multi-sensor data fusion

Irrespective of the applications, the three main issues which are pervasive in sensor data fusion are:

- Interpretation and Representation- Fusion, Inference and Estimation- Sensor Management

Ref.: Olena Punska: Bayesian Approaches to Multi-Sensor Data Fusion, 1999



Sensor Fusion

Ref.: Huadong Wu,Sensor Data Fusion for Context-Aware Computing Using Dempster-Shafer Theory, 2003



Sensor Fusion


The user-centered scheme to group context information



Sensor Fusion


Mapping sensory data into context information space

personal physical state:heart rate respiration rate blood pressure blink rate Galvanic Resistance body temperature sweat

registration & psychophysiological mapping

Biometric sensors:heart-rate blood-pressure GRS, temperature respiration

physical & chemical environmentdirect registrationThermometer, barometer,humidity sensor, photo-diode sensors, accelerometers, gas sensor

location altitude speed orientationmap registrationGPS, DGPS serverIP, RFID gyro, accelerometers dead-reckoning, network resource

object recognition 3-D object measuring face recognition

image processingCamerasinfra-red sensors

sound pattern recognition speaker recognition, speaking understanding

sound processingMicrophone

Context informationWidgetSensors



Sensor Fusion


Sensor fusion can be classified from different perspectives, such as information level, implementation architecture, algorithms being used, etc.

Possible Classification:Competitive type sensor fusioncombines sensor data that represent the same measurement to reduce uncertainty and resolve conflicts. This is the basic sensor fusion type. It is often regarded as the "traditional" or "classical" sensor fusion technique. Some examples of this type are: taking multiple measurements and then applying the weighting average algorithm to accurately evaluate the size of a machine part, manufacturing a certain number of standard parts and measuring the products to evaluate the status of a machine tool, etc.

Complementary type sensor fusioncombines incomplete sensor data that do not dependant on each other directly to create a more complete model. For example, combining sensor data according to a predefined physical model could enable the sensor outputs collectively to estimate the state of a higher-level measured physical process. A more specific example is combining measurements of pressure and airflow to estimate the propulsive force of a jet nozzle.

Cooperative type sensor fusioncombines sensor observations that depend upon each other to deduce higher-level measurement. In stereovision, for example, image components (pixels, featured spots) depend on each other in pairs to estimate object distances.



Sensor Fusion


Generally speaking, competitive sensor fusion enhances measurement reliability or confidence, whereas complementary and cooperative sensor fusion lead to higher-level measurements.The three sensor fusion types are not exclusive though, as many sensor fusion processes can belong to more than one type. Moreover, of the three sensor fusion types, the complementary and the cooperative types are generally domain specific.This means that their methods are often valid only under specific conditions where specific knowledge based artificial intelligent inference techniques can apply.

From the information process model point of view, however, sensor fusion can be roughly grouped into three categories:

a) direct data fusionb) feature level fusionc) decision level (identity declaration) fusion

continued



Sensor Fusion


S1

Association

S2 Sn…

…

S1

Feature Extraction

S2 Sn… S1

Association

S2 Sn…

AssociationData Level Fusion

Feature Extraction Feature Extraction

Identity DeclarationFeature Level

FusionIdentity Declaration

Declaration LevelFusion

Identity Declaration

Joint IdentityDeclaration

IdDecl

IdDecl

IdDecl

…



Sensors SensorsSensors

Sensor fusion process model

Direct data fusion Feature level fusion Declaration level fusion



Sensors

Sensors and WPAN

RF Shadowing Effects



Sensors

Setup of experiment investigating RFtransmission through the human body

Ref.: Anirudh Natarajan, Mehul Motani, Buddhika de Silva, Kok-Kiong Yap & K. C. Chua;Investigating Network Architectures for Body Sensor Networks

Comparing the human body to two other media, namely air and aluminum (which shields all radiation). There are two ways in which transmissions can be propagated from one node to another, i.e., the line-of-sight (LOS) and reflected multipath. The multipath phenomenon is heavily influenced by the environment. To ensure consistency, all experiments were performed in the same rich scattering environment.All experiments were conducted with Crossbow TelosB motes, which uses the popular 802.15.4 compatible transceiver Chipcon CC2420, also used in the Imperial College BSN motes.

Packet delivery ratio (PDR) for transmissionthrough human body – The figures are for thehighest/lowest power level.

Challenges of radio wave transmission around the human body.



Security

Security Issues

in Body Sensor Networks

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Ambient Intelligence - dispert.international-university.eudispert.international-university.eu/lecture-files/9-Ubicomp_AmI_BAN... · Fachbereich Informatik und Elektrotechnik Ambient