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Current and Future Applications for Motion and Bio Sensor Networks
Stephan Bosch
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IntroductionIntroduction
Inertia Technology is a spin-off from University of Twente, The Netherlands, that brings forward the idea of Wireless Sensor Networks in Motion.
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OutlineOutline
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Inertia Technology Wireless Sensor Networks Motion Sensing Healthcare Applications
Activity monitoring Activity stimulation
Sports Applications Miscellaneous Applications
Activity recognition Movement-based group detection Wireless Sensor and Actuator Networks
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Inertia TechnologyOverview
Inertia TechnologyOverview
Using low-cost wireless sensor network technology, Inertia captures and monitors the motion of people
and moving objects, recognizes current situations and activities, provides constructive feedback and takes appropriate actions.
Applications in Healthcare, Sports, Industry, Logistics, etc.
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Inertia TechnologyFeatures
Inertia TechnologyFeatures
Smaller. Highly miniaturized platforms (sensor nodes) with low-power
microcontrollers, wireless communication and sensors. Size matters because the sensor nodes have to be easily worn by
people, embedded into objects or deployed within the infrastructure.
Smarter. Sensor nodes process data locally Sensor nodes collaborate in a network to achieve more accurate and
reliable results Accuracy increases with having multiple points of observation and
multiple types of sensors.
Wireless. No wires means reduced installation and maintenance costs. The system is autonomous, self-organizing, self-repairing and does not
require any external infrastructure.
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Wireless Sensor NetworksIntroduction
Wireless Sensor NetworksIntroduction
Tiny, cheap, battery-powered nodes with:
Sensors CPU and memory Wireless transceiver
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Wireless Sensor Networks Networking
Wireless Sensor Networks Networking
Ad-hoc networks of sensor nodes Self-organizing Unattended Collect data and communicate
over multiple hops Context-aware React locally to events
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Environmental Monitoring
(traditional)
Oil & Gas Industry Transport and Logistics
Guidance in Emergency situations
Wireless Sensor NetworksApplications
Wireless Sensor NetworksApplications
Applications:
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Wireless Sensor NetworksChallenges
Wireless Sensor NetworksChallenges
Energy efficiency Very limited resources Massive deployment - large WSN infrastructures Heterogeneity - specialized nodes (log
environmental data, check hazardous substances, fire detection, localization, etc.)
Reconfigurability - parameter tuning / complete reprogramming
Reliability - key factor in the given application domains
Programmability - user programming interface
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Wireless Sensor NetworksSensor Intelligence
Wireless Sensor NetworksSensor Intelligence
Sensor nodes can sense, think, talk and act. Sensing and communication are widely
discussed. Distributed, collaborative reasoning and
actuation only recently gained attention Many AI techniques that match the specifics of
WSNs – unreliable, imprecise information, scarce resources, local processing combined with distributed reasoning.
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Wireless Sensor Networks The temperature and humidity
problem
Wireless Sensor Networks The temperature and humidity
problem
So many names for similar ideas: WSN-WSAN, Smart Objects, Pervasive Systems, Ubiquitous Computing, Internet of Things...
In the end, it's all about embedding some intelligence and wireless capability into devices.
Monitoring temperature and humidity became the "standard example" in WSNs.
Although temperature and humidity are very useful, after a while you start wondering, can sensor nodes do anything more intelligent than that?
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Motion SensingMotion Sensing
Motion sensors used to be large and power-inefficient
Recent advances in Micro-electro-mechanical systems (MEMS) made miniature motion sensors available
Such sensors are silicon devices with tiny moveable sensor parts on a nano scale
Power-consumption can be as low as 60 μW when measuring and 0.2 μW in standby mode
Courtesy of Sandia National Laboratories, SUMMiTTM Technologies, http://mems.sandia.gov
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Motion SensingModalities
Motion SensingModalities
Available sensors: Accelerometer: measures lateral acceleration Gyroscope: measures angular velocity Compass: measures orientation relative to
Earth’s magnetic field
These sensors can respectively measure acceleration, angular velocity and orientation in a three-dimensional manner.
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Motion SensingPossibilities
Motion SensingPossibilities
Movement sensors can capture a lot of information. Their potential usage is still to be explored by the community.
Latest mobile devices like mobile phones are equipped with motion sensors, creating the foundations for fun and very useful applications.
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Motion Sensingin a Wireless Sensor Network
Motion Sensingin a Wireless Sensor Network
Wireless sensor nodes are equipped with motion sensors
Motion data processing: Motion data is processed locally on the sensor nodes Motion data from multiple nodes is combined to a achieve a
common goal, e.g. activity monitoring and recognition Focuses on getting reliable results out of unreliable and
imprecise sensor data. Network provides just-in-time constructive feedback to
the user Ease of installation and instant wireless connectivity
without requiring external infrastructure Low-cost and low-power operation
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Motion SensingHardware
Motion SensingHardware
Wireless Sensor Nodes ProMove board
Three-dimensional accelerometer Three-dimensional compass Two-dimensional gyroscope MSP430 low-power microcontroller
48 kB flash ROM and 10 kB RAM
CC2430 802.15.4 radio + CPU (System on a Chip)
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Elderly and people with chronic conditions.
Paradigm shift from care center to home-centric, person-centric healthcare & wellbeing services.
Healthcare ApplicationsHealthcare Applications
Goals: Help to improve the physical condition. Augment training and exercising. Enhance interaction with smart objects and artifacts. Build virtual “healthy” groups and improve social interaction.
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Healthcare ApplicationsActivity Monitoring
Healthcare ApplicationsActivity Monitoring
Health condition and quality of life are directly influenced by the amount and intensity of daily physical activity.
This is particularly relevant to persons with chronic conditions, such as Chronic Obstructive Pulmonary Disease (COPD), asthma and diabetes. Persons suffering from these ailments enter a vicious circle, in
which being active causes discomfort, making them progressively less active and less healthy.
Monitoring the daily activity can stimulate people to perform exercises and to be more active.
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Activity MonitoringApplications
Activity MonitoringApplications
Mainly: improving health and quality of life for patients with chronic diseases (e.g. COPD).
But also: healthy users that want to assess and improve their overall fitness.
Activity monitoring systems can remind, stimulate and motivate people to be more active.
Within groups with a competitive nature this could work even better.
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Activity MonitoringThe Problem
Activity MonitoringThe Problem
We need to obtain a reliable measure of the user’s level of activity.
The solution needs to be unobtrusive: User needs to wear it during his daily life. Directly measuring the user’s energy expenditure
requires measuring the user’s rate of metabolism, which is an intrusive process and therefore not a solution.
The system needs to provide feedback in an intuitive manner, without the use of a PC.
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Activity MonitoringThe Basic Solution
Activity MonitoringThe Basic Solution
Provide an estimate of energy expenditure in stead of a measurement.
The estimate is obtained by measuring the amount of movement on a particular point of the subject’s body during daily life (the so-called IMA value).
A proven method is to use an accelerometer. This is limited to a single acceleration sensor
mounted near the subject’s center of mass.
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Activity MonitoringRemaining Issues
Activity MonitoringRemaining Issues
Using a single accelerometer not optimal in some situations: Certain activities contribute much more to the activity
estimate than others, although energy expenditure is expected to be similar.
The set of activities considered in existing work is usually limited to only simple daily activities like walking, sitting, running etc.
How to provide feedback to the user? Simplicity is key!
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Activity MonitoringEstimation Improvements
Activity MonitoringEstimation Improvements
Use more than a single movement sensor to obtain a more accurate activity level estimate.
However, there is a trade-off between the number of sensors used, their size and the obtrusiveness of the system.
Therefore, we will use small wireless sensor nodes distributed over the body of the user at key locations.
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Activity MonitoringFeedback
Activity MonitoringFeedback
Use a colored light to indicate the user’s activity level.
Mount it somewhere at a fixed location in the environment.
Such a feedback device can be used intuitively. The user does not need to carry the interface. Multiple users can use the same light interface
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Activity MonitoringUser Interface
Activity MonitoringUser Interface
Ambient Orb: LED-based RGB color lamp with
Displays the activity level as a color between red and green: With green meaning active
and red meaning lazy.
Supports multiple users by displaying colored ID for newly encountered users
Users interact by tapping their sensor: Two times to let orb display daily activity Three times to let orb display hourly activity
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Activity MonitoringExperiments (1)
Activity MonitoringExperiments (1)
Subjects wear a node at their pant belt
Nodes record activity values in flash memory on an hourly basis.
Subjects manually record their activities in a simple diary.
At a fixed time during the day, data is extracted wirelessly from the flash records during a short meeting.
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Activity MonitoringExperiments (2)
Activity MonitoringExperiments (2)
Tested multiple subjects performing various activities of interest in the course of multiple experiments.
Experiments include: home and office activities,
Three days with 9 subjects a skiing vacation,
One week and two subjects and various sports.
Tested in separate experiments of at least an hour with two to four subjects
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Activity MonitoringExample Result
Activity MonitoringExample Result
Two persons with similar daily activities:
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Activity MonitoringExperiment ResultsActivity MonitoringExperiment Results
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Healthcare ApplicationsActivity Stimulation
Healthcare ApplicationsActivity Stimulation
Stimulate user activity without actually monitoring
Users are usually not inclined to be more active without something in return
Make being active a fun and appealing thing to do E.g. by letting the user play a game that
stimulates activity
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Activity StimulationGaming
Activity StimulationGaming
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Activity StimulationExample
Activity StimulationExample
Submarine game User controls small submarine on screen with
a dumbbell as controller The submarine follows the up/down
movements of the player.
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Sports ApplicationsSports Applications
For professional athletes a means to quantify their level of training as well as their performance is important.
Real-time feedback is essential for quickly assessing the training efficiency, as well as preventing overuse injuries.
The Inertia system applies both to individual athletes that seek to improve their performance and teams where coordination is a key factor.
Current Applications Professional Cycling
Assessing proper bicycle fit by measuring joint angles and cyclist posture
Future sports of interest Skating (posture) Running
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Sports ApplicationsCycling: Bicycle Fit
Sports ApplicationsCycling: Bicycle Fit
Proper bicycle fit is essential for comfort, safety, injury prevention and peak performance [1]
Saddle height Adjusted such that the knee
should be flexed 25° to 30° from full extension
Stem and handlebar height Hands on brakes - torso should
flex to 45° Hands in the drops – torso should
flex to 60°[1] Silberman MR, Webner D, Collina S, Shiple BJ. Road bicycle fit.Clin J Sport Med. 15(4):271-6, Jul. 2005
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Sport ApplicationsBicycle Fit: FatigueSport Applications
Bicycle Fit: Fatigue
Due to fatigue during cycling, kinematics vary among joint angles (torso, knee, ankle) [2]
Injuries are the result from biomechanical alterations associated with fatigue.
Fatigue related injuries: primarily repetitive strain injuries (RSI) Overuse knee pain (42% - 62% of recreational
cyclists)[2] Dingwell, J.B.; Joubert, J.E.; Diefenthaeler, F.; Trinity, J.D., Changes in Muscle Activity and Kinematics of Highly Trained Cyclists During Fatigue, IEEE Transactions on Biomedical Engineering, vol.55, no.11, pp.2666-2674, Nov. 2008
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Sports ApplicationsBicycle Fit: Existing Solution
Sports ApplicationsBicycle Fit: Existing Solution
Camera system Vicon, Optotrak,… Active/passive
markers Measures distances Very accurate (0.1°,
1 mm) Works only indoors Works only in a
limited space
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Sports ApplicationsBicycle Fit: Wireless Sensor
Alternative
Sports ApplicationsBicycle Fit: Wireless Sensor
Alternative
(Wireless) inertial sensing system Portable, unobtrusive A lot of information: acceleration, angular
velocity, orientation How about accuracy?
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Sports ApplicationsBycicle Fit: Experiments (1)
Sports ApplicationsBycicle Fit: Experiments (1)
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Sports ApplicationsBycicle Fit: Experiments (2)
Sports ApplicationsBycicle Fit: Experiments (2)
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Sports ApplicationsBicycle Fit: MethodSports ApplicationsBicycle Fit: Method
What we need to measure: Thigh-shank (TS) peak
angles Shank-foot (SF) peak angles
Data processing activities: Establishing orientation of
each node (difficult) Computation of TS and SF
angles Peak detection Computation of accuracy
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Sports ApplicationsBicycle Fit: Sensor Orientation
Sports ApplicationsBicycle Fit: Sensor Orientation
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Sports ApplicationsBicycle Fit: Result Comparison
Sports ApplicationsBicycle Fit: Result Comparison
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Sports ApplicationsBicycle Fit: AccuracySports Applications
Bicycle Fit: Accuracy
Average TS error: 2.06 Average TS STD: 0.85 Average SF error: 4.65 Average SF STD: 2.27
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Industrial ApplicationsManufacturing
Industrial ApplicationsManufacturing
Assembly and testing processes in industrial manufacturing production lines.
Distributed system composed of wireless sensor nodes worn by the workers, embedded into the tools and deployed within the infrastructure.
Sensors self-organize and recognize online the currently performed activity – assistance, verification, training.
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Industrial ApplicationsDWARF
Industrial ApplicationsDWARF
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Movement-based Group Detection Overview
Movement-based Group Detection Overview
Communication of movement data
Synchronization of movement data
Computation of the correlation coefficient
Group objects and persons based on correlated movement Which sensors are moving
together?
-100
-50
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
X
Y Corr = 0.896
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Movement-based Group DetectionLogistics and Transport Applications
Movement-based Group DetectionLogistics and Transport Applications
Incorrect loading and delivery – can be checked by inertial sensors.
Detect groups of goods or vehicles based on their movement
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Movement-based Group DetectionOn-body Applications
Movement-based Group DetectionOn-body Applications
Automatic association of nodes moving together: Worn on the body Objects the persons is
interacting with Possible functionality:
Context and activity recognition
Multimodal HMI Build trusted networks Extendable to groups of
people that need to coordinate movements.
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http://www.youtube.com/watch?v=ZzWYO5dbo1M
Wireless Sensor and Actuator NetworksExample
Wireless Sensor and Actuator NetworksExample
Movement coordination of autonomous vehicles.
Inertial sensing + Wireless communication + Control loop = Swarm of moving vehicles.
Everything running on sensor nodes!
Demo video:
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Thank you!