Mitja Luštrek Jožef Stefan Institute Department of Intelligent Systems.
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Transcript of Mitja Luštrek Jožef Stefan Institute Department of Intelligent Systems.
Mitja LuštrekJožef Stefan Institute
Department of Intelligent Systems
Environment should be◦ Intelligent◦ Require no special skills of the user◦ Require minimal interaction from the user
The technology should disappear Its advantages should remain
Defined by objectives, not methods Interdisciplinary
On the go:◦ Wearable sensors◦ Smart phone applications
At home:◦ Sensors◦ Computer controlled appliances◦ Home automation
Living labs (Philips...)
Pupulation is aging – over 65 in Europe:◦ 17.9 % in 2007◦ 53.5 % in 2060
Not enough young people to care for the old Technology must step in
◦ Assistance with activities of daily living (ADL)◦ Detection of health problems
Equip elderly with radio tags
Sensors determine tag coordinates:◦ Installed in the
appartments◦ Included in tags and
portable device outdoors Detect falls and other
health problems
Portable
device
Body tags
Sensors in the
appartment
Equip elderly with radio tags
Sensors determine tag coordinates:◦ Installed in the
appartments◦ Included in tags and
portable device outdoors Detect falls and other
health problems
Intelligence
Radio tags and sensors to be developed in the project◦ Distance to tag – time needed for signal to travel
from tag to sensor◦ Direction of tag – angle of arrival of the signal
Expected standard deviation of noise:◦ ~5 cm when stationary (Ubisense × 1)◦ ~10 cm when moving (Ubisense × 2)
6 infrared cameras 12 reflective markers on the
body Multiple cameras see a marker
⇒ location can be computed
Standard deviation of noise:◦ ~1 mm
Add more noise to simulate radio hardware
815 recordings:◦ Walking◦ Sitting◦ Lying◦ Falling – 11 types◦ Lying down◦ Sitting down◦ Health problems:
Limping Hemiplegia (stroke) Parkinson’s disease Dizziness Epilepsy
Six
basic
activ
ities
Input: sequence of snapshots of tags (each consisting of coordinates of all tags)
Attributes
Output: posture/activity (walking, lying...)
Class
Manually segment and label recordings Compute attributes for each snapshot Concatenate to create attribute vectors
Z coordinates of tags Absolute, z velocities of tags Absolute, z distances between tags
Attributes – Attributes – anglesangles
All coordinates of tags Velocities of tags
(absolute, direction)
One coordinate systemper snapshot
One coordinate systemper 1-second interval
Two options
Two more options: each coordinate system can use reference z axis
Attributes: reference coordinate system Machine learning algorithms:
◦ SVM◦ Random forest◦ Bagging◦ Adaboost M1 boosting◦ 3-nearest neighbor
Winner:◦ Reference coordinate system + angles◦ SVM
Sitting down, no noise
Falling, Ubisense × 1 noise
Tag placement◦ More tags ⇒ better performance◦ More tags ⇒ worse user acceptance
Noise level◦ We are only estimating noise of the radio
hardware
12 11 10 9 8 7
6 5 4 3 2 1
LR
We can recognize walking Can we recognize abnormal walking?
Gait (way of walking) important to physicians
Used to recognize health problems in clinical setting
Support (foot on the ground), swing (foot off the ground) and step (support + swing) times
Double support time (both feet on the ground) Step length and width Maximal distance of the foot from the ground Ankle, knee and hip angles upon touching the ground Knee angle when the ankle of the leg on the ground is
directly below the hip and knee angle of the opposite leg at that time
Minimal and maximal knee and hip angles, the angle of the torso with respect to the ground, and the range for each
Hip and shoulder sway (the difference between the extreme left and right deviation from the line of walking)
X, y coordinates of ankles L: lowest distance travelled (standing still) H: highest distance travelled (moving)
Normal:◦ Completely normal◦ With a burden
Abnormal:◦ Limping◦ Hemiplegia (stroke)◦ Parkinson’s disease◦ Dizziness
In-depth analysis of activities other than walking
Attributes other than walking signature
Macroscopic movement (about the appartment)