Transcript FALL DETECTION OF ELDERLY THROUGH FLOOR VIBRATIONS …

Mr. Dima Litvak
Prof. Israel Gannot
Dr. Yaniv Zigel
July 2008
 Baby boomers are growing older
 Longevity of life
 Hospitalization costs raise
 Lack of rooms and health care
professionals in Care Centers
 People prefer to stay in their
natural habitats
 Independence of elderly people
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First cause of accidental death, third cause of chronic
disability, and the fifth most common cause of death [1-2].
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1 of 3 persons over the age of 65 fall at least once a year [3].
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At least 70% of all falls occur at home[4].
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The costs of falls in the US in 2006 were $19.2 billion [5].
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People that fall, develop a phobia of falling again [6].
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Fast fall detection increases the chances to survive.[8]
Popular solutions
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Social alarms [9-10]
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Wearable automatic fall detectors [11-15]
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Video based fall detection systems [16-17]
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Floor vibration passive system [18]
The proposed solution
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A human fall creates a shock signal that propagates in the
floor with a suitable sound event in the room
Detection of vibration and sound signals by an accelerometer
and a microphone that are attached to the floor.
Signal processing and pattern recognition techniques to
detect the events and distinguish between a human fall, and
other events such as fall of an object.
Signals are recorded by NI Labview and analyzed by Matlab.
The proposed solution
Controller
Today
In the future
Fall detection and classification
algorithm
Training phase
Database
of fall
sounds and
vibration
Event
detection and
segmentation
model
estimation
feature extraction
and selection
Memory
Other
events'
model
Event
classification
Adaptive noise
threshold
Signals
from
sensors
Event
detection and
segmentation
Testing Phase
Human fall
model
feature
extraction
Human-fall /
other event
decision
The signals
Vibration Signal
Sound Signal
The training phase
Event Detection and Segmentation:
eth1 = 0.00562
emax=0.2352
ne=664
nstart=658
nend=677
emax=2.352/0.00001
The training phase
Event Detection and Segmentation:
Vibration
signal
Sound
signal
Vibration
event
Sound
event
Feature Extraction:
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The training phase
The signals of a human fall and other events might look
similar. Therefore, the main problem is to find the
appropriate special features for classification.
Temporal Features: length (time) and energy of vibration
and sound events (total of 4 features)
Spectral Features: shock response spectrum (SRS) [19]
from vibration event and Mel frequency cepstral
coefficients (MFCC) [20] from sound event.
The training phase
Shock response spectrum (SRS):
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Robinovitch et al. [21] described the dynamics of impact
to the hip during a fall event as Mass-Spring system.
x
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kc
bc
The SRS calculation is kind of a wavelet transform that
assumes that the fall event is a mass-spring system.
The training phase
Shock response spectrum (SRS):
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The SRS is the peak acceleration responses of a large
number of single degree of freedom (SDOF) systems each
one with a different natural frequency.
It is calculated by calculation of the convolution integral
of the measured signal (Base input) with each one of the
SDOF systems and taking the maximum of the result.
The training phase
Shock response spectrum (SRS):
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Example of the SRS plot of a vibration event as measures by
our accelerometer:
93 values of the SRS were taken as candidate features
The training phase
Mel frequency cepstral coefficients
(MFCC):
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Popular in speech and speaker recognition [22]
Represent audio signals with frequency bands on the mel
scale:
f
Mel ( f )  2595 log10 (1 
)
700
 The algorithm: Divide the signal to frames (0.03 sec.) ->
-> Calculate FFT for each frame -> Take the logarithm ->
-> Convert to Mel spectrum with filter bank-> Calculate DCT
Frequency (Hz)
The training phase
Mel frequency cepstral coefficients
(MFCC):
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The MFCC transform supplies 13 features for each frame
We chose the frame with the maximum energy
Example of MFCC coefficients from a sound event signal
Feature Selection:
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The problem of selecting a subset
of features from N-dimensional
features measurement vector.
Sequential forward floating
selection (SFFS) algorithm with
Mahalanobis distance criterion
for performance evaluation of
the features.
The training phase
The training phase
Feature Selection:
No. of
Kind of feature
Vibration/Sound
Feature
Feature name
Selected features
features
feature
symbol
Vibration event length
1
Vibration
L1
L1
Sound event length
1
Sound
L2
L2
Vibration event energy
1
Vibration
E1
E1
Sound event energy
1
Sound
E2
-
Temporal features
S2, 10, 34, 64, 68, 74,
SRS
93
Vibration
S1-S93
76, S77, 82, 84, 91
Spectral features
MFCC
13
Sound
C1-C13
C3, C11, C12
Complete set of top performing 17
features was chosen for classification.
Classifier Estimation:
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The training phase
Bayes classification with Gaussian conditional density
function for the two classes: “Human”, “Other event”.
Choose class (i=1,2) for a specific vector in the features'
N dimentional space with the quadratic classifier:
1
1
i  arg max{  ln | C k |  ln P (k )  ( Z   k )T C k1 ( Z   k )}
2
2
k 1, 2
µk - expectation vector Ck - covariance matrix
z – vector in features‘ space k- class number (k=1,2)
Classifier Estimation:

The training phase
In the training phase, the algorithm estimated a Gaussian
model for each class by the training data.
'Human' fall
'Human' fall
Other
event
Other
event
The testing phase
Training phase
Database
of fall
sounds and
vibration
Event
detection and
segmentation
model
estimation
feature extraction
and selection
Memory
Other
events'
model
17 features are
extracted from
the testing data,
and classified
Event
classification
Adaptive noise
threshold
Signals
from
sensors
Event
detection and
segmentation
Testing Phase
Human fall
model
feature
extraction
Human-fall /
other event
decision
Experimental setup
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Falls were simulated by drops of “Rescue
Randy”- a human mimicking doll and
four “popular falling” objects.
The objects include: a heavy bag,
a book, a plastic box and a metal box.
Distance of 2-5 meter.
Drops Close to the sensors:
the objects + walking, dropping a chair,
jumping from the chair on the floor
Experiments have been performed on a
typical concrete tile floor.
The trials
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Training phase:
40 drops of "Rescue Randy“ (40 detected)
80 drops of objects (28 detected)
12 events close to the sensors (12 detected)
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Testing phase:
20
20
48
18
drops of "Rescue Randy“ on the floor (20 detected)
drops of "Rescue Randy“ on the carpet (20 detected)
drops of objects (44 detected)
events close to the sensors (18 detected)
Results
Real
"Human"
Events close to
Objects
the sensors
Class. As
"Human"
("Other event")
carpet
("Other event")
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on a
"Other event"
44+4 undetected
17
1
0
"Human"
0
1
19
20
Undetected event classified as “Other event”
The sensitivity is 97.5% (39/40)
The specificity is 98.5% (65/66)
Discussion
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The results show that the proposed solution has a
potential to serve as a reliable innovative solution for
detection of falls.
The proposed solution is a low cost, does not require the
person to wear anything, and is considerate of privacy.
The system is adaptive, can be calibrated to any kind of
floor and room acoustics.
For improvement of the classification algorithm, training
can be performed using various weights of "Rescue
Randy" dolls and objects, in a wider variety of kinds of
drops.
Evaluation of the maximum distance in which the
sensitivity is low.
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Thanks
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Prof. Israel Gannot and Dr. Yaniv Zigel
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Lab staff: Arik, Idan, Moshe, Marina, Michal, Ranit, Tomer
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LTC Yakov Shalom – Head of Medical Engineering division,
medical corps, IDF
Workshop staff
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My girlfriend Alona, and my Family.
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