20
10
Falls Detection using Accelerometry and
Barometric Pressure
Author: Tabish Rizvi
Supervisor: Dr. Stephen Redmond
Motivation
Pattern Classification
Falls and falls induced injuries among the elderly are a
major area of concern. This is because the injuries that
the elderly sustain due to falls often require immediate
medical attention.
To serve as a point of comparison to decision tree
classification, a Bayesian pattern classifier was adopted.
A 0.25s time window was deemed to be sufficient to capture
the information of a fall.
Tri-axial Accelerometry Signals
However, due to lack of available sensor data and/or poor
algorithm design, falls detection devices currently suffer
from high levels of inaccuracy.
{
Objective
Acceleration (g)
To facilitate independent living, wearable sensor devices
have been developed to automate falls detection.
Fall
The aim of this thesis was to improve falls classification by
designing a falls detection algorithm that considers data
from a tri-axial accelerometer, a tri-axial gyroscope and a
barometer.
Time (s)
At each 0.25s interval in time, the posterior probability is
calculated for the feature vector using Bayes theorem:
Feature Extraction
From the sensor signals, features of interest such as the
subject’s orientation are extracted to aid in falls detection.
Orientation (degrees)
Subject Orientation
A window in time is then classified as a fall if:
Clinical Trial
To compare the accuracy of the falls algorithms, a clinical trial
was conducted in a supervised environment with the
University of New South Wales Ethics Committee approval.
Time (s)
Decision Tree Classification
As a first approach to designing a falls detection algorithm, a
decision tree classifier was considered. The algorithm is
designed so as to minimise redundant computations and
maximise detection accuracy.
ΔP > th?
Yes
Mean tilt angle
> 20⁰?
No
Algorithm
Wait 1 sec
Calculate mean
tilt angle (1 sec)
No
ΔP peak?
Yes
Yes
Wait 0.5 sec
Max tilt angle >
40⁰?
Abnormal gyro.
peak?
Yes
Gyro peak
between range?
Yes
Tilt angle < 30⁰?
Yes
No
UNSW
Results
Author
Angular rotation
mag. > 50⁰?
aSMA < th?
Yes
Fall with
recovery
Yes
Fall
No
Data Source
Accelerometer
Gyroscope
Algorithm 1
Karantonis

Algorithm 2
Bianchi

Algorithm 3
Rizvi


Algorithm 4
Rizvi


Yes
Abnormal acc.
peak?
Yes
Five healthy volunteers (3 male and 0 female; age: 22.3 ± 0.57
years; height: 1.81 ± 0.04m) participated in the study. Subjects
were asked to perform a sequence of falls and a series of
actions that were designed to mimic activities of everyday
living, e.g. sitting down into a chair.
Barometer
Classifier
Decision Tree

Decision Tree

Decision Tree
Pattern
Algorithm 1
Algorithm 2
Algorithm 3
Algorithm 4
Accuracy (%)
75.68
72.92
87.84
82.27
Sensitivity (%)
75.81
75.41
80.65
76.36
Specificity (%)
75.58
71.08
93.02
86.05
Conclusion
Algorithm 3 offers the best performance balance in terms of
distinguishing fall events from movements of everyday living.
The use of gyroscopes has aided in improving the accuracy of
falls classification.
Future Work
• Comprehensive falls trials.
• Algorithm 3 gyroscope parameter adjustment.
• Bayes classifier window size and feature vector optimisation.
• Further application of pattern recognition techniques.
ENGINEERING @ UNSW