Azzi, Djamel

Bersch, Sebastian

Khusainov, Rinat

English

Portsmouth University Research Portal (Pure)

FALL DETECTION USING BIOLOGIALLY INSPIRED 
MONITORING 
Artificial Immune System in the Area of Assisted Living 
Sebastian D. Bersch, Djamel Azzi and Rinat Khusainov 
School of Engineering, University of Portsmouth, Portsmouth, UK 
Sebastian.Bersch@port.ac.uk, Djamel.Azzi@port.ac.uk, Rinat.Khusainov@port.ac.uk 
Keywords: Artificial Immune System, AIS, Fall Detection, Health Monitoring, Wellbeing, Accelerometer. 
Abstract: This position paper supports the use of Artificial Immune System (AIS) in the area of Ambient Assisted 
Living (AAL). While AIS has been used for anomaly detection and classification in a wide range of 
applications, little work has been done on using AIS for detecting abnormal behaviour in health monitoring 
applications. In this paper, we propose to use AIS for fall detection, since falls can be seen as deviations 
from the normal behaviour. We justify our proposal by analysing research that has been carried out in the 
past using AIS in different fields and emphasising on the similarities to the area of AAL. The paper also 
describes the experimental setup that is currently being used for our current and future work. 
1 INTRODUCTION 
Due to the advances in medicine over the last 
half century, humans are able to live longer than 
ever before. Concurrently the birth rate is slowing 
down. These trends invert the aging pyramid (Figure 
1), meaning that soon there will be more people over 
the age of 65 than under (Commission, Economic, & 
Affairs, 2009). In economical terms: fewer 
caretakers will have to look after more elderly 
people. The field of Ambient Assisted Living (AAL) 
aims to find solutions for this problem. 
The research directions in AAL are widely varied, 
but the main aim is common - to support and help 
the elderly to stay longer safe and healthy in their 
home in a cost effective manner. The work ranges 
from sensor development for better vital sign 
monitoring (Boylan, 2011) and position information 
to recognition of Activities of the Daily Living 
(ADL) (Sim, et al., 2010) over to health monitoring 
(Monekosso & Remagnino, 2010) (see Figure 2). 
Each monitoring system will learn at one stage what 
“normal” health means. The problem is that the 
definition of “normal” is slightly different for each 
person and therefore introduces uncertainty in the 
results of each system monitoring the behaviour of 
an elderly person. So far researchers have 
concentrated on short term behaviour monitoring. 
These research projects included the use of Hidden 
Markov Model (HMM) (Monekosso & Remagnino, 
2010) and Rule Based Activity Recognition (Storf, 
Becker, & Riedl, 2009). A new direction is to move  
away from the short term monitoring and consider 
the long term monitoring of a person (Elbert, Storf, 
Eisenbarth, Ünalan, & Schmitt, 2011). 
This long term monitoring cannot only be used 
for behaviour analysis but also to improve fall 
detection. Previously, we investigated intelligent fall 
detection for elderly people (ADL in Figure 2) using 
Fast Fourier-Transformation (Bersch, Chislett, Azzi, 
& Khusainov, 2011) and neural networks to 
successfully detect falls. This position paper makes a 
Figure 1: Aging pyramid inversion 
Figure 2: Range of monitoring 
 case to use Artificial Immune System as a new way 
for adaptive long term monitoring. 
2 ARTIFICIAL IMMUNE 
SYSTEMS AND ASSISTED 
LIVING 
Artificial Immune Systems – AIS is a fairly new 
approach in Artificial Intelligence and is based on 
modelling the human immune system. The technique 
is very similar to genetic algorithms (GA) in terms 
of binary detectors used to match data strings with 
the main difference that it is better suited to detect 
system anomalies and abnormal behaviour patters 
(Jui-Yu Wu;, 2010). 
For a better understanding of the AIS function, 
the human immune system needs to be briefly 
described. The general function of the immune 
system (IS) is to detect cells in the human body and 
class them using the chemical surface structure 
(matching) into a “non-self” and a “self” set. While 
the “self” set is harmless to the body and is a 
repetitive pattern in the body, the “non-self” set is 
harmful and only a sporadic pattern in the body, 
besides a chronic illness. This is in technical terms a 
system anomaly and called a pathogen in IS terms. 
When the human IS detects a pathogen, it has to be 
attacked and destroyed. This can be illustrated by 
looking at the different illness symptoms people 
have with different infections. (Timmis, 2007) 
(Hofmeyr, 2000). 
Case for AIS in Assisted Living – In the work of 
(Hofmeyr, 2000), the authors point out that for AIS 
to be effective, an application should have the 
following features: 
• Require pattern classification and response 
• Require a distributed architecture scalable to 
environments with arbitrary numbers of 
nodes 
• Address problems for which there is some 
commonality of patterns across the nodes, 
i.e., multiple nodes see the same or similar 
patterns within some limited time period 
• Require the detection of novel anomalous 
patterns 
• Change behaviour slowly over time 
• Have storage capacities on any single node 
that are small compared to the amount of 
information required to represent all 
possible normal patterns 
These application requirements need to be compared 
to the future research areas listed in the Ambient 
Assisted Living Roadmap of the AALIANCE 
(Broek, Cavallo, Odetti, & Wehrmann, 2009) to 
evaluate the opportunities of AIS in AAL. The key 
research areas are: 
• Detection of emergency situations 
• Activity recognition 
• Handling imperfect information 
• Fusing sensor data 
Analysing the AAL research areas, the main 
qualities that are needed are data fusion and pattern 
recognition, which matches the ones for an effective 
AIS implementation.  
Previous Work – AIS has been used in a wide range 
of research areas including fault detection and 
monitoring systems. The past research in AIS has 
mostly looked into detection of deviations from 
normal behaviour. 
In the research work of Cai et al. (Yixin Cai, 
Mo-Yuen Chow, Wenbin Lu, & Lexin Li;, 2010) the 
authors are comparing Artificial Immune 
Recognition Systems (AIRS) versus Artificial 
Neural Networks (ANN), Logistic Regression (LR), 
Support Vector Machines (SVM) and K-Nearest 
Neighbour (KNN) for their ability for fault 
detection. The authors used different machine 
learning algorithms (including AIRS) to classify real 
power distribution fault data from three regions in 
North Carolina. AIRS outperformed the other 
algorithm most of the time.  
Polat et al. (Polat, Expert Systems with 
Applications, 2008) used a combination of principle 
component analysis (PCA) and AIRS to classify the 
UCI lung cancer data set. The authors were able to 
achieve 100% accuracy on the data set and pointed 
out that this accuracy is the highest among the 
classifier reports in the literature. The same authors 
also used a similar combination of AIRS with PCA 
in (Polat, Expert Systems with Applications, 2008) 
to classify EEG signals. Nearly the same accuracy 
was achieved. The authors point out that the most 
important feature of AIRS is the ability of its self-
learning and therefore enable a fully automated 
classification. 
Another application of AIS is the area of 
Intrusion Detection Systems (IDS). The authors of 
(Golovko, Komar, & Sachenko, 2010) proposed to 
use AIS in IDS to detect unknown intrusion attacks. 
Furthermore, Hofmeyr et.al (Hofmeyr, 2000) 
designed an architecture for an AIS and developed a 
network IDS called LISYS as a proof of concept. 
LISYS used the simulated data as a “self” set and 
was then confronted with seven different unknown 
attacks to the system. The outcome of the test was 
that all seven intrusive activities were detected. A 
low false positive rate was achieved without 
compromising the ability to detect intrusions. 
 In the field of monitoring systems Lehmann et.al 
(Bersini & Carneiro, 2006, pp. 335-348) used AIS to 
control the heating system of an intelligent home. 
The aim was to learn the inhabitants’ behaviour and 
adjust the heating pattern for the house accordingly. 
AIS was used to act quickly on the users’ special 
heating demands without forgetting its normal 
behaviour. The AIS was able to adapt and could still 
react to changes in the users behaviour. 
Considering the long term monitoring of the 
wellbeing of elderly people, several comparisons can 
be drawn to past work in AIS. Using the circadian 
activity rhythms (CAR), proposed in (Virone, 2008), 
as the foundation of the rhythm of an elderly person 
it is possible to speak of a normal repetitive pattern 
in living conditions, which is a necessity for AIS. 
The probability of a health related problem with the 
monitored person increases, if a deviation of its 
“normal” pattern occurs (Monekosso & Remagnino, 
2010) (Storf, Becker, & Riedl, 2009).  
In the work of Franco et al. (Franco, Demongeot, 
Villemazet, & Vuillerme, 2010), the nycthemeral 
shift (change in daily routines) is used to detect 
dementia related diseases. This shift represents an 
abnormality in a recurring pattern. Parallels can be 
drawn to IDS. Intrusion Detection Systems based on 
AIS use frequent events to build a “self” set and 
identify attacks through the deviation from “normal” 
traffic pattern (compare (Golovko, Komar, & 
Sachenko, 2010) and (Hofmeyr, 2000)). 
The research described above supports the 
believe of the authors that AIS will also achieve 
good results in the area of fall detection and in 
particular the feature of self-determination of AIS 
which should help improve the accuracy of fall 
detection and reduce false alarms in ubiquitous fall 
detectors. 
3 EXPERIMENAL SETUP 
Equipment and Data Collection – The accelerometer 
data that will be used for the proposed research was 
collected in an earlier experiment. The hardware 
used to collect the data was an inexpensive off-the-
shelf development kit. Two Texas Instruments 
eZ430-Chronos 868 MHz sports watches were used 
with an optimised firmware (customised to achieve 
equal time spacing between the accelerometer 
samples). One watch was attached to the subject’s 
waist, the other to its wrist. The watch samples the 
X, Y and Z axes each 1/10th of a second, and sends 
the data to a PC. Compared to other research 
experiments, which use higher sampling rates (up to 
160 Hz) (Cagnoni, Matrella, Mordonini, Sassi, & 
Ascari, 2009), a sampling rate of 10 Hz can be 
considered as quite low. The use of a lower 
sampling rate has three main benefits: 
1. Lower data generation 
2. Longer battery life of the wearable sensor 
3. Short peak accelerations are ignored 
The Data Set – The pre-recorded data collection 
included three different activities, walking, sitting 
and falling. The records of each activity were 2 
minutes long and up to 10 repetitions were available. 
As a consequence of the research looking into long 
term health monitoring, this data needed to be 
enlarged. The different data snippets of each activity 
were randomly linked together to render a simulated 
month worth of data. The requirements on the newly 
created data sets were: 
• Sitting had to be a continuous activity 
(occurring every 4 hours) 
• Falling had to be a sporadic event (once 
every 10 days). 
The longer time frame of the data set allows the 
AIS to be trained with different walking and sitting 
behaviours of one test person. This will help to build 
a “normal” behaviour pattern and a fall should be 
detected more easily because of the deviation from 
normal activity. 
Preparation of Data and Implementation of AIS –
The first implementation of the monitoring system is 
designed to validate the use of AIS in the field of 
AAL applications. The first stage should 
demonstrate that the particular used data set contains 
a normal as well as an abnormal self set. A data 
instance presented to the AIS contains four 
attributes: 
1. X-Acceleration 
2. Y-Acceleration 
3. Z-Acceleration 
4. Acceleration Average (calculated using 
equation (1) !"# =    ! − !"# ! + ! − !"# ! +    ! − !"# ! (1) 
 
In this phase, only the data set of the arm is used and 
no pre-processing takes place, besides a simple 
averaging over the last 5 data samples. Each data 
instances can be presented as a 32 Bit variable. 
Detectors are matched with the data instances using 
the Hamming Distance to calculate the bit difference 
between both instances (see Figure 3). 
Figure 3: Presentation of a data instance and matching 
 4 FUTURE WORK 
The next stage is the implementation of a monitoring 
system based on the described AIS method. The 
direct outcomes are to achieve a proof of a “non-
self” and “self” set in the used acceleration data set 
and a dynamic adaptation in immature and mature 
detectors. A dynamic variation in the monitoring can 
be achieved by mutating detectors at specific time 
intervals (lifespan). The mutation process can be 
based on different method like random generation or 
more advanced clonal algorithms. Therefore further 
research will look into the following areas: 
• Possible use of data pre-processing 
• Generation of detector 
• Lifespan of detector 
• Matching of detector and data instance 
• Uncertainty after a mature detector is 
triggered 
The authors believe that research in these areas will 
lead to an improved recognition in fall detection. 
The research outcome will be compared against the 
earlier results using FFT and neural networks. 
ACKNOWLEDGEMENTS 
This work is supported by the University of 
Portsmouth under the Higher Education Innovation 
Fund (HEIF 4) and performed by the Digital 
Wellbeing Research Group at the same University.  
REFERENCES 
Yixin Cai, Mo-Yuen Chow, Wenbin Lu, & Lexin Li;. 
(2010). Evaluation of distribution fault diagnosis 
algorithms using ROC curves. Power and Energy 
Society General Meeting, 2010 IEEE , 1 - 6. 
Virone. (2008). Monitoring activity patterns and trends of 
older adults. Engineering in Medicine and Biology 
Society, 2008. EMBS 2008. 30th Annual International 
Conference of the IEEE , 2071 - 2074. 
Bersch, S., Chislett, C., Azzi, D., & Khusainov, R. (2011, 
Jan 1). Activity detection using frequency analysis and 
off-the-shelf devices: fall detection from 
accelerometer data. eprints.port.ac.uk . 
Bersini, H., & Carneiro, J. (2006). Artificial Immune 
Systems: 5th International Conference, ICARIS 2006, 
Oeiras, Portugal, September 4-6, 2006, Proceedings 
(Lecture Notes in Computer Science).  
Boylan, G. (2011, Jan 1). EEG monitoring in the neonatal 
intensive care unit: A critical juncture. Clinical 
neurophysiology: official journal of the … . 
Broek, G., Cavallo, F., Odetti, L., & Wehrmann, C. (2009, 
Sep 22). Ambient Assisted Living Roadmap. 1-120. 
Cagnoni, S., Matrella, G., Mordonini, M., Sassi, F., & 
Ascari, L. (2009). Sensor Fusion-Oriented Fall 
Detection for Assistive Technologies Applications. 
Intelligent Systems Design and Applications, 2009. 
ISDA '09. Ninth International Conference on , 673 - 
678. 
Commission, E., Economic, D.-G., & Affairs, F. (2009). 
Ageing Report 2009. 
Elbert, D., Storf, H., Eisenbarth, M., Ünalan, Ö., & 
Schmitt, M. (2011). An approach for detecting 
deviations in daily routine for long-term behavior 
analysis. Workshop: Orange Alerts - Behaviour 
Modeling and Health of older people in their homes 
(AAL 2011). Dublin. 
Franco, C., Demongeot, J., Villemazet, C., & Vuillerme, 
N. (2010). Behavioral Telemonitoring of the Elderly at 
Home: Detection of Nycthemeral Rhythms Drifts from 
Location Data. Advanced Information Networking and 
Applications Workshops (WAINA), 2010 IEEE 24th 
International Conference on , 759 - 766. 
Golovko, V., Komar, M., & Sachenko, A. (2010). 
Principles of neural network artificial immune system 
design to detect attacks on computers. Modern 
Problems of Radio Engineering, Telecommunications 
and Computer Science (TCSET), 2010 International 
Conference on , 237 - 237. 
Hofmeyr, S. (2000, Jan 1). Architecture for an artificial 
immune system. Evolutionary Computation . 
Jui-Yu Wu;. (2010). Computational Intelligence-Based 
Intelligent Business Intelligence System: Concept and 
Framework. Computer and Network Technology 
(ICCNT), 2010 Second International Conference on , 
334 - 338. 
Monekosso, D., & Remagnino, P. (2010). Behavior 
Analysis for Assisted Living. Automation Science and 
Engineering … . 
Polat, K. (2008, Jan 1). Artificial immune recognition 
system with fuzzy resource allocation mechanism 
classifier, principal component analysis and FFT 
method based new hybrid automated …. Expert 
Systems with Applications . 
Polat, K. (2008, Jan 1). Computer aided medical diagnosis 
system based on principal component analysis and 
artificial immune recognition system classifier 
algorithm. Expert Systems with Applications . 
Sim, K., Yap, G.-E., Phua, C., Biswas, J., Phyo Wai, A., 
Tolstikov, A., et al. (2010). Improving the accuracy of 
erroneous-plan recognition system for Activities of 
Daily Living. e-Health Networking Applications and 
Services (Healthcom), 2010 12th IEEE International 
Conference on , 28 - 35. 
Storf, H., Becker, M., & Riedl, M. (2009). Rule-based 
activity recognition framework: Challenges, technique 
and learning. Pervasive Computing Technologies for 
Healthcare, 2009. PervasiveHealth 2009. 3rd 
International Conference on , 1 - 7. 
Timmis, J. (2007, Jan 1). Artificial immune systems—
today and tomorrow. Natural Computing . 
 


1). Activity detection using frequency analysis and off-the-shelf devices: fall detection from accelerometer data. eprints.port.ac.uk .

1). Architecture for an artificial immune system. Evolutionary Computation .

1). Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated …. Expert Systems with Applications .

1). Artificial immune systems— today and tomorrow. Natural Computing .

1). Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm. Expert Systems with Applications .

1). EEG monitoring in the neonatal intensive care unit: A critical juncture. Clinical neurophysiology: official journal of the … .

22). Ambient Assisted Living Roadmap.

An approach for detecting deviations in daily routine for long-term behavior analysis. Workshop: Orange Alerts - Behaviour Modeling and Health of older people in their homes (AAL

Behavior Analysis for Assisted Living. Automation Science and Engineering … .

Behavioral Telemonitoring of the Elderly at Home: Detection of Nycthemeral Rhythms Drifts from Location Data.

Computational Intelligence-Based Intelligent Business Intelligence System: Concept and Framework.

Evaluation of distribution fault diagnosis algorithms using ROC curves. Power and Energy Society General Meeting,

Improving the accuracy of erroneous-plan recognition system for Activities of Daily Living. e-Health Networking Applications and Services (Healthcom),

Intelligent Systems Design and Applications,

Monitoring activity patterns and trends of older adults.

Principles of neural network artificial immune system design to detect attacks on computers.

Rule-based activity recognition framework: Challenges, technique and learning. Pervasive Computing Technologies for Healthcare,

Sensor Fusion-Oriented Fall Detection for Assistive Technologies Applications.

Fall detection using biologically inspired monitoring: artificial immune system in the area of assisted living

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Fall detection using biologically inspired monitoring: artificial immune system in the area of assisted living

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