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Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System
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Fan, X., Zhang, H., Leung, C. & Shen, Z. (2019). Fall Detection with Unobtrusive Infrared Array Sensors. Multisensor
Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, (pp. 253-267). Springer.
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Fall Detection with Unobtrusive Infrared Array
Sensors
Xiuyi Fan1, Huiguo Zhang2, Cyril Leung3, and Zhiqi Shen2
1Swansea University, Swansea, United Kingdom,
2Nanyang Technological University, Singapore,
3The University of British Columbia, Canada
Abstract. As the world’s aging population grows, fall is becoming a
major problem in public health. It is one of the most vital risks to the
elderly. Many technology based fall detection systems have been devel-
oped in recent years with hardware ranging from wearable devices to
ambience sensors and video cameras. Several machine learning based fall
detection classifiers have been developed to process sensor data with var-
ious degrees of success. In this paper, we present a fall detection system
using infrared array sensors with several deep learning methods, includ-
ing long-short-term-memory and gated recurrent unit models. Evaluated
with fall data collected in two different sets of configurations, we show
that our approach gives significant improvement over existing works us-
ing the same infrared array sensor.
Keywords: fall detection, machine learning, unobtrusive sensing
1 Introduction
Aging is a global challenge faced by many countries in the world. The rapid
growth of the aging population puts high demand for relevent assistive tech-
nologies supported by various sensor-actuator systems [15]. There are various
types of sensors utilized in assisted living, including cameras [24], light sensors,
accelerometers [39], temperature sensors, gyroscope, barometer, infrared sensors
[32], etc. These sensors are rich data sources for analyzing various aspects of a
user’s daily life, ranging from health and fitness monitoring, personal biometric
signature, navigation and localization [25]. In this context, one particular prob-
lem is the detection of falls. Fall is the most vital risk to the elderly’s health as
over one in every three elderly people suffer from fall consequences [12, 41]. In
event of fall, it is urgent to provide immediate treatment of the injured. Thus
the quick detection of fall is essential for on time treatment [38].
Technology based fall detection has been of great interest. It has generated a
wide range of applied research and has prompted the development of telemoni-
toring systems to enable the early diagnosis of fall conditions [27]. Mubashir et.
al. distinguish fall detection systems into three categories, wearable devices, am-
bience sensors and cameras [25]. The first category needs the subject of interest
2 Fan et al.
wearing a wearable device all the time whereas the last two only to deploy the
device in the vicinity of the subject.
In addition to sensor development, different data classification techniques
have been developed for fall detection. From raw sensor data, various data pro-
cessing algorithms have been proposed in the literature. Roughly speaking, there
are two schools of methods for fall detection: rule-based methods that detect falls
with domain knowledge and machine learning based approaches “learn fall char-
acteristics” from training data [15, 27].
In this work, we present a fall detection system that is based on data col-
lected from Grid-Eye Infrared Array Sensors, which are low cost, low resolution
infrared thermal image temperature sensors. These low resolution sensors have
less intrusion of privacies when compared with high resolution sensors like RGB
cameras. Sensor data is processed with several mainstream deep learning mod-
els, including the long short term memory (LSTM) [11] and gated recurrent
unit (GRU) models [6]. We have also experimented these models with attention
mechanisms as proposed in [7]. We compare our approaches with the fall detec-
tion system reported in [22], which also uses the same Grid-Eye sensor, and we
show that our approach yields improvement over existing ones.
The rest of this paper is organized as follows. Section 2 introduces several
existing works on fall detection. Section 3 introduces deep learning classifiers we
developed in this work. Section 4 presents performance evaluation of the devel-
oped fall detection system. We conclude the paper and discuss future research
directions in section 5.
2 Related Work
Existing fall detection systems can be categorized into three types, wearable
devices, camera systems and ambience sensors [25]. Wearable devices are sensors
attached to a human body to collect body movements and to recognize activities.
Most wearable devices use accelerometers and gyroscopes [16, 4]. In these fall
detection systems, sensors are attached to different parts of the user’s body such
as waist [41], chest [12], and shoes [30]. One major problem with wearable device
based methods is that the user has to wear the device all the time, which causes
a great amount of inconvenience. Also, users often forgot to wear such devices
from time to time.
Camera based fall detection systems normally use RGB cameras [28]. Re-
cently, several studies also use Microsoft Kinect [33, 23]. Camera-based devices
are commonly deployed through the elderly’s house or at public places. There
are two limitations with these systems, privacy intrusion with video monitoring
and the lack of system robustness.
Ambience sensor based fall detection systems have also been studied. Differ-
ent sensors or devices such as doppler radar [19], passive infrared sensors [20, 37,
22, 5], pressure sensors [35, 14], sound sensors [18] and Wi-Fi routers [36] have
been tested for fall detection.
Fall Detection 3
Many research has been devoted to the study of fall detection classification
algorithms [38, 1]. There are mainly two categories of methods developed, rule-
based methods that depend much on domain knowledge and machine learning
methods that recognize fall characteristics from sensor data [15, 27]. For instance,
[3, 2, 13, 17] are some early fall detection works with threshold-based algorithms.
In those works, thresholds are set such that if any of these thresholds is exceeded,
then a fall alert is triggered. The major drawback of these approaches is the lack
of adaptability and flexibility.
At the same time, various machine learning based fall detection classifiers
have been developed [21]. Mainstream machine learning approaches, including
decision trees [29], support vector machines (SVM) [34], k-nearest neighbours (k-
NN) [8] and hidden Markov models [10] have been applied in fall detection, see
e.g., [9, 26, 40, 5]. Many of these approaches rely on manually designed features
for classification.
The following works are most relevant to ours. L. Liu et. al. [19] develop a
dual Doppler radar system for fall detection. A fusion methodology combines
partial decision information from two sensors in three different classifiers, k-NN,
SVM and Bayes to form a fall/non-fall decision based on Melfrequency Cepstral
Coefficients (MFCC) features. Its performance measured with AUC is 0.88 and
0.97.
Liu et. al. [20] pospose a two-layer hidden Markov model for recognizing a
fall event based on the signals of five passive infrared sensors which were placed
at different heights on the wall. The associated sensitivity and specificity of the
falls algorithm were 92.5% and 93.7%, respectively.
Chen et. al. [5] use 16-by-4 thermopile array sensors for fall detection and el-
derly tracking. Two sensors are used in their system with a k-NN classifier. They
have reached 95.25% sensitivity, 90.75% specificity and 93% accuracy in their
experiment. Sixsmith and Johnson [31] developed a Smart Inactivity Monitor
using array-based detectors which also detects falls.
Mashiyama et. al. [22] propose a system of fall detection using an infrared
array sensor. From a data sequence obtained in a fixed window, four manually
crafted features, number of consecutive frames, maximum number of pixels, max-
imum variance of temperature and distance of a maximum temperature pixel,
are extracted from the sequence and used to classify falls or non-falls using with
the k-NN algorithm. Experiment results with their testing data show that their
system reaches 94% accuracy.
3 Fall Detection Classifiers
At the core of our fall detection system is the infrared array sensor, Grid-Eye
(AMG8832). A Grid-Eye sensor outputs an 8-pixel by 8-pixel temperature dis-
tribution in its 60-degree field of view at a maximum 10-frame per second rate.
Its maximum detection distance is 5m if there is a ≥4◦C temperature difference
between the foreground object and the background ambience. We use a Zig-
Bee CC2530 as a microprocessor to control the sensor via an I2C bus as shown
4 Fan et al.
in Figure 1. The measured temperature distribution is sent to another ZigBee
CC2530 at a 10Hz rate. A standard PC is then used for data processing and
classification.
Fig. 1. The Grid-Eye sensor package used in our experiment.
Although a Grid-Eye sensor measures temperature in a large range (-20◦C
to 100◦C), its temperature accuracy is only 3.0◦C. Since thermal image based
fall detection depends on correctly identifying the abrupt movement of a human
body, the ability to recognize the subtle temperature difference between the hu-
man body and the ambience is the key to ensure correct detections. However,
as illustrated in Figure 2, data obtained from Grid-Eye sensors is noisy. (In this
figure, warm colour indicates high temperature.) Thus, we develop a fall detec-
tion system with two main components: (1) data filters for pre-processing and
(2) neural networks for classification. As illustrated in Figure 3, data produced
by the Grid-Eye is firstly filtered with one of the filters. Filtered data is then
passed to neural network classifiers.
Three filters, Median, Gaussian and Wavelet, have been experimented in
this work. For neural network classifiers, we have experimented with two-layer
Fall Detection 5
Fig. 2. Illustration of Grid-Eye images. Top left: no person in Grid-Eye’s field of view.
Top right: a person standing on the right-hand side. Bottom left: a person falling from
the right-hand side. Bottom right: a person lying in front of the Grid-Eye.
Fig. 3. Fall Detection Classification Workflow.
6 Fan et al.
perceptron networks (Figure 4), long short-term memory (LSTM) networks and
gated recurrent unit (GRU) networks (Figure 5), each with and without attention
links.
Fig. 4. Two-layer Fully Connected Perceptron Network.
Fig. 5. LSTM / GRU Networks.
As illustrated in Figure 6, the developed system works as follows. At each
time step t, the Grid-Eye outputs thermal reading represented with a 1 ×64
vector. To detect fall, we examine data collected in a 2-second (outer) window.
Since the Grid-Eye is running at 10Hz, 20 1 ×64 vectors are collected during
each (outer) window. We then filter data stored in this outer window with one of
the three filters. For both median and Gaussian filters, an inner window of size
5 is used. For the wavelet filter, we use Daubechies 4 tap wavelet. The filtering
process does not change the size of the data. Filtered data is then sent to neural
networks for classification.
Two-layer perceptron networks with the following configuration are selected
for their simplicity. The input layer contains 64 ×20 = 1280 nodes (64 is the
length of the Grid-Eye output vector and 20 is the size of the outer window).
Fall Detection 7
Fig. 6. Data layout for filters and classifiers.
The fully connected hidden layer contains 400 nodes. The output layer contains
2 nodes (indicating a fall and not a fall, respectively).
LSTM and GRU networks have seen many successes in recent years. They
both contain “memory structures”, i.e., LSTM cells and GRU units, to store
past information. As illustrated in Figure 5, the input layers of our LSTM and
GRU networks both contain 64 nodes. There is a fully connected perceptron
layer with 64 nodes between the LSTM / GRU layer and the 2-node output
layer. The LSTM model can be described with the following equations.
i=σ(xtUi+st−1Wi) (1)
f=σ(xtUf+st−1Wf) (2)
o=σ(xtUo+st−1Wo) (3)
g=tanh(xtUg+st−1Wg) (4)
ct=ct−1◦f+g◦i(5)
st=tanh(ct)◦o(6)
Here, σis the sigmoid function. ◦denotes element-wise multiplication. xtis the
input at time t.stis the output of the cell at time t.Us and Ws are weight
matrices connecting various components. Specifically, in our system, xtis a 1-
by-64 vector; stis a 1-by-64 vector; Us, are 64-by-64 matrices; Ws are 64-by-64
matrices.
GRU [6] is a recently proposed variation of the LSTM model. The main
difference is that, instead of using three gates to control memory updates, a
8 Fan et al.
GRU unit uses only two gates. Formally, a GRU model can be described with
the following equations:
z=σ(xtUz+st−1Wz) (7)
r=σ(xtUr+st−1Wr) (8)
h=tanh(xtUh+ (st−1◦r)Wh) (9)
st= (1 −z)◦h+z◦st−1(10)
Again, σis the sigmoid function. xtis the input at time t.his the output.
stis the internal state of a GRU unit at time t. The size of Us and Ws are the
same as in LSTM. Essentially, we use the same network structure as our LSTM
implementation, with LSTM cells replaced by GRU units.
Introducing attention mechanism into both LSTM and GRU models in this
work is very simple. Conceptually, the attention mechanism provides a means for
specifying the relative importance of each frame in a classification window (20-
frames in our case). For instance, stin Equation 6 for t= 20 not only depends
on s19 but also (directly) depends on all previous si, for 1 ≤i≤19, i.e.,
s20 =X
0≤i<20
ωisi,(11)
for some ωialso learned with backward propagation though time as Uand W.
4 Performance Evaluation
To evaluate the performance of the developed system, we conduct fall detection
experiments in our laboratory environment (Figure 7). In our test, we have
created a dataset with 312 falls in two sets of configurations. As illustrated in
Figure 8, in the first set of experiments, the testing subject falls perpendicular
to the Grid-Eye sensor at A, B and C three different positions. In the second set
of experiments, the testing subject falls parallel to the Grid-Eye sensor, also at
A, B and C three different positions. In both configurations, negative examples
including randomly walking in the room, slowly sitting down, jumping, running
and laying down in front of the sensor have been performed. The dataset has
been created in multiple sessions crossing several days with ambient temperature
ranging from 19◦C to 23◦C.
For evaluation, we have divided the dataset into a training set with 240 falls
and a testing set with 72 falls with each falling position contains exactly the same
number of falls. Since robust fall detection requires high ratings in both precision
and recall, reducing both false positives and false negatives, we compare results
Fall Detection 9
Fig. 7. Testing Environment (illustrated for one testing configuration).
Fig. 8. Illustration of Experiment Configurations. In configurations shown on the left,
the testing subject falls in directions perpendicular to the Grid-Eye at positions A,
B and C. In configurations shown on the right, the testing subject falls in directions
parallel to the Grid-Eye at positions A, B and C.
10 Fan et al.
with F1 scores for each test case, defined as follows.
Precision = True Positive
True Positive + False Positive,
Recall = True Positive
True Positive + False Negative ,
F1 = 2 ×Precision ×Recall
Precision + Recall.
Table 1: Experiment Results from the MLP classifier.
F-Score Precision Recall Total True Positive False Negative
No Filter (H) 0.972 0.972 0.972 36 35 1
No Filter (V) 0.679 0.522 0.972 67 35 1
Median Filter (H) 0.986 0.972 1 37 36 0
Median Filter (V) 0.666 0.619 0.722 42 26 10
Gaussian Filter (H) 0.972 0.972 0.972 36 35 1
Gaussian Filter (V) 0.693 0.666 0.722 39 26 10
Wavelet Filter (H) 0.972 0.947 1 38 36 0
Wavelet Filter (V) 0.658 0.568 0.75 46 27 9
Table 2: Experiment Results from the LSTM classifier.
F-Score Precision Recall Total True Positive False Negative
No Filter (H) 0.956 1 0.916 33 33 3
No Filter (V) 0.864 0.777 0.972 45 35 1
Median Filter (H) 1 1 1 36 36 0
Median Filter (V) 0.805 0.805 0.805 36 29 7
Gaussian Filter (H) 0.986 0.972 1 37 36 0
Gaussian Filter (V) 0.805 0.805 0.805 36 29 7
Wavelet Filter (H) 0.986 0.972 1 37 36 0
Wavelet Filter (V) 0.746 0.659 0.861 47 31 5
Experiment results from our systems are shown in Table 1-5. In each table,
rows labelled with (H) and (V) are experment results from falls parallel and per-
pendicular to the Grid-Eye sensors, respectively. Overall, we make the following
observations.
–Measured by F1 scores, all classifiers perform better in settings where users
fall parallelly to the sensor. This indicates that falling-parallel-to-the-sensor
is intrinsically easier to classify than falling-perpendicular-to-the-sensor.
–Introducing filters specifically to remove noise improves the performance in
certain cases. Amongst three filters tested, the simple median filter performs
better than the other two.
Fall Detection 11
Table 3: Experiment Results from the LSTM-ATT classifier.
F-Score Precision Recall Total True Positive False Negative
No Filter (H) 0.972 0.947 1 38 36 0
No Filter (V) 0.857 0.804 0.916 41 33 3
Median Filter (H) 0.947 0.9 1 40 36 0
Median Filter (V) 0.819 0.723 0.944 47 34 2
Gaussian Filter (H) 0.96 0.923 1 39 36 0
Gaussian Filter (V) 0.735 0.627 0.888 51 32 4
Wavelet Filter (H) 0.944 0.944 0.944 36 34 2
Wavelet Filter (V) 0.749 0.681 0.833 44 30 6
Table 4: Experiment Results from the GRU classifier.
F-Score Precision Recall Total True Positive False Negative
No Filter (H) 0.972 0.9447 1 38 36 0
No Filter (V) 0.825 0.75 0.916 44 33 3
Median Filter (H) 0.935 0.878 1 41 36 0
Median Filter (V) 0.819 0.723 0.944 47 34 2
Gaussian Filter (H) 0.972 0.972 0.972 36 35 1
Gaussian Filter (V) 0.722 0.638 0.833 47 30 6
Wavelet Filter (H) 0.911 0.837 1 43 36 0
Wavelet Filter (V) 0.692 0.642 0.75 42 27 9
Table 5: Experiment Results from the GRU-ATT classifier.
F-Score Precision Recall Total True Positive False Negative
No Filter (H) 0.935 0.878 1 41 36 0
No Filter (V) 0.904 0.891 0.916 37 33 3
Median Filter (H) 0.986 0.972 1 37 36 0
Median Filter (V) 0.742 0.764 0.722 34 26 10
Gaussian Filter (H) 0.945 0.921 0.972 38 35 1
Gaussian Filter (V) 0.739 0.729 0.75 37 27 9
Wavelet Filter (H) 0.933 0.897 0.972 39 35 1
Wavelet Filter (V) 0.722 0.638 0.833 47 30 6
12 Fan et al.
–There is no clear winner between LSTM models and GRU models. The
memory ability of both models works well.
–Introducing attention mechanisms in both LSTM and GRU models does not
consistently improve the performance. This may suggest that fall detection
takes information from all frames containing a fall equally and it gives no
advantage to focus the detection at any single moment of the fall.
–When the classification problem is easy (parallel settings), MLP does not
expose its weakness; however, when the problem gets more difficult (perpen-
dicular settings), models explicitly recording previous information perform
significantly better.
In order to put our results into perspective, we compare our approaches with
the model presented in [22], which uses the same Grid-Eye sensor with a k-
NN classifier with four manually crafted features. We replicate their system and
tested on our dataset, the comparison results are shown in Table 6 (perpendicular
to the sensor) and 7 (parallel to the sensor). From these two tables, we see that
their approach also performs better when falls are parallel to the sensor. However,
overall, their k-NN classifier with manually crafted features performs worse than
any of our neural network based approaches with data filtering.
Table 6: Fall Detection Performance (Falls are perpendicular to the Grid-Eye).
Precision Recall F1
GRU-ATT 0.891 0.916 0.904
GRU 0.75 0.916 0.825
LSTM-ATT 0.804 0.916 0.857
LSTM 0.777 0.972 0.864
MLP 0.666 0.722 0.693
k-NN [22] 0.52 1 0.68
Table 7: Fall Detection Performance (Falls are parallel to the Grid-Eye).
Precision Recall F1
GRU-ATT 0.97 1 0.99
GRU 0.972 0.972 0.972
LSTM-ATT 0.947 1 0.972
LSTM 1 1 1
MLP 0.972 1 0.986
k-NN [22] 0.83 0.97 0.9
We have also experimented with different outer window size for the fall de-
tection using four different classifiers. In the original setting, the outer window is
20 (See Figure 6), meaning that each fall detection occurs in a 2-second window,
Fall Detection 13
as the Grid-Eye is running at 10Hz. In Table 8 and 9, we show fall detection
result with outer window being 30, we see that the performances are consid-
erably lower for all four classifiers (the Median filter has been used in these
experiments). We interpret these results as: since fall is an instantaneous event,
increasing the window size does not help improving the detection performance.
Table 8: Fall Detection Performance with 3-seconds detection window (Falls are
perpendicular to the Grid-Eye).
Precision Recall F1
GRU-ATT 0.632 0.861 0.729
GRU 0.731 0.833 0.779
LSTM-ATT 0.695 0.888 0.780
LSTM 0.82 0.888 0.853
Table 9: Fall Detection Performance with 3-seconds detection window (Falls are
parallel to the Grid-Eye).
Precision Recall F1
GRU-ATT 0.7 0.972 0.813
GRU 0.809 0.944 0.871
LSTM-ATT 0.875 0.972 0.921
LSTM 0.947 1 0.972
5 Conclusion
Fall is a major health threat to the elderly. In event of fall, it is urgent to pro-
vide immediate treatment to the injured people. In this paper, we present a fall
detection system using Grid-Eye infrared array sensor. Due to its low spatial res-
olution, infrared array sensor incurs little privacy intrusion and can be deployed
to sensitive areas such as washrooms, which are known to be fall-prone. For data
processing, we have taken a two-step approach: (1) pre-processing data filtering
and (2) machine learning classification with neural networks. For filtering, we
have experimented with Wavelet, Gaussian and Median filters. For classification,
we have experimented with several deep learning models, including multi-layer
perceptrons, LSTM and GRU. To evaluate our approaches, we have created a
dataset containing over 300 falls in multiple configurations. We then compare
our work with an existing work using the same infrared array sensor but with
different classification techniques and show significantly improved classification
accuracy. In the future, we would like to (1) perform in depth theoretical study,
including computational complexity analysis, of the proposed methods, (2) de-
ploy our system to nursing homes for real-world experiment and (3) explore fall
detection with other ambience sensor systems and deployment configurations.
14 Fan et al.
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