Novelty Detection in Physical Activity
Bernardo Leite, Amr Abdalrahman, Jo
ao Castro, Julieta Frade, Jo
ao Moreira and Carlos Soares
Department of Informatics Engineering, Faculty of Engineering, University of Porto,
R. Dr. Roberto Frias, 4200-465, Porto, Portugal
Artificial Intelligence, Machine Learning, Activity Recognition, Novelty Detection.
Artificial Intelligence (AI) is continuously improving several aspects of our daily lives. There has been a
great use of gadgets & monitoring devices for health and physical activity monitoring. Thus, by analyzing
large amounts of data and applying Machine Learning (ML) techniques, we have been able to infer fruitful
conclusions in various contexts. Activity Recognition is one of them, in which it is possible to recognize and
monitor our daily actions. The main focus of the traditional systems is only to detect pre-established activities
according to the previously configured parameters, and not to detect novel ones. However, when applying
activity recognizers in real-world applications, it is necessary to detect new activities that were not considered
during the training of the model. We propose a method for Novelty Detection in the context of physical
activity. Our solution is based on the establishment of a threshold confidence value, which determines whether
an activity is novel or not. We built and train our models by experimenting with three different algorithms and
four threshold values. The best results were obtained by using the Random Forest algorithm with a threshold
value of 0.8, resulting in 90.9% of accuracy and 85.1% for precision.
There are many applications which are required to de-
cide whether a new observation belongs to the same
distribution as existing observations (inlier), or should
be considered as different (outlier)
. We, therefore,
make this important distinction:
Outlier Detection: Aims to detect outlier(s),
i.e., an observation which deviates so much from
the other observations as to arouse suspicions
that it was generated by a different mechanism
(Hawkins et al. (2002)).
Novelty Detection: Aims to detect novel classes,
i.e., classes that were not seen in the training set.
Typically a classification problem.
Novelty detection is the identification of new or un-
known classes that a machine learning system is not
aware of during training (Miljkovi
c, 2010). More-
over, this can also be defined as the task of discover-
ing that test data differ in some respect from the data
available from the training step. The main goal is to
try to recognize/identify these new observations that
are different (or not consistent) with the original train-
ing data.
Novelty detection has its impact in many practi-
cal and real-life applications regarding different do-
mains. In concrete, these application areas can be di-
vided into 6 distinct categories: (a) Information and
Technology (IT) Security, (b) Industrial Monitoring,
(c) Image Processing and Video Surveillance, (d) Text
Mining, (e) Sensor Networks and, finally, (f) Health-
care Informatics and Medical Diagnosis. Research in
IT Security systems mainly includes fraud detection
to avoid malicious programs and the identification of
intrusions (Helali, 2010). Studies in Industrial Mon-
itoring try to identify deterioration in industrial as-
sets as early as possible (Surace and Worden, 2010).
The use of novelty detection techniques for Image
and Video (Markou and Singh, 2006; Yong et al.,
2013) allow us to identify novel objects in images and
video streams. Regarding Text Mining, the goal is to
detect novel topics, new stories, and events (Zhang
et al., 2005). Research in Sensor Networks (Has-
san et al., 2011) mainly focuses on discovering faults
and malicious attacks on these networks. Lastly, re-
search in Healthcare Informatics and Medical Diag-
nosis (Clifton et al., 2011) has great importance since
it helps to identify clinically relevant changes in pa-
tient health. Thus, it facilitates a more timely inter-
vention by doctors.
By studying Novelty Detection for Physical Activ-
Leite, B., Abdalrahman, A., Castro, J., Frade, J., Moreira, J. and Soares, C.
Novelty Detection in Physical Activity.
DOI: 10.5220/0010254908590865
In Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) - Volume 2, pages 859-865
ISBN: 978-989-758-484-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
ity we will process data records from vital signs. For
such, we use the PAMAP2 dataset (Reiss and Stricker,
2012a,b), from UCI Machine Learning Repository
This dataset contains data of 18 different physical ac-
tivities (such as walking, cycling, playing soccer, etc),
performed by 9 subjects wearing 3 inertial measure-
ment units (IMU) and a heart rate monitor. We select
three of the available activities to train our classifi-
cation models. For that, we experiment with three
algorithms: Decision Tree, Random Forest, and K-
Nearest Neighbors (k-NN). To detect novel activities,
the main idea is to insert new activities (the original
three plus two) into the test data (that were not in the
training data) and, by comparing the model’s predic-
tion confidence with a certain threshold (tested with
0.5, 0.6, 0.7 and 0.8), classify them as a novel or not.
We emphasize the fact that we propose this approach
in a little-explored context of novelty detection, which
is Activity Recognition.
The rest of the paper is structured as follows. Sec-
tion 2 presents previous research in Novelty Detec-
tion and Activity Recognition. Section 3 describes in
detail our methodology and Section 4 outlines the re-
sults from evaluation. Conclusions and future work
are exposed in Section 5.
One of the most significant reviews in the con-
text of novelty detection was conducted by Pimentel
et al. (2014). They provided a structured investiga-
tion of novelty detection approaches that have ap-
peared in the Machine Learning literature. These ap-
proaches fall into five different categories: probabilis-
tic, distance-based, reconstruction-based, domain-
based, and information-theoretic techniques. We use
that study as a reference to the methodologies that we
will now synthesize.
Starting with the probabilistic techniques (Clifton
et al., 2012; Hazan et al., 2012), these mainly use
probabilistic methods that involve a density estima-
tion of the normal/usual/standard class. Also, they
consider that low-density areas from the training set
indicate that these areas have a low probability of in-
cluding normal objects. Distance-based techniques
are related to the Nearest Neighbor approach (Ghot-
ing et al., 2008) in which a certain point is consid-
ered as a novelty if its distance to a k-NN neighbor
surpasses the predefined threshold. Also, distance-
based techniques include the concept of clustering
analysis (Viegas et al., 2018) where is the assump-
tion that normal data belong to dense (and large)
clusters, whereas novel objects don’t belong to any
of these clusters. By using reconstruction-based ap-
proaches (Marchi et al., 2015; Xia et al., 2015), the
main idea is to map the unusual data using the train-
ing model and then, the error (reconstruction error)
between the regression target and the values that are
actually observed causes a higher novelty value (or
score). Domain-based approaches (Le et al., 2010;
Peng and Xu, 2012) have the main goal of describ-
ing and characterize a domain in which is the nor-
mal/usual data is present, by creating a boundary
around the normal/usual class. Regarding Informa-
tion Theoretic techniques (Filippone and Sanguinetti,
2010; Wu and Wang, 2011), these are methods that
compute information content from the training data
by using information-theoretic measures, e.g. en-
tropy, relative entropy, and kolmogorov complexity.
The main idea is that unusual (or novel) data signifi-
cantly alter the information content from the dataset.
More recently, new studies have emerged with
new advances in the field of Deep Learning (DL). In
this context, Mello et al. (2018) propose a novelty de-
tector based on Stacked AutoEncoders (SAE) to de-
tect unknown arriving patterns in a passive sonar sys-
tem. Sabokrou et al. (2018), inspired by the success
of Generative Adversarial Networks (GANs), propose
an end-to-end architecture for one-class classification.
The authors make use of two deep networks: One of
them works as the novelty detector, while the other
supports it by enhancing the inlier samples and dis-
torting the outliers. Finally, in the scope of object
recognition, Lee et al. (2018) have studied informa-
tive novelty detection schemes based on a hierarchi-
cal classification framework. They propose top-down
and flatten methods, and their combination as well.
The authors claim that one of the essential ingredients
of their methods are confidence-calibrated classifiers
for modeling novel classes.
As previously mentioned, our study is in the scope
of Activity Recognition. In this context, Sprint et al.
(2016) formalize the problem of unsupervised Phys-
ical Activity Change Detection (PACD). The authors
compare the abilities of three change detection algo-
rithms from the literature and one proposed algorithm
to capture different types of changes as part of PACD.
Rossi et al. (2018) present a two-step framework im-
plementing a strategy for the detection of Activities
of Daily Living (ADL) that are divergent from normal
ones. This strategy uses a deep learning technique to
determine the most probable ADL class related to a
certain action and a Gaussian Mixture Model to com-
pute the likelihood that the action is normal or not.
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
In this section, we present our approach that fol-
lows roughly the CRISP-DM methodology (Shearer,
2000). First, we start with Data Understanding (Sec-
tion 3.1), where we describe and explore the acquired
data from the chosen dataset. Still at this step, we ver-
ified the quality of the same data. Then, we proceed
to Data Preparation (Section 3.2) where we apply the
necessary pre-processing steps, namely the selection,
cleaning, and integration of our data. In Modelling
and Novelty Detection (Section 3.3), we explain the
modeling selection technique, in which we build our
model and describe its parameter settings. Finally, we
present the applied technique to detect novel activi-
ties. To accomplish all these steps we made use of the
3.1 Data Understanding
As stated previously, we use the information from the
PAMAP2 dataset to train and build our models. It
holds data of 18 distinct corporal activities performed
by a group consisting of 8 males and 1 female with
ages between 24 and 32. The dataset comprises the
subsequent activities with the corresponding activity
IDs: 1 (lying), 2 (sitting), 3 (standing), 4 (walking), 5
(running), 6 (cycling), 7 (nordic walking), 9 (watch-
ing TV), 10 (computer work), 11 (car driving), 12
(ascending stairs), 13 (descending stairs), 16 (vac-
uum cleaning), 17 (ironing), 18 (folding laundry), 19
(house cleaning), 20 (playing soccer), 24 (rope jump-
ing) and 0 (other/transient activities).
Also, within this dataset we were able to retrieve
a total of 2,872,533 examples (rows) equivalent to 10
hours of information along one data file per individ-
ual, comprising the following 54 columns per row:
1: timestamp (s);
2: activityID;
3: heart rate (bpm);
4-20: IMU hand;
21-37: IMU chest;
38-54: IMU ankle.
The IMU sensory data contains the following
columns attributes:
1: temperature (ºC);
2-4: 3D-acceleration data (ms
), scale: ±16g;
5-7: 3D-acceleration data (ms
), scale: ±6g;
8-10: 3D-gyroscope data (rad/s);
11-13: 3D-magnetometer data (µT);
14-17: orientation (invalid in this data collection).
In terms of data quality, three situations for future
treatment were found. Firstly, the data examples con-
cerning activity ID 0 mainly cover transient activities
between performing different activities, e.g. going
from one location to the next activity’s location. For
this reason, it is necessary to discard these examples.
Secondly, 90% of the heart rate attribute values were
missing. Also, there is an average of 8,528 missing
values for the remaining attributes. Third and last, the
dataset is not perfectly balanced, e.g, 238,753 exam-
ples for activity ID 4 vs 98,192 examples for activity
ID 5. In the next Section 3.2, the goal is to explain
what we did to deal with the referred situations.
3.2 Data Preparation
To solve data-related problems, we start by removing
examples from activity ID 0 since they were not ac-
tual activities but yet transient ones. Regarding the
missing values, we completely removed the heart rate
attribute (ID 3) since a substantial part (90%) of the
values were missing. Timestamp (ID 1) was also ex-
cluded since we did not consider it as a relevant at-
tribute for the classification model. Orientation (IDs
14-17) was dropped since it was invalid in this data
collection. The IMU (IDs 2-4 and 5-7) columns were
removed as they were highly related to acceleration.
After that, the examples that contained missing val-
ues were dismissed considering that they only repre-
sent 0.5% of the whole dataset. It was not necessary
to create new attributes or change/modify the existing
At the end of this phase, we are left with a total
of 1,929,578 examples (from the 2,872,533 examples
mentioned in Section 3.1), each of them containing
16 attributes (or features). In Section 3.3 we will ex-
plain the modeling and novelty detection techniques,
starting by defining how many of these last examples
are used for training and testing.
3.3 Modelling and Novelty Detection
In Figure 1 it is possible to observe our experimental
setup scheme that represents the modeling and nov-
elty detection processes. Throughout this section, we
describe all the steps involved.
Previously, in Section 3.2, from the (1) Data
Preparation phase, we obtained a total of 1,929,578
examples to be used in the following steps. Thus,
from (2) Data Splitting, we separate the training and
test data by applying a split size value of 0.5 (50% for
each one) using the stratified sampling type. Stratified
Novelty Detection in Physical Activity
Figure 1: Modeling and Novelty Detection - Experimental Setup.
sampling builds random subsets and ensures that the
class distribution in the subsets is the same as in the
whole example set. Through this splitting, we have
964,789 examples for training and another 964,789
examples for testing. We decided to create our mod-
els to perform multiclass classification. We consider
the first three activities from the example set as our
classes: lying (ID 1) sitting (ID 2), and standing (ID
3). So, we use the (3) Filtering (Training Data) to
filter only the examples corresponding to ID 1, 2, or
3. This gives a total of 283,677 examples for training.
On the other hand, we use (4) Filtering (Test Data)
to filter the examples corresponding to ID 1, 2, 3 but
also two other activities: walking (ID 4) and running
(ID 5). This gives a total of 447,242 examples for
testing. This then allows us, when later applying the
novelty detection method, to classify the ve activi-
ties as a novel or not (walking and running should be).
In order to adjust the dimension and class distribution
of our dataset, we use (5) Sampling (Training Data)
and (6) Sampling (Test Data) to obtain the following
final examples:
Number of Training Examples (1,800): lying
(600), sitting (600) and standing (600);
Number of Testing Examples (1,250): lying
(250), sitting (250), standing (250), walking
(250), and running (250).
To train the models, we experimented with three dif-
ferent algorithms:
A Decision Tree: This uses a tree-like model of de-
cisions and their possible consequences, including
chance event outcomes, resource costs, and util-
B Random Forest: This is an ensemble of a certain
number of trees (random forest), specified by the
number of trees parameters. These trees are cre-
ated/trained on bootstrapped sub-sets of the exam-
ple set provided;
C k-NN: This is based on comparing an unknown
example with the k training examples which are
the nearest neighbors of the unknown example.
Regarding the Decision Tree, for the criterion on
which attributes are selected for splitting, we use gain
ratio that adjusts the information gain for each at-
tribute to allow the breadth and uniformity of the at-
tribute values. Also, we set the value of 100 for the
maximal depth. This parameter is used to restrict the
depth of the decision tree. The depth of a tree varies
depending upon the size and characteristics of the ex-
ample set. As for Random Forest, we set the value
of 100 for the number of trees. This parameter spec-
ifies the number of random trees to generate. For the
criterion on which attributes are selected for splitting,
we use information gain in which the entropies of all
the attributes are calculated and the one with the least
entropy is selected for the split. Finally, we select 50
as being the maximal depth of the trees. Concerning
k-NN, we set the value of 5 for finding the k train-
ing examples that are closest to the unknown example
(this is the first step of the k-NN algorithm).
From (7) Training we are training our data using
each of the three algorithms. Thus, we produce three
different models after training the data with each of
the referred algorithms. Before assessing the ability
of the approach proposed here for novelty detection
(on the test set), we need to understand how good the
model is at predicting the classes it was trained with.
To do this, we estimate the predictive performance of
the algorithms using cross-validation (10 folds) on the
training data. For each algorithm, the cross-validation
produces 10 iterations, with each of the 10 subsets
used exactly once as the test data. The 10 results from
the 10 iterations are averaged to produce a single es-
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
timation. The performance results can be analyzed in
Table 1. These results represent the average perfor-
mance of the 10-fold cross-validation for each algo-
Table 1: Performance Results: Accuracy (Acc), Macro-
average Precision (P), Macro-average Recall (R) and
Macro-average F1-Score (F1) for each algorithm, consid-
ering the multiclass classification.
Measure (%)
Acc P R F1
94.4 94.5 94.4 94.4
96.7 96.7 96.7 96.7
k-NN 95.0 95.1 95.0 95.0
We recall that these models were trained consid-
ering only three classes (lying, sitting and standing).
When we apply each model to the new data, two new
classes (walking and running) are mixed with the oth-
ers. Therefore, with (8) Model Application we are
now predicting whether a given example belongs to
one of the three classes used in the training data. For
each prediction (1, 2 or 3), the model presents the con-
fidence value [0-1] for its respective decision. Also
note that each example, in addition to prediction and
confidence, it has the activityID attribute that repre-
sents the correct/real activity (1, 2, 3, 4 or 5) for that
given example. In these conditions, everything is pre-
pared for the next step, (9) Novelty Detection. The
Novelty detection method can be enumerated with
three essential steps, they are:
1. Create novelty attribute which will be true or
false based on this condition:
(if confidence<threshold, then novelty=true, else
2. Set the novelty attribute with the target role of
prediction label;
3. Create isNovel attribute from activityID using
this condition:
(if activityID == (1 or 2 or 3), then isNovel=false,
else isNovel=true);
From item 1 the idea is to create a new attribute (nov-
elty) that can take only two values: true (it is novel)
or false (it is not novel). For that, we define the re-
ferred condition. The threshold acts as a comparison
value that will define whether the confidence value for
a given prediction corresponds to a navel/non novel
activity. We experimented with four distinct thresh-
old values: 0.5, 0.6, 0.7 and 0.8. Also, note that we
now define the novelty attribute with the target role of
prediction label (item 2). Finally, from item 3 we are
creating the isNovel attribute, setting it as false (not
novel) when activityID is equal to 1, 2 or 3 and with
the value of true (is novel) when activityID is equal
to 4 or 5. This is done since we now want to compare
whether the predictions established in the new novelty
attribute (that can be true or false) correspond to the
truth, by comparing it with what is defined in isNovel
attribute (also can be true or false). In other words,
we just created the (10) Performance Test, which is
our test environment for novelty detection.
In Section 3.3 we explain the modeling mechanism
and the novelty detection method. For the latter, we
end up with two important attributes: isNovel and
novelty. novelty is our prediction label, in which we
predict whether a given example is novel or not (true
or false). On the other hand, isNovel is the attribute
that contains the correct answer, that is, whether the
activity is novel or not (true or false). By compar-
ing these two values for each example, we are able to
evaluate the performance for novelty detection. The
results are shown in Table 2.
Table 2: Performance Results: Accuracy (Acc), Precision
(P), Recall (R) and F1-Score (F1) for each Algorithm (Alg)
and Threshold (T), considering our novelty detection tech-
Alg. T.
Measure (%)
Acc P R F1
0.5 60.0 50.0 0.60 1.2
0.6 59.9 47.8 2.2 4.2
0.7 59.8 47.7 4.2 7.7
0.8 59.8 57.7 4.2 7.8
0.5 76.2 95.6 42.6 58.9
0.6 85.7 92.1 70.2 79.7
0.7 89.9 90.5 83.6 86.9
0.8 90.9 85.1 93.8 89.3
0.5 63.3 93.6 8.8 16.1
0.6 70.2 88.0 29.4 44.1
0.7 74.5 83.4 45.2 58.6
0.8 80.2 84.9 61.6 71.4
We consider that reasonable accuracy values are
achieved for the Random Forest and k-NN, which
are increased as the threshold values increase, except
when using the Decision Tree which does not seem to
improve its correctly predicted observations ratio. In
general, the best results go for Random Forest, when
Novelty Detection in Physical Activity
using a threshold value of 0.8. In fact, this algorithm
has some advantages such as reducing overfitting and
being extremely flexible. However, Random Forests
(depending on the dataset) are time-consuming.
Random Forest presents the best recall value
(93.8%). If the scope of this research was related to
health-related systems (e.g, a person has a disease or
not), the recall would be a better measure than preci-
sion. That is, it is far preferable to not miss any person
with the disease even if that means “signaling” some
patients as having a disease that actually do not have
it. As here we study the detection of new physical ac-
tivities, false-negatives are less of a concern. Then,
precision is preferable here.
We highlight that the precision values for Random
Forest and k-NN are indeed very close (85.1% and
84.9%). This means that they both are good at detect-
ing novelty activities of all activities that were pre-
dicted as a novelty.
In order to put our research in retrospect, we recall
that our motivation is to study novelty detection in
the context of activity recognition and be able to de-
tect new activities. To achieve this goal, we propose a
method that involves experimenting with three differ-
ent algorithms by creating three classification models
in a example set that contained three classes (or activ-
ities). We apply these models in a test set that con-
tained five classes, two of which were new, not being
present in the original training set. When comparing
the model’s confidence predictions with four thresh-
old values, we are able to detect how many of these
five activities were in fact novel (or not). We now
point some general observations.
Firstly, by increasing the threshold value, it means
that more activities are classified as a novel, which
leads to higher accuracy, recall but in a lower pre-
cision. Furthermore, lowering the threshold means
that fewer activities are classified as a novel, which
leads to lower recall and higher precision. Finally, by
choosing a threshold bigger than 0.8 would make it
possible to detect more novel activities. However, it
would make the model less precise. The best results
go for the Random Forest algorithm with a threshold
value equal to 0.8. However, k-NN is not far behind,
as both of them achieve a very close precision.
For future work, it would be relevant to study a
mechanism that would allow us to divide novel activ-
ities into different categories. Although we are detect-
ing if these examples are novel or not, it does not nec-
essarily mean that they belong to the same activity.
Another improvement would be using a clustering-
based technique to take into account outliers, to avoid
classifying an example as a novelty activity since it
also is a detached occurrence. Besides that, the use of
the latest deep learning techniques can help improve
the performance of novelty detection.
To sum up, we see a promising outlook for this re-
search area in the future, as novelty detection can help
us by recognizing and monitor our daily actions with
the fruitful purpose of providing useful information.
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