Skeleton-based Human Action Recognition
A Learning Method based on Active Joints
Ahmad K. N. Tehrani, Maryam Asadi Aghbolaghi and Shohreh Kasaei
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Keywords:
Human Action Recognition, Active Joints, Hidden Marcov Model (HMM), Skeletal Human Body Model.
Abstract:
A novel method for human action recognition from the sequence of skeletal data is presented in this paper.
The proposed method is based on the idea that some of body joints are inactive and do not have any physical
meaning during performing an action. In other words, regardless of the subjects that perform an action, for
each action only a certain set of joints are meaningfully involved. Consequently, extracting features from
inactive joints is a time-consuming task. To cope with this problem, in this paper, only the dynamic of active
joints is modeled. To consider the local temporal information, a sliding window is used to divide the trajectory
of active joints into some consecutive windows. Feature extraction is then applied on all windows of active
joints’ trajectories and then by using the K-means clustering all features are quantized. Since each action has
its own active joints, in this paper one-vs-all classification strategy is exploited. Finally, to take into account
the global motion information, the consecutive quantized features of the samples of an action are fed into the
hidden Markov model (HMM) of that action. The experimental results show that using active joints can get
96% of maximum reachable accuracy from using all joints.
1 INTRODUCTION
Human action recognition is one of the most widely
studied research topics in computer vision (because it
has been massively applied in many real-world appli-
cations like health care systems, video analysis, and
so forth). In the past decades, research of human
action recognition has mainly focused on recogniz-
ing actions from videos captured by traditional visible
light cameras. In the recent years, it has continued to
be a hot area of research in computer vision thanks to
the emergence of low-cost depth sensors like Kinect.
It has some advantages over the visible light cameras
(Xia and Aggarwal, 2013). First, the 3D structure in-
formation of the environment can be obtained, which
provides more discriminative knowledge. Next, the
depth data is independent of environment brightness
and it can capture depth images even in total dark-
ness. Moreover, in (Shotton et al., 2013), a real-time
method is proposed which estimated human joints 3D
positions based on an algorithm that extracts body
parts from the depth data.
Almost all skeletal-based approaches extract fea-
tures from all body joints. It is noteworthy that most
of the time, some joints are not involved; for instance,
neither are lower body joints effective during hand-
Figure 1: General idea of proposed method. The trajectories
show the position of each joint during the action.
waving action nor is hand’s motion effective during
the ”forward-kick” action. Therefore, for each action,
joints can be categorized into two groups of active and
inactive joints. Active joints for one class of action are
the joints which are dynamic during performing that
action and inactive joints are the ones that do not have
any significant motions. Regarding this fact, a novel
method is proposed in this paper which uses only ac-
tive joints of each action for feature extraction and
classification of human actions (Fig. 1).
It seems that humans use the idea of weighting
most important joints to recognize each action. For
example, during viewing an unknown action which
K. N. Tehrani A., Asadi Aghbolaghi M. and Kasaei S.
Skeleton-based Human Action Recognition - A Learning Method based on Active Joints.
DOI: 10.5220/0006134903030310
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 303-310
ISBN: 978-989-758-226-4
Copyright
c
 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
303

has some effective motions on hands and no effec-
tive movements on feet, humankind ensures that per-
formed action does not belong to foot-based action
categories such as ”ball-shooting”. In addition, if one
knows that a certain action has been performed by
moving hands, for recognizing the performed action,
the attention should be focused on the kind of hands’
motion rather than feet’s motion, which have been in-
active in this case. This observation intuitively shows
that the proposed method can work thoroughly.
Action recognition needs two main parts: feature
extraction and learning method. There are different
types of feature extraction methods some of which are
presented in Section 2. Moreover, to learn extracted
feature vector, two main methods are used: first, using
a multi-class classifier to learn all of feature vectors
and the other, using one-vs-all classifier for each class
beside a unit which selects the best class based on the
results of classifiers.
In this paper, the main goal is representing a learn-
ing method which is able to manage different kinds of
active joints per action. To achieve this, based on what
was stated before about the behavior of humankind
in recognizing actions, two different approaches are
presented, one for training and one for testing. In
the training phase, firstly, by using a proposed unit,
the most active joints for each class of actions are se-
lected. Then, after extracting features from each win-
dow of active joints’ trajectory and constructing the
full feature vector of each window, all feature vectors
are quantized by using a clustering algorithm. Finally,
a one-vs-all classifier (HMM) is learned for each class
of actions by using the samples of that class. In the
testing phase, firstly, active joints of each sample are
selected. Then, the full feature vector is converted to a
quantized sequence (by using the center of each clus-
ter), It is then fed into the classifier of probable action
(explained in Section 3). Finally, a decision is made
from yielded certainty factor of probable classes.
The main contributions consist of four parts: First,
the idea of using few active joints to recognize the ac-
tion instead of using all joints. Second, an approach
has been proposed to select active joints from a sam-
ple of an action. Third, a method to learn the training
data has been presented based on differences between
active joints involved in actions of different classes.
Fourth, an approach has been proposed based on the
effects of active joints on an action class.
2 RELATED WORK
The recent advent of low-cost and easy-operation
depth sensors like Kinect have received a great deal
of attention from researchers to reconsider problems
such as activity recognition using depth images in-
stead of color images. Generally, recent approaches in
3D action recognition can be divided into two groups
based on the input data of depth data or skeleton in-
formation. 3D skeletal information is at a higher level
of semantic rather than depth information which has
less data to be processed. As a result, the skeleton-
based descriptor is more discriminative for presenting
human actions.
The 3D skeletal information can be obtained in
different ways. The most accurate ones are provided
by motion capture (F. De la Torre and Macey., 2009)
which are typically captured using optical sensing of
markers placed on specific positions of human body.
Although these data are more reliable and less noisy
than the other sources of skeletal data, it is so difficult
to produce such data. The 3D joints locations can be
also estimated accurately from depth map by (Shot-
ton et al., 2013) in real-time. This algorithm brings
many benefits to numerous tasks in computer vision
especially in action recognition. Many approaches
have been proposed to recognize human actions us-
ing 3D joints locations. There are some kinds of fea-
tures that can be extracted from joints; i.e., raw-based,
displacement-based, and orientation-based features.
In (Hussein et al., 2013), the statistical covariance
of raw 3D joints positions (Cov3DJ) is used as the
feature. Some other methods like (Wei et al., 2013)
have formed the trajectories using the raw positions
of joints and then extract features from the trajecto-
ries. Temporal information can be processed in two
ways for trajectory-based methods. Some approaches
exploit histogram of spatio-temporal extracted fea-
tures and some other approaches use temporal analy-
sis tools like HMM, self-similarity matrix (SSM), and
dynamic time warping (DTW) in (Xia et al., 2012),
(Junejo et al., 2011), and (Vemulapalli et al., 2014),
respectively. In (Xia et al., 2012), HOJ3D is proposed
as a descriptor for action recognition from skeletal
data. To make this method invariant to rotation, a
special spherical coordinate is defined based on 3D
location of joints, such that the hip joint is used as the
center of coordinate and the vector from left hip to
right hip is defined as the horizontal axis.
Displacement-based approaches usually use two
kinds of displacements, spatial and temporal. Spa-
tial features like (Yang and Tian, 2012; Luo et al.,
2013) are the ones which are extracted from all joints
locations in just one frame like joints pairwise dis-
tances and the temporal features like (Yang and Tian,
2012) are the ones which measure the movement of
one joint during the time, for instance, joints velocity
and acceleration.
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304

Oriented-based methods exploited the joints ori-
entation as their features. These features can also be
spatial or temporal. Spatial oriented-based features
such as (Gu et al., 2012) are the orientation of dis-
placement vectors of a pair of human skeletal joints in
one frame and the temporal features such as (Boubou
and Suzuki, 2015) are the ones that compute differ-
ence between orientations of each joint during the
time. In (Eweiwi et al., 2014), three kinds of fea-
tures are extracted from skeletal data of each joint in
all frames: 3D histograms of joint location, 2D his-
tograms of velocity vectors, and 2D histogram of the
cross product of location vector and velocity vector.
3 PROPOSED METHOD
Each recognition problem needs to use two main sec-
tions: feature extraction and learning. In this paper, A
novel way of learning is designed which can discrimi-
nate between different joints with different amount of
activity.
When an action is performed, to recognize an ac-
tion, one focuses on joints which have the most ef-
fective motions. For instance, when someone drinks
water, if you concentrate on the feet’s motion, you
may not find out which action is performed. There-
fore, the only way to recognize each action is to focus
on its effective joints (called active joint in this pa-
per). In addition, to recognize each action, one evalu-
ates how active joints move during the action period.
Ultimately, when an action is performed, one can rec-
ognize it by answering two key questions:
1. Which joints are involved in that action?
2. How does each joint move during the action time?
Answering these two questions can lead to recogniz-
ing each action.
The expression active joint must be clarified be-
fore starting to explain the proposed method. The ac-
tive joints are the joints that have the most effective
movements during the action. To measure the amount
of activity of each joint, an energy function is defined
on each joint, given by
e
i
=
∑
t∈T
k P
i
(t) − P
i
(t −1) k
2
(1)
where e
i
is the energy of joint i which P
i
(t) relates to
its position at frame t of total action time (T ).
To make this energy function comparable with
other samples, a normalization factor is applied on it.
Joints’ energies are normalized by
E
i
=
e
i
∑
j
e
j
(2)
(a)
(b)
Figure 2: (a) Joints number in skeleton model. (b) Effect of
applying threshold (α = 0) on normalized joint energy for
”hand-waving” action.
where E
i
is the normalized energy of joint i which has
been scaled to the summation of all joints ( j) energy.
Normalized joint energy for the action ”hand-
waving” is illustrated in Fig. 2(b) (based on skeleton
model in Fig. 2(a)). As is expected, the amount of
joint energy for joints 9, 11, and 13 (related to the left
hand) is greater than other joints’ energy.
Proposed learning algorithm is inspired from the
idea introduced above. To treat in this manner (mag-
nifying the effect of active joints on recognition), two
main sections are required: one for training and an-
other for testing. In the following, the functionality of
each part is explained.
3.1 Training Phase
This part is designed based on using active joints. The
main idea of this part is illustrated in Fig. 3. The
block diagram of training phase illustrates that the
proposed method firstly finds the set of active joints
(by the ”Active Joint Selector” unit) for each class
of training data. Then, for each training sample, fea-
tures are extracted from windows which are sliding
on the trajectory of active joints. After extracting fea-
tures from windows of trajectory of active joints, they
are concatenated together (for inactive joints, a fixed
set of numbers is placed) to construct a main feature
vector for each window. By applying a vector quan-
tization method (K-means) on all main feature vec-
Skeleton-based Human Action Recognition - A Learning Method based on Active Joints
305

Figure 3: Block diagram of training phase.
tors of samples of all classes, they will be converted
to a quantized sequence. Finally, the quantized se-
quence of samples of each class are used for learn-
ing the HMM (as a one-vs-all classifier) for that class.
Using HMM in proposed method causes to manage
effect of time that is eliminated from feature vectors
by using the feature extraction method which are sav-
ing spatial information.
Fig. 3 shows that working with this kind of learn-
ing method needs some subsequent processes. These
will be explained in the following subsections.
3.1.1 Active Joint Selector
This part of the proposed method must select the most
active joints. The amount of joint activity is rep-
resented by the normalized joint energy which was
stated before.
To select some of the joints as active joints from
the normalized energy of each sample, an adaptive
threshold must be applied on them. The adaptive
threshold is given by
thr = mean{E
1,...,I
} + α ∗ std{E
1,...,I
} (3)
where E
1,...,I
and α are the normalized energy of
skeleton model of each action sample and the align-
ment factor (which can change the accuracy of select-
ing active joints), respectively.
Therefore, by applying this threshold on the joint
energy of skeleton model, active joints can be se-
lected. Result of selecting active joints for instances
of the action hand-waving is illustrated in Fig. 2(b).
What was stated before was a method to select ac-
tive joints of each sample. However, in the training
phase, selecting active joints must be more general
than each sample, due to the noise which has been
distributed on each sample joint energy. To select ac-
tive joints of each class, declared threshold must be
applied on the average of normalized energy of all in-
stances of each class.
3.1.2 Feature Extraction
The main idea of this paper is its learning method.
However, to test the proposed learning method, an
approach for feature extraction is required. Feature
extraction method which is used during testing this
learning method is stated in the following.
To extract features, firstly, a sliding window af-
fects the trajectory of selected active joints. Then, a
feature extraction method is applied on each window
of trajectories. The important and remarkable point of
this type of feature extraction is that extracted features
describe local characteristics of each joint’s trajectory.
In other words, the proposed method attempts to de-
scribe each trajectory by a set of local features instead
of a global extracted feature vector which describes
the whole trajectory. To describe local characteristics
of trajectories, the feature extraction method which
has been applied on each slided window of trajecto-
ries, consists of some parts:
1. Multi-level wavelet decomposition: This trans-
form (Primer et al., 1998) can break each signal
into two parts: approximation and detail. Coeffi-
cients of this transform function which are applied
on the window of joint trajectory are used as part
of feature vector.
2. Discrete cosine Transform: This transform can
convert a finite signal into sum of cosine functions
with different frequencies.
3. Fast Fourier transform: The magnitude and phase
of each window of trajectory of the joint is used
as part of feature vector.
4. Displacement of joint w.r.t. joint ”torso” for each
frame of applied window on the joint trajectory
(Rahmani et al., 2014).
5. Cov3DJ: This feature vector was introduced in
(Hussein et al., 2013). In this paper, Cov3DJ is
modified by using just one joint instead of all.
6. Displacement of joint at the first frame of window
w.r.t. the next frame.
7. Difference between maximum and minimum of
joint trajectory located in each window (Rahmani
et al., 2014).
After extracting feature vector from each window
of active joints, now it is time to generate the main and
large feature vector which describes a period of time
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306

Figure 4: Block diagram of test phase.
of action instance (related to each widow). To con-
struct the main feature vector of each period of time,
feature vector of each active joint and a set of fixed
numbers for each inactive joint must be concatenated
to each other.
3.2 Test Phase
As asserted before, this method is trying to add the
concept of active joints in each main part. This con-
cept also shows its effect on the test phase as follows.
The main idea of the test phase is illustrated in
Fig. 4. According to that block diagram, for each
test instance, firstly, active joints are selected by the
unit which has been previously defined (Active Joint
Selector). Next, the similarity between test active
joints and active joints of each class are evaluated. In
this situation, two cases may occur. If they were not
acceptably similar (like active joints between ”hand-
waving” and ”ball-shooting”), the certainty of not be-
longing to that class is in a high level. In contrast,
If their similarity is acceptable, initially features are
extracted from active joints and then they will be con-
catenated to each other (instead of inactive joints, a
fixed set of numbers is placed). Next, by comparing
extracted feature vectors with center of each cluster
(obtained by K-means) quantized sequence goes to
be evaluated with pre-learned HMM. The output of
HMM will be a certainty factor which represents the
similarity of test data and The HMM’s class.
The similarity unit works based on comparing the
most active joints of test action and active joints of
each class. If they have more mutual joints than a
fixed percentage of active joints of the class being
compared, their similarity is considered acceptable.
Figure 5: Input of ”Decision Fusion”.
Eventually, the ”Decision Fusion” unit is required
to choose the best label for test data. Input of this unit
is similar to the one illustrated in Fig. 5. According to
the functionality of this unit, an action label must be
selected from relevant classes by selecting the class
with maximum certainty factor.
4 EXPERIMENTAL RESULTS
To assess the proposed method, two datasets are used:
MSR Action 3D (Li et al., 2010) and UTKinect (Xia
et al., 2012). In the following, the performance analy-
sis on these datasets is given based on the parameters
which have been selected experimentally.
4.1 MSR Action 3D
MSR action 3D is a gaming action dataset with depth
sequences. It includes 20 actions: high arm wave,
horizontal arm wave, hammer, hand catch, forward
punch, high throw, draw x, draw tick, draw circle,
hand clap, two hand wave, side boxing, bend, forward
kick, side kick, jogging, tennis swing, tennis serve,
golf swing, and pick up and throw. Each action is
performed 2 or 3 times by 10 subjects. The frame rate
is 15 fps and its resolution is 320×240.
To test the proposed method on MSR action 3D,
the window size and the overlap between two win-
dows for feature extraction have been set to 8 and 7
frames, respectively. Then, all feature vectors have
been quantized into 400 clusters and finally by setting
the number of HMM state to 15, training and test-
ing phases are conducted. Train and test are done by
changing factor α which has been previously stated
(Equation 3). By using a fixed α, ”Active Joint Selec-
tor” can select active joints of each class of action and
finally by averaging over the number of active joints
for each action, the average of active joints for that ex-
periment (including both train and test) is calculated.
Test accuracy results for different averages of ac-
tive joints are given in Table 1. Obviously, using the
active joints instead of all joints can speed up the fea-
ture extraction step. As is clear in Table 1, by using
about 9.8 of all joints, obtaining an accuracy of 97%
of using all joints is accessible. The relation between
Skeleton-based Human Action Recognition - A Learning Method based on Active Joints
307

(a) (b) (c)
Figure 6: MSR action 3D results. (a) Effect of using different average amount of active joints at each experiment. (b)
Confusion matrix of all joints. (c) Confusion matrix of 9.8 of joints on average.
(a) (b) (c)
Figure 7: UTKinect results. (a) Effect of using different average amount of active joints at each experiment. (b) Confusion
matrix of all joints. (c) Confusion matrix of 9.7 of joints on average.
Figure 8: Selected active joints of each class for α = 0.372
on MSR action 3D dataset.
accuracies when using all joints and active joints is
illustrated in Fig. 6(a).
In order to get the average of active joints to 9.8,
α is assigned to 0.372 (α = 0.372). By using this
amount of α, the list of active joints per action is il-
lustrated in Fig. 8 based on skeleton model of body
which is illustrated in 2(a). Confusion matrices for
the case ”all joints” and ”average of active joints =
9.8” are given in 6(b) and 6(c), respectively. From
the reported confusion matrix for active joints and the
list of active joints in case α = 0.372, this conclusion
is yielded that there is no error and mis-recognizing
item when active joints are different. Results of the
proposed and previous methods are given in Table 3.
The result on MSR action 3D dataset is lower than
some other methods. This is due to the problem that
a set of actions in this dataset have the same active
joints. Most of the misclassified actions are in this
set. Thus, it can be analyzed that using better feature
extraction methods will solve this problem.
4.2 UTKinect
UTKinect is another action dataset which contains
both RGB and depth video sequences. The depth map
frame rate and the resolution are 30 fps and 320×240,
respectively. Also, the resolution of RGB sequences
is 640×480. This dataset includes 10 actions: walk,
sit down, stand up, pick up, carry, throw, push, pull,
wave, and clap hands. Actions of this dataset have
been performed twice by 10 subjects. The actions in
this dataset cover the movements of hands, arms, legs,
and upper torso.
To test the proposed method based on active joints
on UTKinect, the window size and the overlap be-
tween windows in order to extract feature, are set to
4 and 3 frames, respectively. The number of clusters
to be used for K-means for quantization of extracted
feature vectors, is set to 120. To train the HMM of
each class, 5 states have been considered.
The test results for different averages of active
joints are given in Table 2 for UTKinct. As it is stated
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Table 1: MSR action 3D results.
Used Active Joints 4.3 5.45 7.2 9.8 12.65 15.45 17.85 20
Accuracy % 77.82 80.73 81.09 82.18 82.18 84.00 84.00 84.72
Scaled Accuracy
*
% 91.85 95.29 95.71 97.00 97.00 99.15 99.15 Ref.
Speed up
**
% 365 267 178 104 58 29 12 Ref.
*
The accuracies have been scaled w.r.t. the accuracy when all joints are used.
**
The percentage of speeding up, when fewer joints are used instead of all.
Table 2: UTKinect results.
Used Active Joints 5.6 7.9 9.7 11.7 13.7 15.9 17.8 19.3 20
Accuracy % 73.74 80.81 91.92 91.92 86.87 90.91 87.88 90.91 95.96
Scaled Accuracy
*
% 76.84 84.21 95.79 90.53 94.73 91.58 94.77 94.74 Ref.
Speed up
**
% 257 153 106 71 46 26 12 4 Ref.
*
Accuracies have been scaled w.r.t. when all joints are used.
**
Percentage of speeding up, when fewer joints are used instead of all.
Table 3: Performance of proposed method on MSR action
3D dataset, compared to previous approaches.
Method Accuracy %
(Li et al., 2010) 74.70
(Xia et al., 2012) 79.00
(Oreifej and Liu, 2013) 88.89
(Yang and Tian, 2014) 93.09
Proposed (Using all joints) 84.72
Proposed (Using 9.8 of joints) 82.18
for MSR action 3D, using active joints speeds up fea-
ture extraction. Table 2 shows that using 9.7 joints on
average obtains 96% of maximum available accuracy
when all joints are used. In this case, features can be
extracted about 2 times faster than the case for which
all joints are used. The relation between accuracies
in using all joints and using active joints (Fig. 7(a))
shows that by increasing the number of active joints
(instead of all joints), the accuracy decreases rather
than the case that 9.7 of joints are used.
If the average of active joints gets to 9.7, α must be
set to 0.3 (α = 0.3). Active joints of UTKinect dataset
in this case are illustrated in 9(b) (Joints number is
based on Fig 9(a)). The confusion matrix for the case
”all joints” and ”average of active joints = 9.7” are
given in 6(b) and 6(c), respectively. Results of the
proposed and previous methods are given in Table 4.
5 CONCLUSION
A novel method for human action recognition from
the sequences of skeletal human body data was pre-
sented. Using the skeletal data make this algorithm
applicable to the ”motion capture” problem. The pro-
posed approach was founded upon the idea that given
(a)
(b)
Figure 9: (a) Joints number of skeleton model in UTKinect
dataset. (b) Selected active joints of each class for α = 0.3
on UTKinect dataset.
Table 4: Performance of proposed method on UTKinect
dataset, compared to previous approaches.
Method Accuracy %
(Xia et al., 2012) 90.92
(Devanne et al., 2013) 91.5
(Liu et al., 2015) 95.00
(Vemulapalli et al., 2014) 97.08
Proposed (Using all joints) 95.96
Proposed (Using 9.7 of joints) 91.92
an action, there are just some active joints. Thus, ex-
istence of active joints is sufficient and there is no de-
pendence on the existence of inactive joints of human
Skeleton-based Human Action Recognition - A Learning Method based on Active Joints
309

body, which makes this algorithm more flexible than
the other ones. This characteristic and using skele-
tal data, made this algorithm to be used in the case
for which the information of some inactive joints are
missing. The local temporal features were extracted
using overlapped sliding windows over the trajectory
of each joint, and the global temporal information was
taken into account using the HMM classifier. Also,
there is a rich possibility for extensions. In this pa-
per, there is no contribution to feature extraction, and
this belief exists that by using more discriminative
features, the final accuracy of the method can be im-
proved. Thus, as a future work, the state-of-the-art
feature extraction methods can be used. Using more
powerful quantization method instead of K-means can
also improve the results.
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