Machine Learning Classification
Techniques for Human Activity Analysis
Assistant Professor Mrs Anitha J1 Ramya R2
1
2
Professor of Dr Ambedkar Institute of Technology, Dept of MCA, Bangalore-560056, Karnataka, India
Student of Dr Ambedkar Institute of Technology, Dept of MCA, Bangalore-560060, Karnataka, India
Abstract —Human Activity Recognition (HAR) is for sure an
enormous field of study and examination. This is a time series
classification task in which data from multiple time steps is
required to appropriately characterize the current activity. To
put it differently, movement recognition is the most typical
method of identifying or anticipating which movement or action
will be taken up by someone. This is a series data classification
problem in which data from multiple time steps is required to
appropriately categorise the current activity. Eating, Walking,
resting, reading the newspaper, conversing, jumping, standing,
drinking, and sitting are examples of activities. The problem
here is that to effectively forecast the action that is being
performed, you will essentially need a succession of knowledgebased points in action recognition. As a result, the action-based
recognition would be in the form of a statistic classification,
with the disadvantage that the data from a sequence of time
steps is likely to be insufficient to appropriately identify the
action that is being performed. We used a variety of machine
classification approaches to analyze human being behaviour,
including, Neural Network, SGD, K-Nearest Neighbor (KNN),
Naive Bayes & Random Forest. The results of the experiments
showed that the Neural Network and logistic regression offer
superior accuracy for human activity recognition compared to
other classifiers including k-nearest neighbor (KNN), SGD,
Random Forest, and Naive Bayes, although they require more
processing time and memory.
Keywords: Activity Recognition, Neural Network, Random
Forest, SDN, k-nearest neighbor, Activity, Recognition,
Classification.
I.
INTRODUCTION
Human activity recognition (HAR) is an important exam
question with accurate recognition of different pieces of training
that can be stored in different ways. Human Activity
Recognition has seen use in a variety of businesses and
occupations, including smart healthcare, automation
surveillance, and security.
Computers have become capable of handling some very difficult
jobs as a result of numerous breakthroughs in machine learning
(such as comprehending an image). At the point when given a
picture, models are being constructed that can foresee what's
going on with the picture or distinguish if a specific trademark
is there. These models depend on the system and working of the
cerebrum and are known as brain network models (or fake brain
organizations).
Deep learning is a part of the directed discovery that looks at
brain organizations, and various sorts of brain networks have
been utilized to take care of an assortment of issues after
some time.
Human actions have a built-in hierarchical structure that
displays the activity's varying degrees, which can be divided
into three groups. At the most basic level, there seems to be
an atomic element, and all these movement elements are also
what help compensate for more complex human acts. Just
after the action primitive level, comes the action/activity
level.
Ultimately, complicated connections, which propose human
physical activities related to greater humans and things, are
the nice level
II. PROBLEM DEFINITION
This project will most likely develop a model that can
recognise basic human behaviours such as jogging, sitting,
clapping, hand-waving, and boxing. A progression of
recordings will be provided to the model, with an individual
doing an activity in every film. The video's name will
constitute the occasions which can be unfolding in this
particular scene.
Before it can examine the label of a supply of the information
(videos) it has in no way seen, the computation needs to
recognize this relationship. When shown a few models, the
model should be able to detect various human
demonstrations. Indeed, by claiming to show certain
patterns, the model should be able to detect various human
manifestations.
III. EXISTING SYSTEM
In controlled conditions, the bulk of known techniques
shows human activities as a collection of picture elements
captured in surveillance images or photos and various
classification patterns are used to identify the main activity
tag. The datasets considered are all generic. These limits, on
the other hand, create an unrealistic environment that does
not account for real-world conditions and fails to meet the
requirements for an acceptable human activity dataset.
Examine how people go about their daily lives, such as
studying and eating. We can employ wearable device sensors
to collect data from testing and training. After gathering the
data, it will transmit it to the record output.
IV. PROPOSED SYSTEM
In this proposed system, we present the convolution neural
network method for video action recognition. The input footage
will be recorded using the webcam. The video sequence's
number of frames is changed. The precise frame segment is
subsequently identified by using CNN (Convolution Neural
Network) approach. The maximum criteria weights from the
extracting features frames are subsequently obtained using a
convolutional neural network. Ultimately, the activity in the
video will be identified, and a labelling (activity name) will be
assigned. The output is then transferred to Firebase, where the
user receives the Firebase value through an Android
notification.
A. Detection - The location of a person in a static photograph
or a series of photographs, such as development
photographs, is used to discern activity. The adaptability
with an image can be followed in the case of a continuous
grouping, although this is more important for applications
like gesture communication. The fundamental reason for
activity acknowledgement is that human vision can
distinguish objects that robots can't with similar accuracy. It
resembles a man mishandling his senses to find an item
according to the viewpoint of a machine.
B. Movement Recognition - The reason for Movement
Recognition in registering science and specialized detail is
to examine human activities utilizing numerical calculations
and picture and camera tests. Gesture recognition
techniques, on the other hand, are used to identify and
recognise stance, movement, expressions, and human
behaviours.
The Neural Network is implemented using the Orange Tool,
and thus the Neural Network is run in Python utilizing
dropouts, yielding superior results. The data was processed
using regression algorithms, K-nearest neighbours, SDG,
Multilayer Perceptron, Naive Bayes, and Random Forest.
The Recall and Precision Values of each model were
determined, and the Confusion Matrix for each model was
constructed.
Fig 2: System Architecture
Random Forest: Random Forest is a backslide and
depiction segment. A wide range of trees fills in erratic
timberlands. Each tree is produced utilizing the readiness
information and a bootstrap test. Choose the amount of
accommodating trees to be associated with backwoods
(Numbers of trees within timberland region) and the number
of properties to be drawn for thought at each middle point. If
the not entirely set in stone (decision Number of left
excessive), the number is comparable to a square base of the
number of qualities in the data. The client can pick the
profundity by which the trees will be developed since
Unique Breiman's proposition will be to foster all trees
without pre-pruning.
Stochastic Gradient Descent: Stochastic Gradient Descent
gadgets utilize stochastic slope plunge to limit a direct
misfortune work. Choose the calculation parameters for the
coordinating goof work, backslide incidence limit, and
guidelines to avoid overfitting. Finally, the learning limits
are set, and they are not permanently set up by the learning
rate. For consistent cycles, the beginning readiness botch is
0.0100, and the number of overlooks the scheduling data is
fixed at 1000.
Programming Language: Python is feasible in all
circumstances, particularly for AI and AI estimations. The
problem is that the models don't have complete trust in each
frame buffer prediction in every case, thus these assumptions
will change extremely quickly and without error because the
calculation doesn't witness the actual video transfer.
One of the fundamental solutions to this sort of trouble is to
restrict the casings instead of parting and showing the results
of a solitary structure. To obtain this result, after the
valuation of n, use anything very basic like a moderate
movement or moving in the centre.
Fig 1: Human Activity Recognition
C. Design Approach – Since the data offers a multivariate
classification issue, we used both supervised and
unsupervised algorithms.
Naive Bayes: Naive Bayes is one probabilistic model that
considers speedier expectations from information
continuously.
KNN: The KNN is a solicitation approach that requires no
past information on the information course. Assign a value
of k to the number of nearest neighbours.
The unidentified datapoint will be given out to a class from a
get-together of named places in its close region. The distance
equation, which is either Manhattan or Euclidean, is used here.
The KNN device utilizes a calculation that searches out the k
nearest planning model in featured space and uses their normal
as a gauge. At the point when k is 5, the KNN calculation gives
96.6 per cent exactness in following human exercises.
D. Working - Videos are organised into action categories on
the internet, including cricket, piercing, cycling, and so on.
This database is frequently utilised in the creation of action
viewers, a video sharing software. Frames in a video are in a
specific order. Keras is a Python package for open-source
development and testing of in-depth learning models that is
very effective and simple to use. It combines the Theano &
TensorFlow frameworks to let you design and build models
with just a few program codes.
E. System Implementation Hardware Requirements :
•
•
•
Intel Core i3 3rd gen processor or later.
512 MB disk space.
512 MB RAM.
Software Requirements :
•
•
•
•
Microsoft Windows XP or later / Ubuntu 12.0 LTS.
or later /MAC OS 10.1 or later.
Python Interpreter (3.6).
TensorFlow framework, Keres API.
PyQT5, Tkinter module.
V. RESULTS
The camera first detects the presence of a human, then the action
that is occurring. The deep learning algorithm utilised is ResNet,
which generally employs 2D kernels, however, 3D kernels are
enabled to improve the effectiveness of activity recognition. The
kinetics400 dataset, which specifies 400 various types of work,
was used to train the activity recognition model. In addition, 400
videos for every action are available to improve accuracy.
For activity identification, the ResNet network for human action
recognition with 2D CNN employs 152 layers. This 2D CNN
ResNet network handles tasks such as detection, segmentation,
and captioning, but our model additionally includes a 3D CNN
that processes videos rather than images, increasing the
likelihood of action recognition success. Recognition,
summarization, optical flow, segmentation, and captioning are
some of the activities that can be completed.
As a result, human activity recognition accuracy is improved.
VI. CONCLUSION
We have suggested a machine learning-based Human Action
Recognition system that deals with identifying activity as
regular or suspicious based on its nature.
If any unusual activity is detected, an immediate alert signal
is issued to authority, allowing for the reduction of further
depressing repercussions.
Recognizing human hobbies is a vast area of study that
entails looking at such objectives to comprehend someone's
genuine motion or movement. This is a type of time
collection sort problem in which statistics from a series of
time steps are required to properly classify the current
movement. Recognizing human interest, or HAR is a
difficult task of classifying statistical elements.
So, for future work, we will run the model using more live
datasets and get the result. We'll also look into different
perceptron and optimizers, as well as more advanced ways
to improve model accuracy. Future work may also include
the implementation of this model for useful applications like
monitoring the activities, predicting danger, etc.
VII. FUTURE SCOPE
Human Activity Recognition can help with home automation
surveillance, health services, security surveillance, and
childcare, among other things. We may upgrade our
programme in the future by employing object activity
recognition, which allows us to track and analyse the
activities that objects conduct. The use of combined huge
datasets can be used to identify activities occurring at a lower
rate of time. The system should be able to detect even the
tiniest of differences. If the actor doing the anomalous action
is not captured in the first place, the data of the actor can be
stored and the actor identified. Activities that repeat
themselves should be saved to save time and space
throughout the recognition process.
Such a paradigm can likewise be implemented in the
government authority sector. Much more work can be done
in terms of improving precision and dealing with difficulties
such as optical identification and image background clutter.
Human activity recognition is mostly employed in health
systems deployed in residential settings, hospitals, and
rehabilitation facilities. It's widely utilised in rehabilitation
programmes for chronic disease management and prevention
to monitor the activities of older individuals.
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