722 research outputs found
Machine learning in 3D space gesture recognition
The rapid increase in the development of robotic systems in a controlled and uncontrolled environment leads to the development of a more natural interaction system. One such interaction is gesture recognition. The proposed paper is a simple approach towards gesture recognition technology where the hand movement in a 3-dimensional space is utilized to write the English alphabets and get the corresponding output in the screen or a display device. In order to perform the experiment, an MPU-6050 accelerometer, a microcontroller and a Bluetooth for wireless connection are used as the hardware components of the system. For each of the letters of the alphabets, the data instances are recorded in its raw form. 20 instances for each letter are recorded and it is then standardized using interpolation. The standardized data is fed as inputs to an SVM (Support Vector Machine) classifier to create a model. The created model is used for classification of future data instances at real time. Our method achieves a correct classification accuracy of 98.94% for the English alphabets’ hand gesture recognition. The primary objective of our approach is the development of a low-cost, low power and easily trained supervised gesture recognition system which identifies hand gesture movement efficiently and accurately. The experimental result obtained is based on use of a single subject
Roadmap on 3D integral imaging: Sensing, processing, and display
This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field
Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control
Prosthetic hand control based on the acquisition
and processing of surface electromyography signals (sEMG) is a
well-established method that makes use of the electric potentials
evoked by the physiological contraction processes of one or more
muscles. Furthermore intelligent mobile medical devices are on
the brink of introducing safe and highly sophisticated systems to
help a broad patient community to regain a considerable amount
of life quality. The major challenges which are inherent in such
integrated system’s design are mainly to be found in obtaining a
compact system with a long mobile autonomy, capable of
delivering the required signal requirements for EMG based
prosthetic control with up to 32 simultaneous acquisition
channels and – with an eye on a possible future exploitation as a
medical device – a proper perspective on a low priced system.
Therefore, according to these requirements we present a wireless,
mobile platform for acquisition and communication of sEMG
signals embedded into a complete mobile control system
structure. This environment further includes a portable device
such as a laptop providing the necessary computational power
for the control and a commercially available robotic handprosthesis.
Means of communication among those devices are
based on the Bluetooth standard. We show, that the developed
low cost mobile device can be used for proper prosthesis control
and that the device can rely on a continuous operation for the
usual daily life usage of a patient
A framework for developing motion-based games
Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaNowadays, whenever one intents to develop an application that allows interaction
through the use of more or less complex gestures, it is necessary to go through a long process. In this process, the gesture recognition system may not obtain high accuracy results, particularly among different users.
Since the total number of applications for mobile systems, like Android and iOS, is
close to a million and a half and is still increasing, it appears essential the development of a platform that abstracts developers from all the low-level gesture gathering and that streamlines the process of developing applications that make use of this kind of interaction, in a standardize way. In this case such was developed for the iOS system.
At the present time, given the existing environment issues, it is ideal to attract the attention, motivate and influence the greatest number of people into having more proenvironmental behaviors. Thus, as a proof of concept for the developed framework,
an educational game was created, using persuasive technology, to influence players’s
behaviors and attitudes in a pro-environmental way.
Therefore, having this idea as a basis, it was also developed a game that is presented
in a public ambient display and can be played by any participant close to the displaywho has a device with iOS mobile system
Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)
Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of
patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize
the spread of the virus. The need for providing care to patients at home is essential. Internet
of Things (IoT) is widely known and used by different fields. IoT based homecare will help
in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as
minimizing human exertions, economical savings and improved efficiency and effectiveness. One
of the important requirement on homecare system is the accuracy because those systems are
dealing with human health which is sensitive and need high amount of accuracy. Moreover,
those systems deal with huge amount of data due to the continues sensing that need to be
processed well to provide fast response regarding the diagnosis with minimum cost requirements.
Heart is one of the most important organ in the human body that requires high level of caring.
Monitoring heart status can diagnose disease from the early stage and find the best medication
plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers
efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram
(ECG) signals are used to track heart condition using waves and peaks. Accurate
and efficient IoT ECG monitoring at home can detect heart diseases and save human lives.
As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting
Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used;
one online which is old and limited, and another huge, unique and special from real patients
in hospital. The raw ECG signal for each patient is passed through the implemented Low
Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any
external interference. The clear signal in this model is passed through feature extraction stage
to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to
apply classification on them. For the diagnosis purpose a classification stage is made using three
classification ways; threshold values, machine learning and deep learning to increase the accuracy.
Threshold values classification technique worked based on medical values and boarder lines. In
case any feature goes above or beyond these ranges, a warning message appeared with expected
heart disease. The second type of classification is by using machine learning to minimize the
human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm
on the features extracted from both databases. The classification accuracy for online and hospital
databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a
third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP)
Neural Network is implemented to improve the accuracy and reduce the errors. The number of
errors reduced to 0.019 and 0.006 using online and hospital databases.
While using hospital database which is huge, there is a need for a technique to reduce the amount
of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed.
This algorithm is able to make diagnosis of heart disease from the reduced size using compressed
ECG signals with high level of accuracy and low cost. The extracted features from compressed
and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine
learning and deep learning classification accuracy without the need for any reconstructions. The
throughput is improved by 43% with reduced storage space of 57% when using data compression.
Moreover, to achieve fast response, the amount of data should be reduced further to provide
fast data transmission. A compressive sensing based cardiac homecare system is presented.
It gives the channel between sender and receiver the ability to carry small amount of data.
Experiment results reveal that the proposed models are more accurate in the classification of
Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast
diagnosis and minimum cost requirements. Based on the experiments on classification accuracy,
number of errors and false alarms, the dictionary of the compressive sensing selected to be 900.
As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac
monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy
in addition to minimizing data and cost requirements
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