Sriparna Saha

Maulana Abul Kalam Azad University of Technology, West Bengal

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Ahona Ghosh

Maulana Abul Kalam Azad University of Technology, West Bengal

Lidia Ghosh

Jadavpur University

Sima Das

Dr. Shroff’s Charity Eye Hospital

Samrat Mondal

Indian Institute of Technology Patna

Pranab Sahoo

Indian Institute of Technology Patna

Indranil Sarkar

Techno India Group (India)

Suraj Gowda

Narayana Health

Biswarup Ganguly

National Institute Of Technology Silchar

Amit Konar

Jadavpur University

Rajkanya Das

Gauhati University

Cooperative Institutions

Maulana Abul Kalam Azad University of Technology, West Bengal

Indian Institute of Technology Patna

Jadavpur University

Techno India University

Institute of Engineering

All India Institute of Medical Sciences Bhopal

Chandigarh University

Dr. Hari Singh Gour University

Maulana Abul Kalam Azad Institute of Asian Studies

National Institute Of Technology Silchar

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

A Study on the Role of Electroencephalogram in Cyber Physical System and Security

CRC Press eBooks (2023)

Ahona Ghosh Sriparna Saha

The role of computer-based algorithms in cyber physical systems include controlling and monitoring physical, network and computing resources. Being a widely used sensor in emerging trends like machine learning, the Electroencephalogram (EEG) has also proved its effectiveness and efficiency in various domains including human computer interaction, healthcare, cognitive science and security. In this chapter, the role of EEG in designing cyber physical systems in various aspects will be analyzed from the existing literature and a comparative analysis between the algorithms applied by them will be presented in terms of the advantages and limitations that can guide future researchers proceeding in this area.

Cyber-physical system

10.1201/9781003190301-8

Cite

Citations (0)

Analyzing Adverse Gaming Effects on Emotions Using Neural Networks Based Hybrid Architecture

2021 IEEE 18th India Council International Conference (INDICON) (2019)

Sriparna Saha Jay Karia

In Cyber Security, harmful websites/apps can be flagged using the emotions of the users. Human emotions can be read from facial expressions. In this paper, the emotion detection has been done in real-time while playing video games. A facial emotion detection algorithm is designed for classification of five different emotions. The classifier is a two staged one and in both cases back propagation neural network is used. For the first stage of classification, face or non-face images are classified using haar-like features and integral image. After this classification, the non-face images are discarded and only images that contain face are kept for recognizing emotions from them. In this second stage of classification, four salient features are extracted for classification. The work is first-of its kind and it has tremendous potentials for parental controls for young individuals.

Emotion detection

Contextual image classification

10.1109/indicon47234.2019.9029029

Cite

Citations (3)

An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram

Advances in computational intelligence and robotics book series (2022)

Darunjeet Bag Ahona Ghosh Sriparna Saha

Modern science and technology development enables humans to control devices using their brains. brain computer interface (BCI) system using EEG (Electroencephalogram) is a non-invasive method that uses brain signals to control robots. Brain control robots can assist people with disabilities to improve their livelihood. In our proposed work to control wheelchair movement for rehabilitation using EEG, electrodes are first placed on the subject's scalp to acquire brain signal activity. After that, the signals got filtered using fast fourier transformation (FFT) method, and then features got extracted using power spectral density (PSD). Random forest is used for the classification of wheelchair movement. For this purpose, a publicly available dataset from Kaggle is used, and an average accuracy of 96.79 is achieved. The proposed architecture has outperformed all the existing ones in its concerned domain; thus, it is suitable, cost-effective, and flexible for the users, which also helps maintain user privacy.

Interface (matter)

10.4018/978-1-6684-5381-0.ch007

Cite

Citations (7)

Hybrid Electroencephalography-Induced Robot Navigation for Rehabilitative Platform

2021 IEEE 18th India Council International Conference (INDICON) (2023)

Ahona Ghosh Lidia Ghosh Sharmistha Dey Indranil Sarkar Sriparna Saha

Decoding upper limb movement from electroencephalography (EEG) signals is an emerging field of research that has the potential to enhance the quality of life for individuals with motor impairments. This paper provides an informative overview of EEG-based upper limb movement decoding and addresses the limitations found in existing studies by proposing a novel rehabilitative robotics framework. The framework utilizes three types of EEG signals: motor imagery, error-related potential, and P300 signals, to navigate an OpenManipulatorX robotic arm. The novelty of this research lies in the utilization of a phase-sensitive Common Spatial Pattern approach for feature extraction, as well as the successful recognition of commands for robot control using Linear Discriminant Analysis. The proposed method achieves an impressive average success rate of 95% for target reaching, making it suitable for the design of prosthetics in real-time rehabilitative platforms.

10.1109/indicon59947.2023.10440798

Cite

Citations (0)

Bi-directional Long Short-Term Memory model to analyze psychological effects on gamers

Applied Soft Computing (2020)

Lidia Ghosh Sriparna Saha Amit Konar

Sadness

Surprise

Video game

10.1016/j.asoc.2020.106573

Cite

Citations (21)

Detection of Arrhythmia for Cardiac Functional Recovery Using Electrocardiogram

Lecture notes in networks and systems (2024)

Ahona Ghosh Rajkanya Das Sriparna Saha

Cardiac arrhythmia

10.1007/978-981-97-7532-3_16

Cite

Citations (0)

Suppression of positive emotions during pandemic era: a deep learning framework for rehabilitation

International Journal of Modelling Identification and Control (2022)

Ahona Ghosh Sriparna Saha

The rapidly growing popularity of computer and Android gaming in the pandemic era has led many researchers to focus on the emotional changes and adverse effects of gaming on a player. Sometimes games are played to give relaxation and help to recover from anxiety and stress, but some recent incidents are alarming since vigorous games playing can lead to anxiety and stress. This paper has attempted to overcome the shortcoming of existing literature by a novel approach to detect emotions acquired from electroencephalographic signals using a deep learning algorithm with high accuracy of emotion recognition. The feature extraction and classification algorithms individually outperformed the existing ones in the related area and the combination of them also has shown better performance than the state-of-the-art literature. Outcomes of the proposed framework have shown its wide range of applicability from parental control to cyber security by emotion detection and in terms can help in providing rehabilitation.

Popularity

Affective Computing

10.1504/ijmic.2022.127101

Cite

Citations (6)

A Machine Learning Approach Towards Yoga Exercise Classification for Pain Recovery

Advances in medical technologies and clinical practice book series (2024)

Ahona Ghosh Sriparna Saha Indranil Sarkar

Due to the sedentary life of people, back pain is a common problem in young individuals as well as for elderly persons. To prevent it, or reduce it, back stretch exercises are often prescribed by physicians, and an automated feedback system for these rehabilitative sessions can help individuals achieve their target more flexibly and efficiently. The current chapter proposes a machine-learning approach to classify different yoga exercises for back pain recovery using a Kinect sensor. In front of the sensor, subjects are asked to perform six back stretch exercises: Mermaid, seated, sumo, towel, wall, and Y. Features are then extracted based on the distance and angle between body joints from the skeletons generated by that sensor. Finally, the exercises are recognized through a hyperparameter-tuned random forest with 97.8% accuracy. The classifiers' effectiveness in classifying exercises is reliably assessed in real-time and outperforms its competitors in the related domain.

10.4018/979-8-3693-2333-5.ch001

Cite

Citations (0)

A Mind-Driven Artificial Limb Movement Framework Using Long Short-Term Memory Algorithm

Lecture notes in networks and systems (2023)

Ahona Ghosh Sriparna Saha

Motor Imagery

Extractor

Interfacing

10.1007/978-981-99-3734-9_47

Cite

Citations (0)