Dr. VINOJ P G

@cce.edu.in

Professor, Department of Electronics and Communication Engineering
CHRIST COLLEGE OF ENGINEERING, IRINJALAKUDA

Dr. VINOJ P G


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Vinoj P. G, received the B.Tech. degree in electronics and communication from the Cochin University of Science and Technology, Kerala, India in 2004, and the M. Tech degree in embedded system from the National Institute of Electronics and Information Technology, Calicut, India in 2009. He has received the Ph.D. degree in electronics and communication engineering from APJ Abdul Kalam Technological University, Kerala, India in 2024. He worked as an R&D Engineer with Robhatah Robotic Solution, Bangalore. He is currently an Associate Professor with the Department of Electronics and Communication, SCMS School of Engineering and Technology, and also the Faculty in Charge of Fablab and Center for more than 15 years of teaching experience and 5 years of Industrial experience. He is the recipient of the Gandhian young technology Innovation award 2020. He has more than 15 Publications in reputed national and International Journals. He has worked as Principal/CO-Investigator in more

EDUCATION

Doctoral work(PhD):“Brain Actuated System for Assisting the Paralysed ” (August 2024)
APJ Abdul Kalam Technological University, Thiruvananthapuram.
Post-Graduation: M. Tech in Embedded System (2009)
National Instituite of Electronics and Information Technology,Calicut.
Graduation: B. Tech in Electronics and Communication Engineering (2004)
Cochin University of Science and Technology, Kochi

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Hardware and Architecture, Human-Computer Interaction, Biomedical Engineering
9

Scopus Publications

280

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • AI assisted BCI system for rehabilitation
    Vinoj P G, Varun G Menon
    Conference Proceedings 2025 4th International Conference on Advances in Computing Communication Embedded and Secure Systems Access 2025, 2025
    BCI (Brain Computer Interface) is a leading technology designed to assist individuals with disabilities. However, current BCI-based devices encounter challenges in promoting recovery due to inadequate training, insufficient supervision, and the mental fatigue that arises from prolonged use. Users often feel frustrated because executing mental commands requires intense concentration, which can lead to inaccuracies and a lack of control over the assistive technologies. To tackle these issues, a BCI framework has been unified with machine learning model to mitigate mental tiredness and improve user satisfaction. This model uses an integration of LSTM (Long Short-Term Memory) and CSP (Common Spatial Pattern) based approach to interpret a patient’s movement intentions based on signal characteristics learned during training. It classifies various Electroencephalogram (EEG) patterns to translate them into desired upper limb movements, effectively activating muscles to aid rehabilitation of stiff body parts. Testing on both healthy and paralyzed subjects yielded a classification accuracy of 98.2% in determining limb motion intentions. These findings position the LSTM-based deep learning model as a powerful tool for BCI-controlled rehabilitation.
  • Optimizing firefighting performance with FPV drones
    Sagar P. Gokul, M. J. Sayooj, A. A. Yunus, Anandu Suresh, P. G. Vinoj
    Aip Conference Proceedings, 2024
  • Wearable Fabric Tactile Sensors for Robotic Elderly Assistance
    Mary Catherine V G, Binu Paul, Vinoj P G
    Proceedings 2nd International Conference on Smart Technologies Communication and Robotics 2022 Stcr 2022, 2022
    The demand for Robots in Elderly assistance is increasing due to the lack of human caregivers. In the context of Robot coexisting with the human beings in a home environment, for the safe and friendly interaction it is essential to endow the sense of touch through Tactile sensor systems. This paper proposes a novel scalable approach for tactile sensors based on low cost wearable conductive fabric. Fabric tactile sensor (FABTAC) is conformable with the robot body and can be used as a tactile sensing skin that perceives touch and force applied at the contact location. FABTAC sensors are developed as an array of touch sensors sewed on the cloth substrate with the stainless-steel conductive thread. The thermistor sensors are also sewed to fabric to perceive the temperature information. The FABTAC sensors are integrated on to the custom-made 3D printed Robotic hand and the tactile data is processed with a novel wearable electronic FLORA microcontroller platform. The acquired data can be used to provide a real time tactile feedback for performing assistive tasks like grasping objects of diverse profiles, avoiding slippage. The FABTAC sensors has the advantage of utilizing flexible, light weight sensors with good spatial and temporal resolution. Thus, the system can potentially aid the automation of daily life activities of the Elderly thereby enhancing the quality of their life.
  • IoT-powered deep learning brain network for assisting quadriplegic people
    Vinoj P.G., Sunil Jacob, Varun G. Menon, Venki Balasubramanian, Md. Jalil Piran
    Computers and Electrical Engineering, 2021
  • Deep Learning Brain Actuated Bidirectional Communication and Rehabilitation aid for the Quadriplegic
    Sunil Jacob, Mukil Alagirisamy, P G Vinoj, R Parvathi, Sandeep Verma
    Icsccc 2021 International Conference on Secure Cyber Computing and Communications, 2021
    The Brain controlled technology is the new revolution for aiding and rehabilitating the quadriplegic. Since the brain signals are dynamic in nature, it is difficult to accurately track the device's functioning without error. The bidirectional communication between the quadriplegic person must be established to evaluate and correct the feedback. The existing exoskeletons design bring frustration and difficulty to control for both the patient and the assistance. To solve these issues, Deep Learning Brain Actuated Bidirectional Communication (DLBABC) system is proposed. The system aims at alerting the caregivers with messages in an emergency situation by human intervention. The training in offline mode for specific movement and online mode using DLBABC is carried out to set the bidirectional communication with the assistant. The gesture with correct posture is analyzed and is converted into speech signal, allowing the quadriplegic to communicate with caregivers and friends.
  • A Novel Spectrum Sharing Scheme Using Dynamic Long Short-Term Memory with CP-OFDMA in 5G Networks
    Sunil Jacob, Varun G. Menon, Saira Joseph, P.G. Vinoj, Alireza Jolfaei, Jibin Lukose, Gunasekaran Raja
    IEEE Transactions on Cognitive Communications and Networking, 2020
    With the rapid increase in communication technologies, shortage of spectrum will be a major issue faced in the coming years. Cognitive radio is a promising solution to this problem and works on the principle of sharing between cellular subscribers and ad-hoc Device to Device (D2D) users. Existing 5G spectrum sharing techniques work as per a fixed rule and are pre-established. Also, recent game theoretic approaches for spectrum sharing uses unrealistic assumptions with less likely practical implications. Here, a novel spectrum sharing technique is proposed using 5G enabled bidirectional cognitive deep learning nodes (BCDLN) along with dynamic spectrum sharing long short-term memory (DSLSTM). A joint spectrum allocation and management is carried out with wireless cyclic prefix orthogonal frequency division multiple access (CP-OFDMA). The BCDLN self-learning nodes with decision making capability route information to several destinations at a constant spectrum sharing target, and cooperate via DSLSTM. BCDLN based on time balanced and unbalanced channel knowledge is also examined. With the proposed framework, expressions are derived for the spectrum allocated to multiple sources to obtain their spectrum targets as a variant of the participation node spectrum sharing ratio (PNSSR). The impression of noise when all nodes broadcast with equal spectrum allocation is also investigated.
  • A Top-Up Design for PAL to VGA Conversion in Real Time Video Processing System
    Jafar Alzubi, Sunil Jacob, Varun G Menon, Saira Joseph, P G Vinoj
    International Symposium on Advanced Electrical and Communication Technologies Isaect 2018 Proceedings, 2019
    Real time video processing found its range of applications from defence to consumer electronics for surveillance, video conferencing etc. With the advent of FPGAs, flexible Real-Time Video Processing System (RTVPS) which can meet hard real-time constraints are easily realised with short development time. A hardware software co-design for an FPGA based real time video processing system to convert video in standard PAL 576i format to standard video of VGA / SVGA format with little utilisation of resources is realised and evaluated. Switching between multiple video streams, character/ text overlaying, skin colour detection is also incorporated. The system is also adaptable for rugged applications. VHDL codes for the architecture were synthesized using ALTERA Quartus II and targeted for ALTERA STRATIX I FPGA. The evaluated results show that the resource utilization is low for this design. Since system is also flexible, latest applications can be incorporated in future.
  • Artificial Muscle Intelligence System with Deep Learning for Post-Stroke Assistance and Rehabilitation
    Sunil Jacob, Varun G. Menon, Fadi Al-Turjman, Vinoj P. G., Leonardo Mostarda
    IEEE Access, 2019
    Stroke is one of the prime reasons for paralysis throughout the world caused due to impaired nervous system and resulting in disability to move the affected body parts. Rehabilitation is the natural remedy for recovering from paralysis and enhancing the quality of life. Brain Computer Interface (BCI) controlled assistive technology is the new paradigm, providing assistance and rehabilitation for the paralysed. But, most of these devices are error prone and also hard to get continuous control because of the dynamic nature of the brain signals. Moreover, existing devices like exoskeletons brings additional burden on the patient and the caregivers and also results in mental fatigue and frustration. To solve these issues Artificial Muscle Intelligence with Deep Learning (AMIDL) system is proposed in this paper. AMIDL integrates user intentions with artificial muscle movements in an efficient way to improve the performance. Human thoughts captured using Electroencephalogram (EEG) sensors are transformed into body movements, by utilising microcontroller and Transcutaneous Electrical Nerve Stimulation (TENS) device. EEG signals are subjected to pre-processing, feature extraction and classification, before being passed on to the affected body part. The received EEG signal is correlated with the recorded artificial muscle movements. If the captured EEG signal falls below the desired level, the affected body part will be stimulated by the recorded artificial muscle movements. The system also provides a feature for communicating human intentions as alert message to caregivers, in case of emergency situations. This is achieved by offline training of specific gesture and online gesture recognition algorithm. The recognised gesture is transformed into speech, thus enabling the paralysed to express their feelings to the relatives or friends. Experiments were carried out with the aid of healthy and paralysed subjects. The AMIDL system helped to reduce mental fatigue, miss-operation, frustration and provided continuous control. The thrust of lifting the exoskeleton is also reduced by using light weight wireless electrodes. The proposed system will be a great communication aid for paralysed to express their thoughts and feelings with dear and near ones, thereby enhancing the quality of life.
  • Brain-controlled adaptive lower limb exoskeleton for rehabilitation of post-stroke paralyzed
    P. G. Vinoj, Sunil Jacob, Varun G. Menon, Sreeja Rajesh, Mohammad Reza Khosravi
    IEEE Access, 2019
    Stroke is a standout amongst the most imperative reasons of incapacity on the planet. Due to partial or full paralysis, the majority of patients are compelled to rely upon parental figures and caregivers in residual life. With post-stroke rehabilitation, different types of assistive technologies have been proposed to offer developments to the influenced body parts of the incapacitated. In a large portion of these devices, the clients neither have control over the tasks nor can get feedback concerning the status of the exoskeleton. Additionally, there is no arrangement to detect user movements or accidental fall. The proposed framework tackles these issues utilizing a brain-controlled lower limb exoskeleton (BCLLE) in which the exoskeleton movements are controlled based on user intentions. An adaptive mechanism based on sensory feedback is integrated to reduce the system false rate. The BCLLE uses a flexible design which can be customized according to the degree of disability. The exoskeleton is modeled according to the human body anatomy, which makes it a perfect fit for the affected body part. The BCLLE system also automatically identifies the status of the paralyzed person and transmits information securely using Novel-T Symmetric Encryption Algorithm (NTSA) to caregivers in case of emergencies. The exoskeleton is fitted with motors which are controlled by the brain waves of the user with an electroencephalogram (EEG) headset. The EEG headset captures the human intentions based on the signals acquired from the brain. The brain-computer interface converts these signals into digital data and is interfaced with the motors via a microcontroller. The microcontroller controls the high torque motors connected to the exoskeleton's joints based on user intentions. Classification accuracy of more than 80% is obtained with our proposed method which is much higher compared with all existing solutions.

RECENT SCHOLAR PUBLICATIONS

  • AI assisted BCI system for rehabilitation
    PG Vinoj, VG Menon
    2025 4th International Conference on Advances in Computing, Communication … , 2025
    2025.0
  • Optimizing firefighting performance with FPV drones
    SP Gokul, MJ Sayooj, AA Yunus, A Suresh, PG Vinoj
    AIP Conference Proceedings 3134 (1), 140002 , 2024
    2024.0
    Citations: 2
  • EEG actuated upper limb rehabilitation through LSTM
    VGM Vinoj P G
    International Journal of Science, Technology and Innovation -IHRD 1 (1), 135-138 , 2024
    2024.0
  • IoT enabled renewable energy powered system for efficient sterilization and disposal of masks and gloves
    CM Vinoj P G, Sreeja Rajesh
    Journal of Technology 12 (9), 659-664 , 2024
    2024.0
  • Wearable Fabric Tactile Sensors for Robotic Elderly Assistance
    MC VG, B Paul
    2022 Smart Technologies, Communication and Robotics (STCR), 1-5 , 2022
    2022.0
    Citations: 4
  • Bidirectional Communication Channel for Speech Impaired
    A Ashok, V PG
    2021.0
  • IoT-powered deep learning brain network for assisting quadriplegic people
    PG Vinoj, S Jacob, VG Menon, V Balasubramanian, MJ Piran
    Computers & Electrical Engineering 92, 107113 , 2021
    2021.0
    Citations: 11
  • Deep learning brain actuated bidirectional communication and rehabilitation aid for the quadriplegic
    S Jacob, M Alagirisamy, PG Vinoj, R Parvathi, S Verma
    2021 2nd International Conference on Secure Cyber Computing and … , 2021
    2021.0
    Citations: 3
  • A novel spectrum sharing scheme using dynamic long short-term memory with CP-OFDMA in 5G networks
    S Jacob, VG Menon, S Joseph, PG Vinoj, A Jolfaei, J Lukose, G Raja
    IEEE Transactions on Cognitive Communications and Networking 6 (3), 926-934 , 2020
    2020.0
    Citations: 56
  • Artificial muscle intelligence system with deep learning for post-stroke assistance and rehabilitation
    S Jacob, VG Menon, F Al-Turjman, V PG, L Mostarda
    Ieee Access 7, 133463-133473 , 2019
    2019.0
    Citations: 62
  • Brain-controlled adaptive lower limb exoskeleton for rehabilitation of post-stroke paralyzed
    PG Vinoj, S Jacob, VG Menon, S Rajesh, MR Khosravi
    Ieee Access 7, 132628-132648 , 2019
    2019.0
    Citations: 112
  • A top-up design for pal to vga conversion in real time video processing system
    J Alzubi, S Jacob, VG Menon, S Joseph, PG Vinoj
    2018 International Symposium on Advanced Electrical and Communication … , 2018
    2018.0
    Citations: 5
  • Hybrid brainactuated muscle interface for the physically disabled
    PG Vinoj, S Jacob, VG Menon
    Basic & Clinical Pharmacology & Toxicology 123, 8-9 , 2018
    2018.0
    Citations: 21
  • A Novel Methodology for Detection and Correction of Errors in Memory using Parity Matrix Code (PMC)
    N Ibrahim, PG Vinoj, S Jacob
    International Journal of Advanced Research in Computer science and software … , 2015
    2015.0
    Citations: 2
  • Research on mixed encryption of DES and LFSR and FPGA implementation
    R Tomy, PG Vinoj
    IEEE J Comput Sci Appl 5, 125-132 , 2015
    2015.0
    Citations: 2
  • Zigbee Mesh networking for a swarm of Mobile Robots
    V P G
    International Journal of Computer Applications, ICVCI 2011 5 (11), 8-11 , 2011
    2011.0
  • Multi-Agent Systems and Swarm Intelligence in AI
    PG Vinoj, S Rajesh, C Mohanan
  • A Novel Architecture for High Quality Random Number Generation based on Enhanced WELL Method
    VPG Harigovind
  • Wireless Iris Recognition System using Zigbee
    PG Vinoj
    Computational Intelligence and Healthcare Informatics, 113 , 0
  • Applications and challenges in Service and Security robots
    S Rajesh, C Mohanan, K Hills, PO Pathamuttom, K Kottayam, PG Vinoj, ...

MOST CITED SCHOLAR PUBLICATIONS

  • Brain-controlled adaptive lower limb exoskeleton for rehabilitation of post-stroke paralyzed
    PG Vinoj, S Jacob, VG Menon, S Rajesh, MR Khosravi
    Ieee Access 7, 132628-132648 , 2019
    2019.0
    Citations: 112
  • Artificial muscle intelligence system with deep learning for post-stroke assistance and rehabilitation
    S Jacob, VG Menon, F Al-Turjman, V PG, L Mostarda
    Ieee Access 7, 133463-133473 , 2019
    2019.0
    Citations: 62
  • A novel spectrum sharing scheme using dynamic long short-term memory with CP-OFDMA in 5G networks
    S Jacob, VG Menon, S Joseph, PG Vinoj, A Jolfaei, J Lukose, G Raja
    IEEE Transactions on Cognitive Communications and Networking 6 (3), 926-934 , 2020
    2020.0
    Citations: 56
  • Hybrid brainactuated muscle interface for the physically disabled
    PG Vinoj, S Jacob, VG Menon
    Basic & Clinical Pharmacology & Toxicology 123, 8-9 , 2018
    2018.0
    Citations: 21
  • IoT-powered deep learning brain network for assisting quadriplegic people
    PG Vinoj, S Jacob, VG Menon, V Balasubramanian, MJ Piran
    Computers & Electrical Engineering 92, 107113 , 2021
    2021.0
    Citations: 11
  • A top-up design for pal to vga conversion in real time video processing system
    J Alzubi, S Jacob, VG Menon, S Joseph, PG Vinoj
    2018 International Symposium on Advanced Electrical and Communication … , 2018
    2018.0
    Citations: 5
  • Wearable Fabric Tactile Sensors for Robotic Elderly Assistance
    MC VG, B Paul
    2022 Smart Technologies, Communication and Robotics (STCR), 1-5 , 2022
    2022.0
    Citations: 4
  • Deep learning brain actuated bidirectional communication and rehabilitation aid for the quadriplegic
    S Jacob, M Alagirisamy, PG Vinoj, R Parvathi, S Verma
    2021 2nd International Conference on Secure Cyber Computing and … , 2021
    2021.0
    Citations: 3
  • Optimizing firefighting performance with FPV drones
    SP Gokul, MJ Sayooj, AA Yunus, A Suresh, PG Vinoj
    AIP Conference Proceedings 3134 (1), 140002 , 2024
    2024.0
    Citations: 2
  • A Novel Methodology for Detection and Correction of Errors in Memory using Parity Matrix Code (PMC)
    N Ibrahim, PG Vinoj, S Jacob
    International Journal of Advanced Research in Computer science and software … , 2015
    2015.0
    Citations: 2
  • Research on mixed encryption of DES and LFSR and FPGA implementation
    R Tomy, PG Vinoj
    IEEE J Comput Sci Appl 5, 125-132 , 2015
    2015.0
    Citations: 2
  • AI assisted BCI system for rehabilitation
    PG Vinoj, VG Menon
    2025 4th International Conference on Advances in Computing, Communication … , 2025
    2025.0
  • EEG actuated upper limb rehabilitation through LSTM
    VGM Vinoj P G
    International Journal of Science, Technology and Innovation -IHRD 1 (1), 135-138 , 2024
    2024.0
  • IoT enabled renewable energy powered system for efficient sterilization and disposal of masks and gloves
    CM Vinoj P G, Sreeja Rajesh
    Journal of Technology 12 (9), 659-664 , 2024
    2024.0
  • Bidirectional Communication Channel for Speech Impaired
    A Ashok, V PG
    2021.0
  • Zigbee Mesh networking for a swarm of Mobile Robots
    V P G
    International Journal of Computer Applications, ICVCI 2011 5 (11), 8-11 , 2011
    2011.0
  • Multi-Agent Systems and Swarm Intelligence in AI
    PG Vinoj, S Rajesh, C Mohanan
  • A Novel Architecture for High Quality Random Number Generation based on Enhanced WELL Method
    VPG Harigovind
  • Wireless Iris Recognition System using Zigbee
    PG Vinoj
    Computational Intelligence and Healthcare Informatics, 113 , 0
  • Applications and challenges in Service and Security robots
    S Rajesh, C Mohanan, K Hills, PO Pathamuttom, K Kottayam, PG Vinoj, ...

Publications

Journal/Papers Published
Over 20 publications in international journals, 5 SCI Indexed, 7 Scopus Indexed Journals and 260 citations with an h-index of 5.
A. Publications in International Journals with SCI Index

1. PG Vinoj, Sunil Jacob, Varun G Menon, Sreeja Rajesh, Mohammad Reza Khosravi “Brain-controlled adaptive lower limb exoskeleton for rehabilitation of post-stroke paralyzed”, IEEE Access, 2019
2. S Jacob, VG Menon, F Al-Turjman, PG Vinoj, “Artificial muscle intelligence system with deep learning for post-stroke assistance and rehabilitation, IEEE Access, 2019
3. Sunil Jacob, Varun G Menon, Saira Joseph, PG Vinoj, Alireza Jolfaei, Jibin Lukose, Gunasekaran Raja, “A novel spectrum sharing scheme using dynamic long short-term memory with CP-OFDMA in 5G networks”, IEEE Transactions on Cognitive Communications and Networking,2020
4. PG Vinoj, Sunil Jacob, Varun G Menon, Venki Balasubramanian, Md Jalil Piran, “IoT-powered deep learning brain network for assisting quadriplegic people”, Elsevier, Computers & Electrical Engineering,2021
5. PG Vinoj, Sunil Jacob, Varun G Menon, “Hybrid brain actuated muscle interface for the physically disabled, Basic & Clinical Pharmacology & Toxicology”, Wiley,2018

B. Publications in Scopus Indexed journals/International/National Conferences
1. Vinoj P.G, Vineeth VV, WIRELESS IRIS RECOGNITION SYSTEM USING ZIGBEE International Conference on VLSI and Communication, ICVCom, 16-18 April 2009.

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patent Published and Granted

1) India Utility Patent Titled “Deep Learning Brain controlled assistive technology for the Paralyzed” published on 10/11/2022 in the field of Biomedical Engineering.
2) Indian Design Patent titled “MEDICAL TROLLEY INTEGRATED WITH WASTE DISPOSAL SYSTEM (SET), Granted on 29/07/2025

INDUSTRY EXPERIENCE

• R&D engineer Robhatah Robotic Solution, Bangalore for 2 years (2008-2010)