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AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities

- Jacob, Sunil, Alagirisamy, Mukil, Xi, Chen, Balasubramanian, Venki, Srinivasan, Ram

Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
Date: 2021
Type: Text , Journal article
Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
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Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**

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Short-term forecasting of load and renewable energy using artifical neural network

- Srinivasan, Ram, Balasubramania, Venki, Selvaraj, Buvana

Authors: Srinivasan, Ram , Balasubramania, Venki , Selvaraj, Buvana
Date: 2021
Type: Text , Journal article
Relation: International journal of engineering trends and technology Vol. 69, no. 6 (2021), p. 175-181
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Description: Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for short-term electrical load forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularisation (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand. © 2021 Seventh Sense Research Group®

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A supervised learning model to identify the star potential of a basketball player

- Srinivasan, Ram, Balasubramanian, Venki, Vidyasagar, Abhishek

Authors: Srinivasan, Ram , Balasubramanian, Venki , Vidyasagar, Abhishek
Date: 2022
Type: Text , Journal article
Relation: Expert Systems Vol. 39, no. 5 (2022), p.
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Description: Basketball is a mathematical game with many abstract data interpretations. An average fan ceases to witness the revolution in sports, which is influenced using data science and analytics unless someone brings it to light. Nowadays, teams look at data and tend to make decisions on scouting the player for the team. The decision making for the coaches can be made easier using machine learning algorithms to identify the star potential of players. The paper provides a novel algorithm by building a machine learning model on all players to predict whether the player is a star or not. Besides, an interactive user interface is developed for coaches to input the player's data and to make an informed decision based on the prediction. © 2021 John Wiley & Sons Ltd.

A secured real-time IoMT application for monitoring isolated COVID-19 patients using edge computing

- Balasubramanian, Venki, Sulthana, Rehena, Stranieri, Andrew, Manoharan, G., Arora, Teena, Srinivasan, Ram, Mahalakshmi, K., Menon, Varun

Authors: Balasubramanian, Venki , Sulthana, Rehena , Stranieri, Andrew , Manoharan, G. , Arora, Teena , Srinivasan, Ram , Mahalakshmi, K. , Menon, Varun
Date: 2021
Type: Text , Conference paper
Relation: 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, Shenyang, China, 20-22 October 2021, Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021 p. 1227-1234
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Description: Internet of Medical Things (IoMT) is an emerging technology whose capabilities to self-organize itself on-the-fly, to monitor the patient's vital health data without any manual entry and assist early human intervention gave birth to smart healthcare applications. The smart applications can be used to remotely monitor isolated patients during this COVID-19 pandemic. Remote patient monitoring provides an opportunity for COVID-19 patients to have vital signs and other indicators recorded regularly and inexpensively to provide rapid and early warning of conditions that require medical attention using secured edge and cloud computing. However, to gain the confidence of the users over these applications, the performance of healthcare applications should be evaluated in real-time. Our real-time implementation of IoMT based remote monitoring application using edge and cloud computing, along with empirical evaluation, show that COVID-19 patients can be monitored effectively not only with mobility but also helps the health care professionals to generate consolidated health data of the patient that can guide them to obtain medical attention. © 2021 IEEE.

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