Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation

Covaciu, Florin; Pîslă, Adrian; Iordan, Anca

doi:10.3390/s21041537

Cited by 20 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When using this exoskeleton, the participants felt submerged, having a flight sensation, which proved that FlyJacket can be used for teleoperating a real drone [9]. The integration of Virtual Reality tools and Artificial Intelligence algorithms in medical applications was also demonstrated in [10], emphasizing the importance of these tools in the development of innovative systems able to work safely in human populated environments. In order to improve the accuracy of a gyroscope with a micromechanical system, noise modeling was performed using and evaluating an advanced deep recurrent neural network [11].…”

Section: Introductionmentioning

confidence: 90%

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Covaciu

Iordan

2022

Micromachines

Self Cite

View full text Add to dashboard Cite

In recent years, drones have been widely used in various applications, from entertainment, agriculture, their use in photo and video services, military applications and so on. The risk of accidents while using a drone is quite high. To meet this risk, the most important solution is to use a device that helps and simplifies the control of a drone; in addition, the training of drone pilots is very important. To train the drone pilots, both physical and virtual environments can be used, but the probability of an accident is higher for beginners, so the safest method is to train in a virtual environment. The aim of this study is to develop a new device for controlling a drone in a virtual environment. This device is attached to the upper limb of the person involved in the control of that drone. For precise control, the newly created device uses MEMS sensor technology and artificial intelligence-specific methods.

show abstract

Section: Introductionmentioning

confidence: 90%

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Covaciu

Iordan

2022

Micromachines

Self Cite

View full text Add to dashboard Cite

show abstract

“…The K-nearest neighbours method [25] is the easiest machine learning method, and the fact that it fetches the right results in a shorter time has led to its great use for both classification and regression problems. This method is based on a feature similarity, which represents that the similarity level of the values' features to those of the training set determines how a new input will be predicted.…”

Section: Selected Machine Learning Methodsmentioning

confidence: 99%

An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

Iordan

2024

Mathematics

Self Cite

View full text Add to dashboard Cite

Software effort estimation has constituted a significant research theme in recent years. The more important provocation for project managers concerns reaching their targets within the fixed time boundary. Machine learning strategies can lead software management to an entire novel stage. The purpose of this research work is to compare an optimized long short-term memory neural network, based on particle swarm optimization, with six machine learning methods used to predict software development effort: K-nearest neighbours, decision tree, random forest, gradient boosted tree, multilayer perceptron, and long short-term memory. The process of effort estimation uses five datasets: China and Desharnais, for which outputs are expressed in person-hours; and Albrecht, Kemerer, and Cocomo81, for which outputs are measured in person-months. To compare the accuracy of these intelligent methods four metrics were used: mean absolute error, median absolute error, root mean square error, and coefficient of determination. For all five datasets, based on metric values, it was concluded that the proposed optimized long short-term memory intelligent method predicts more accurately the effort required to develop a software product. Python 3.8.12 programming language was used in conjunction with the TensorFlow 2.10.0, Keras 2.10.0, and SKlearn 1.0.1 to implement these machine learning methods.

show abstract

“…In addition to its application in upper-limb rehabilitation, VR technology has also been used for the rehabilitation of lower limbs. Covaciu et al (2021) introduced the development of a VR simulator for ankle rehabilitation after a stroke. The simulator used sensors equipped with a gyroscope and accelerometer to detect and track the patient’s motions and muscle activity.…”

Section: Enhanced Physical Training Through Sensor Fusion-based Virtu...mentioning

confidence: 99%

Sensor fusion-based virtual reality for enhanced physical training

Li,

Fan,

Deng

et al. 2024

RIA

View full text Add to dashboard Cite

Purpose This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training. Design/methodology/approach The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation. Findings Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training. Originality/value The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

show abstract

Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation

Cited by 20 publications

References 34 publications

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

Sensor fusion-based virtual reality for enhanced physical training

Contact Info

Product

Resources

About