The advancements in mixed reality (MR) technology in recent years have provided us with excellent prospects for creating novel approaches to supplement conventional physiotherapy to maintain a sufficient quantity and quality of rehabilitation. The use of MR systems to facilitate patients’ participation in intensive, repetitive, and task-oriented practice using cutting-edge technologies to enhance functionality and facilitate recovery is very encouraging. Multiple studies have found that patients who undergo therapy using MR experience significant improvements in upper limb function; however, assessing the efficacy of MR is challenging due to the wide variety of methods and tools used. Because of these challenges, a novel approach, gamified MR-based solution for upper extremity rehabilitation, is proposed, which is an MR application for the Microsoft HoloLens 2, complete with game levels, and can measure the ranges of motion of the arm joints. The proposed rehabilitative system’s functionality and usability were evaluated with ten healthy adult participants with no prior arm-related injuries and two occupational therapists (OTs). The system successfully provided rehab exercises for upper limb injuries through interactive mixed-reality games. The system can mimic upper limb behavior without additional sensors during rehab sessions. Unlike previously researched technologically-based rehabilitation methods, this method can integrate arm–joint data within the application and are independent of one another. The results and comparisons show that this system is relevant, accurate, and superior to previous VR-based rehabilitation methods because the VR-based system is blind to the surroundings, whereas the proposed approach has spatial awareness of the environment.

Information Extraction (IE) from textual documents locates important entities and their underlying connections using automated systems which are crucial to different applications including Data Mining (DM), Question Answering (QA), Machine Translation (MT) and so on. Named Entity Recognition (NER) being a sub-component of Natural Language Processing (NLP) is an IE task which aims at locating the textual presence of entities belonging to a prescribed set of classes. Due to its political and geographical influence, Bangla language is widely spoken around the globe and it is important to enrich its linguistic knowledge through NLP tools where NER is a common pre- processing step. The expeditiously growing World Wide Web (WWW) containing Bangla textual documents is in a formative stage with the proliferation of Bangla online newspapers and researchers have applied traditional classic learning algorithms for Bangla NER task while few researchers have used hand- crafted rules. Technological improvements show that with the capability of Deep Learning technique, NER performance can be boosted and hence this work is an effort to apply a variation of Recurrent Neural Network (RNN); especially a Gated Recurrent Unit (GRU) model for developing a Bangla NER task with a manually annotated dataset. The evaluation of our experimental results discovers how our approach can perform better when applied on a large scale dataset.

This paper addresses the challenge of precise control and robust trajectory tracking for Four Mecanum Wheeled Mobile Robots (FMWMR) operating in environments where time-sensitive responses are critical and external conditions may vary. Traditional control approaches often fall short due to slow convergence rates and limited resilience to disturbances. To tackle these issues, we propose a novel control framework that integrates Prescribed-Time (PT) control, Cascaded Extended State Observers (CESO), and Pure Pursuit Guidance (PPG). The PT control component ensures that the system converges to the desired state within a user-defined prescribed time, regardless of initial conditions or disturbances, while CESO enhances disturbance estimation through a multi-layered observer structure, ensuring rapid and reliable adaptation under varying conditions. PPG is incorporated to improve trajectory tracking by guiding the robot toward a designated lookahead point, aligning the robot’s velocity with the target’s direction to enable smooth and computationally efficient path following that leverages FMWMR’s omnidirectional capabilities. We provide theoretical guarantees of stability and convergence, demonstrating that the proposed control architecture outperforms traditional finite-time and ESO-based control methods in terms of tracking accuracy, disturbance rejection, and adherence to temporal constraints. Numerical simulations validate the effectiveness and robustness of the framework, highlighting its potential for real-time applications in environments that demand high precision and adaptability.

The increasing number of powered wheelchair users, driven by an aging global population and advancements in medical care, highlights a critical need to understand their unique experiences and challenges, particularly regarding assistive technologies and caregiver support. Our study, drawing on qualitative data from 11 participants with diverse disabilities, investigates the complex interplay of technology, independence, and socio-economic factors in their lives. It reveals a significant gap between the potential and actual use of assistive technologies, underscoring the importance of innovation in areas where users heavily rely on caregivers. The implication of this study includes emphasizing the necessity for inclusive, adaptable, and economically accessible assistive technologies and highlighting the vital role of human support in augmenting the quality of life for individuals with mobility impairments.