Advancing Remote Healthcare Using Humanoid and Affective Systems

Tripathi, Utkarsh; J, Ritvik Saran; Chamola, Vinay; Jolfaei, Alireza; Chintanpalli, Ananthakrishna

doi:10.1109/jsen.2021.3049247

Cited by 24 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using advanced sensing and imaging technology, users can interact with and manipulate real-world and virtual objects, and surroundings in mixed reality [32], [33]. Without ever taking off the headset, mixed reality enables users to see and become fully immersed in the world around them while interacting with a virtual environment with one's hand.…”

Section: ) Mixed Realitymentioning

confidence: 99%

Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare

et al. 2022

View full text Add to dashboard Cite

The COVID-19 pandemic has revealed several limitations of existing healthcare systems. Thus, there is a surge in healthcare innovation and new business models using computer-mediated virtual environments to provide an alternative healthcare system. Today, digital transformation is not limited to virtual communication alone but encompasses digitalizing the network of social connections in the healthcare industry using metaverse technology. The metaverse is a universal and immersive virtual world facilitated by virtual reality (VR) and augmented reality (AR). This paper presents the first effort to offer a comprehensive survey that examines the latest metaverse developments in the healthcare industry, which covers seven domains: telemedicine, clinical care, education, mental health, physical fitness, veterinary, and pharmaceuticals. We review metaverse applications and deeply discuss technical issues and available solutions in each domain that can help develop a self-sustaining, persistent, and future-proof solution for medical healthcare systems. Finally, we highlight the challenges that must be tackled before fully embracing the metaverse for the healthcare industry.

show abstract

Section: ) Mixed Realitymentioning

confidence: 99%

Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare

et al. 2022

View full text Add to dashboard Cite

show abstract

“…It is also noteworthy that techniques applied in this work can also be extended for application in BCI-based humanoid control and other health care applications. 30,31…”

Section: F I G U R E 12mentioning

confidence: 99%

Brain–computer interface‐based target recognition system using transfer learning: A deep learning approach

Chen

Zhang

et al. 2021

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

The traditional target recognition and classification is mostly done manually, with low efficiency and high cost. Improving the level of target recognition automatically has become an important research topic. This paper proposes a target recognition method based on transfer learning to effectively complete the classification and recognition of targets using a brain-computer interface (BCI) model. Based on the construction of the faster-RCNN deep learning model, the pre-training of the model is achieved by VGG-16 and Inception-v2, and the transfer learning algorithm is used to optimize the faster-RCNN deep learning model based on the kinematics model. Experiments are carried out with the aim to detect tableware by the persons whose brain signals recognition rate has been substantially improved using faster-RCNN. Compared with the traditional recognition methods, the results at the lab-scale level illustrated that the proposed algorithm can effectively improve the speed and accuracy of target recognition by using the BCI model to classify tableware of different colors and shapes in a complex background.

show abstract

“…The increase in older adults is a concern for first-world countries, as reflected in a smaller workforce in most production areas. Among these areas is health care as collaborating with nursing tasks [3], [4]. Current service robots that perform household tasks, nursing work, or assist in office tasks serve partial functions in human activities.…”

Section: Introductionmentioning

confidence: 99%

“…In the searching phase, it computes mutual information assuming equal a priori probability that a feature vector belongs to each object. Thus mutual information is calculated as shown in (4).…”

mentioning

confidence: 99%

Active object search using a pyramid approach to determine the next-best-view

Flores-Rodriguez

Trujillo-Romero

González-Barbosa

2022

IJRA

View full text Add to dashboard Cite

<p>The development of service robotics continues to arouse interest in the scientific community due to the complexity of the activities performed like interaction in human environments, identifying and manipulating objects, and even learning by themselves. This paper proposed to improve the perception of the environment by searching for objects in service robotics tasks. We present the development and implementation of an active object search method based on three main phases: Firstly, image pyramid segmentation to examine in detail the im- age features. Second step, object detection at each level of the pyramid through a local feature descriptor and a mutual information calculation. Finally, the next camera position selection through analyzing the object detections accumulation in the pyramid. To evaluate the implementation of the proposed method, we use a NAO robot in a familiar place for humans, such as an office or a home. Ordinary objects are part of our database with the premise that a robot must know them before looking for an object. The results in the experiments showed an acceptable performance in simulation and with a real platform.</p>

show abstract

Advancing Remote Healthcare Using Humanoid and Affective Systems

Cited by 24 publications

References 40 publications

Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare

Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare

Brain–computer interface‐based target recognition system using transfer learning: A deep learning approach

Active object search using a pyramid approach to determine the next-best-view

Contact Info

Product

Resources

About