GUI system for elders/patients in intensive care

Raheja, Jagdish Lal; Sangwan, Dhiraj; Gopinath, Deepak; Chaudhary, Ankit

doi:10.1109/itmc.2014.6918605

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…For example, we can ask the computer to drive a car to the south by pointing in that direction. Manipulative gestures serves as a natural way to interact with virtual objects [10][11][12]. Sign language is an important case of communicative gesture [13].…”

Section: Hand Gesture Recognitionmentioning

confidence: 99%

Android Based Portable Hand Sign Recognition System

Raheja¹,

Singhal²,

Chaudhary³

2015

Gate to Computer Science and Research

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These days mobile devices like phones or tablets are very common among people of all age. They are connected with network and provide seamless communications through internet or cellular services. These devices can be a big help for the people who are not able to communicatie properly and even in emergency conditions. A disabled person who is not able to speak or a person who speak a different language, these devices can be a boon for them as understanding, translating and speaking systems for these people. This chapter discusses a portable android based hand sign recognition system which can be used by disabled people. This chapter shows a part of on-going project. Computer Vision based techniques were used for image analysis and PCA was used after image tokenizer for recognition. This method was tested with webcam results to make system more robust.

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Section: Hand Gesture Recognitionmentioning

confidence: 99%

Android Based Portable Hand Sign Recognition System

Raheja¹,

Singhal²,

Chaudhary³

2015

Gate to Computer Science and Research

Self Cite

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“…However, despite the fact that scholars have concentrated on building assistance programmes for patients who are hospitalised, bedridden, and individuals who are unable to move about and have limited ability for engagement (for example, monitoring systems for patients based on brainwave activity interface systems, namely interaction systems based on hand gestures, etc.) (Gao et al, 2003;Raheja et al, 2014;Saha et al, 2018), just a little amount of study has been carried out thus far to determine the needs of patients in this category and then come up with a plan to meet their specific needs. To mitigate the effects of this emergency on the healthcare system, the necessities that a patient in a hospital smart bed is expected to have are clearly perceived and well comprehended.…”

Section: Introductionmentioning

confidence: 99%

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

Ayouni¹,

Maddeh²,

Al-Otaibi³

et al. 2023

Journal of Disability Research

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An Internet of Things-based automated patient condition monitoring and detection system is discussed and built in this work. The proposed algorithm that underpins the smart-bed system is based on deep learning. The movement and posture of the patient’s body may be determined with the help of wearable sensor-based devices. In this work, an internet protocol camera device is used for monitoring the smart bed, and sensor data from five key points of the smart bed are core components of our approach. The Mask Region Convolutional Neural Network approach is used to extract data from many important areas from the body of the patient by collecting data from sensors. The distance and the time threshold are used to identify motions as being either connected with normal circumstances or uncomfortable ones. The information from these key locations is also utilised to establish the postures in which the patient is lying in while they are being treated on the bed. The patient’s body motion and bodily expression are constantly monitored for any discomfort if present. The results of the experiments demonstrate that the suggested system is valuable since it achieves a true-positive rate of 95% while only yielding a false-positive rate of 4%.

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“…The core business of the hospital is to provide health services. A good hospital cannot only offer professional medical personnel but also provides the best facilities and an excellent patient-care system [3]. At the same time, monitoring patient clinical status is essential, particularly in intensive care units (ICUs) [4].…”

Section: Introductionmentioning

confidence: 99%

FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases

Caraka

Chen

Lee

et al. 2021

Preprint

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Background: In the heart data mining and machine learning, dimension reduction is needed to remove the multicollinearity. Meanwhile, it has been proven to improves the interpretation of the parameter model. In addition, dimension reduction is also can increase the time of computing in high dimensional data. Methods: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital , following emergency department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM’s). Results: During the analysis, we measure the performance and calculate the time computing of GLLVM with employing variational approximation and Laplace approximation, and compare the different distributions including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedi, respectively. Conclusions: In a nutshell, GLLVM’s leads as best performance reaching the accuracy 98% comparing other methods. In line with this, we get the best model negative binomial and Variational approximation which provides the best accuracy by accruacy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.

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GUI system for elders/patients in intensive care

Cited by 6 publications

References 20 publications

Android Based Portable Hand Sign Recognition System

Android Based Portable Hand Sign Recognition System

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

FastGLLVM: Big Data Ordination Towards Intensive Care Event Count Cases

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