User Requirements and Analysis of Preeclampsia Detection done through a Smart Bracelet

• URL: http://arxiv.org/abs/2102.09346v1
• Date: Fri, 29 Jan 2021 17:23:36 GMT
• Title: User Requirements and Analysis of Preeclampsia Detection done through a Smart Bracelet
• Authors: Iuliana Marin, Andrei Vasilateanu, Bujor Pavaloiu, Nicolae Goga
• Abstract summary: This paper is based on a survey completed by persons of different ages regarding the use of smart bracelets for detecting preeclampsia. The aim is to decide upon its popularity among people and to determine the user requirements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical students along with the medical staff have to monitor the state of the patients by using modern devices which have to offer precise results in a short amount of time, so that the intervention to be done as soon as possible. E-learning systems for blood pressure monitoring are used and new methods of patient observation, evaluation and treatment are applied compared to classical intervention. Based on this, medical students can improve their knowledge for the practical training. In the medical activities specialized devices occupy an important place. A device that can monitor the blood pressure is a smart bracelet that incorporates a pressure sensor along the wrist for continuous recording of blood pressure values. This enables the prediction of the emergency disorders using a decision support system. It facilitates the learning of new intervention approaches and boosts the responsiveness among learners. According to the World Health Organization, hypertensive disorders affect about 10% of pregnant women worldwide and are an important cause of disability and long-term death among mothers and children. This paper is based on a survey completed by persons of different ages and having various specialization domains regarding the use of smart bracelets for detecting preeclampsia. The aim is to decide upon its popularity among people and to determine the user requirements. The pregnant women will be constantly monitored, doctors can update the diagnosis of the patient. The medical students can learn from the critical situations and benefit from these cases while learning. The results of the survey showed that most of the interviewed persons consider the existence of such a device to be very useful, mostly the female individuals would feel more comfortable to have their blood pressure monitored during pregnancy.

Related papers

• A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images [71.26717896083433]
  Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging.
  arXiv  Detail & Related papers  (2023-09-05T19:45:09Z)
• HEAR4Health: A blueprint for making computer audition a staple of modern healthcare [89.8799665638295]
  Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems. Computer audition can be seen to be lagging behind, at least in terms of commercial interest. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data.
  arXiv  Detail & Related papers  (2023-01-25T09:25:08Z)
• User-Driven Research of Medical Note Generation Software [49.85146209418244]
  We present three rounds of user studies carried out in the context of developing a medical note generation system. We discuss the participating clinicians' impressions and views of how the system ought to be adapted to be of value to them. We describe a three-week test run of the system in a live telehealth clinical practice.
  arXiv  Detail & Related papers  (2022-05-05T10:18:06Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• Assessment of Fetal and Maternal Well-Being During Pregnancy Using Passive Wearable Inertial Sensor [0.0]
  This paper focuses on a device that can be used effectively by the mother herself with minimal supervision. The device proposed uses a belt with a single accelerometer over the mother's uterus to record the required information.
  arXiv  Detail & Related papers  (2021-11-19T06:53:39Z)
• Securing the Network for a Smart Bracelet System [0.0]
  The smart bracelet monitors the pregnant women, but also other users who wish to have their blood pressure under control. The doctors, the medical staff, user and user's family and caregivers have access to the health recordings belonging to the monitored user. Security is a main feature of the electronic healthcare system based on the smart bracelet.
  arXiv  Detail & Related papers  (2021-02-09T11:50:02Z)
• A Supervised Learning Approach for Robust Health Monitoring using Face Videos [32.157163136267954]
  Non-contact, device-free human sensing methods can eliminate the need for specialized heart and blood pressure monitoring equipment. In this paper, we used a non-contact method that only requires face videos recorded using commercially-available webcams. The proposed approach used facial recognition to detect the face in each frame of the video using facial landmarks.
  arXiv  Detail & Related papers  (2021-01-30T22:03:16Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Cyber-Physical Platform for Preeclampsia Detection [0.0]
  The present paper details a cyber-physical system built around an intelligent bracelet for monitoring hypertension-related conditions tailored to pregnant women. The bracelet uses a microfluidic layer that is compressed by the blood pressing against the arterial wall. Integrated sensors register the waveform and send it to the user's smartphone, where the systolic and diastolic values are determined.
  arXiv  Detail & Related papers  (2020-09-04T10:15:00Z)
• Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
  Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD. Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
  arXiv  Detail & Related papers  (2020-08-21T20:22:55Z)
• Little Motion, Big Results: Using Motion Magnification to Reveal Subtle Tremors in Infants [0.0]
  Infants exposed to opioids during pregnancy often show signs and symptoms of withdrawal after birth. The constellation of clinical features, termed as Neonatal Abstinence Syndrome (NAS), include tremors, seizures, irritability, etc. Monitoring with FNASS requires highly skilled nursing staff, making continuous monitoring difficult. In this paper we propose an automated tremor detection system using amplified motion signals.
  arXiv  Detail & Related papers  (2020-08-01T15:35:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.