Biomedical and Health Sciences

The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.
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The dataset contains motion capture data of the human hand of 20 healthy subjects acquired using two different motion capture technology (wearable IMU and camera-based). This database provides an opportunity to expand the fields of research involving the hands or their range of mobility. Indeed, using this database to train AI's net to recognise gestures/tasks is an excellent beginning point for expanding the field of human-robot collaboration.
This work is licensed under the Creative Commons Attribution 4.0 International License.
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The formation and changes of the pulse wave areaffected by the functional status of the heart, blood, and arterial vessels. The pulse signal covers pathological information in the cardiovascular system, and the doctor of traditional Chinese medicine (TCM) can diagnose diseases by feeling the pulse. The diagnosis of diseases through feeling the pulse of TCM primarily relies on the doctor’s feelings and subjective experience, and it lacks objectified data.
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This dataset contains the raw data to figure 6 of the paper "Modeling and evaluation of a rate-based transcutaneous blood gas monitor" that has been submitted to IEEE Transactions on Biomedical Engineering
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N/A
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Background In traditional meta-analyses, ischemic optic neuropathy (ION) has been shown to be ineffective. The effects of all/10 drugs for the treatment of ION were assessed through a meta-analysis using direct and indirect comparisons of multiple treatments.
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The data collection questionnaire consisted of two sections. One section involved the collection of data via Google Forms questionnaires, and the other involved the collection of WhatsApp voice samples. There were three subsections in the questionnaire section. The first consisted of the individual's basic information, such as email address, name, and identification number. The second was the personal health questionnaire depression scale (PHQ8), which included 8 groups of statements, and the third was the Beck Depression Inventory-II, which contained 21 groups of statements.
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In this paper, we propose a mecanum-built perturbation-based balance training (M-PBT) device to train a person with a neurological disorder or an elderly person to regain their deteriorated motor adaptive skill to prevent a fall. The following are the features of the device: to challenge the trainees to predict the fall direction, the device (1) generates multi-directional fall options that simulate a slip and trip scenario; (2) is portable to assist in-patients’ rehabilitation; (3) possesses qualities of modified constraint-induced movement therapy (mCIMT).
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Personal assistive devices for rehabilitation will be in increasing demand during the coming decades due to demographic change, i.e., an aging society. Among the elderly population, difficulty in walking is the most common problem. Even though there are commercially available lower limb exoskeleton systems, the coordination between user and device still needs to be improved to achieve versatile personalized gait. To tackle this issue, an advanced EXOskeleton framework for Versatile personalized gaIt generation with a Seamless user-exo interface (called "EXOVIS") is proposed in this study.
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