Assessment of hospital waste management in Tanta University

El-Shinawey, Amira K. El-Dawla Mokhtar; Atalla, Asmaa Abdel-Reheem; Abbas, Khalil Mohamed; Atlam, Samir Abd El-Mageed

doi:10.4103/tmj.tmj_19_17

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Visual-based fall detecting systems can monitor the elderly throughout surveillance cameras in real-time in a home care setting. Thermal vision cameras can also be applied in certain cases [9][10][11]. The criteria chosen for fall detection after subtraction can be further categorized into three branches, namely, time of inactivity, body movement, and posture recognition.…”

Section: Visual-based Fall Detecting Systemsmentioning

confidence: 99%

Feasibility of Using Floor Vibration to Detect Human Falls

Shao

Wang

Song

et al. 2020

IJERPH

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With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

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Section: Visual-based Fall Detecting Systemsmentioning

confidence: 99%