Body Weight Measurement Using Image Processing Based on Body Surface Area and Elliptical Tube Volume

In aviation, transportation is a direct connection between the fuel consumed by aircrafts and carbon dioxide emissions. A new solution for determination of the necessary aircraft fuel will lead to companies’ savings and environment protection. The proposed method considers the commercial passenger aviation. The software developed by International Civil Aviation (ICAO) use a methodology for simulation of necessary fuel and for computing carbon emissions. A part of this methodology uses fixed value for passenger’s weight determination. Not all passengers from a flight have the same weight and, thus, the proposed method could replace the fixed value. Based on mathematical morphology operators, the new algorithm will improve the accuracy of aircraft fuel consumption with significant results on environmental protection.

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“…The accuracy of weight detection from images is influenced by factors such as camera orientation, pixel resolution and lighting [7].…”

Section: W = Body Weight H = Body Heightmentioning

confidence: 99%

Fuel burn reduction in commercial aviation using mathematical morphology

Abrudan

Drăgulinescu²,

Preda³

et al. 2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

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“…The inclusion of the cot’s weight to the patient’s body weight in such setups necessitates an over-design of the load cells to ensure excellent accuracy at their lower operating ranges, which also leads to raising the measuring device’s complexity and cost [ 22 ]. Without using force sensors, certain experimental techniques for bodyweight assessment are confined to image processing work grounded on the surface area of the body and volume of the elliptic tube [ 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics

et al. 2022

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Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients’ lateral positions both ways, comfortably massage patients’ backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff’s safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans.

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“…General-purpose weight estimation Body weight or body mass index estimation from full-body RGB, depth or RGB-D images has been addressed by numerous works, which predominantly rely on handcrafted geometric or biometric features [3,13,14,16,24,37,38]. In a common approach, the subject is segmented from the background, features are subsequently extracted from the silhouette, and weight regression is performed by a neural network or support vector regression [13,16,24,38].…”

Section: Related Workmentioning

confidence: 99%

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

et al. 2021

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Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

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Body Weight Measurement Using Image Processing Based on Body Surface Area and Elliptical Tube Volume

Cited by 8 publications

References 9 publications

Fuel burn reduction in commercial aviation using mathematical morphology

Fuel burn reduction in commercial aviation using mathematical morphology

Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

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