Computational approach to body mass index estimation from dressed people in 3D space

Jiang, Min; Shang, Yuanyuan; Guo, Guodong

doi:10.1049/iet-ipr.2019.1170

Cited by 3 publications

(2 citation statements)

References 33 publications

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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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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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“…Often times, these types of devices have limited use for medical purposes, however, Jiang et al [9] propose to use RGB-D devices, to calculate BMI from 3D captured data, by estimating the height of each individual, along with an approximate weight extracted from the volume. The results of their study can find the BMI within an error of 2.54 kg/m 2 .…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

et al. 2022

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Artificial intelligence techniques have been increasingly applied in healthcare to help in many areas, from assisting clinical diagnoses to preventing diseases. In this paper, a machine learning approach to predict cholesterol levels using non-invasive and easy-to-collect data is presented. Specifically, it uses clinical and anthropometric data gathered by nutritionists during weight loss intervention (dieting) periods. The prediction power analysis of different patient variables is aimed at improving both non-invasive diagnosis quality and screening of associated diseases. Moreover, a clustering analysis has been carried out to identify different groupings of patients that might share some characteristics that have so far remained inconspicuous but might contain a valuable diagnosis or prognosis information for clinical experts.The experiments show a mean absolute percentage error rate (MAPE) of 4.39% in cholesterol estimation via regression, as well as clustering of patients within four profiles in which variable values share commonalities among cluster members.INDEX TERMS digital health assessment, 3D body reconstruction, clinical data regression, patient data clustering, pattern recognition.

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A Novel Approach in Web Based 3D Virtualization For Healthcare

Banu Rekha,

Charushree,

Divyadharshan

et al. 2023

2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)

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Computational approach to body mass index estimation from dressed people in 3D space

Cited by 3 publications

References 33 publications

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

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

A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

A Novel Approach in Web Based 3D Virtualization For Healthcare

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