Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data

Abstract

This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.

Personalized computed tomography – Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence

“…Standardized volume data, such as the expected residual volume of the liver, could make a decisive contribution to therapy in the planning phase, e. g. before oncological liver surgery, selective radioembolization or liver transplants [20][21][22][23]. Automatic implementation of the collected parameters in a digital patient file could also ensure rapid availability of relevant data, such as during follow-up examinations in other institutions or when determining the dose of medication in emergency situations without the possibility of patient history or measurement, for example before lysis therapy for stroke patients [12]. In the context of quality assurance, inclusion of this data supports investigation of causes for exceeding the CT dose reference values and to document justifiable reasons for this.…”

“…Die automatische Implementierung der erhobenen Parameter in eine digitale Patientenakte könnte zudem die schnelle Verfügbarkeit relevanter Daten, z. B. bei Folgeuntersuchungen in anderen Instituten oder bei der Dosisfindung von Medikamenten in Notfallsituationen ohne Anamnese-oder Messmöglichkeit, beispielswiese vor einer Lysetherapie bei Schlaganfallpatienten, gewährleisten [12]. Diese Dateneinpflegung hilft auch im Rahmen der Qualitätssicherung, Ursachen für Überschreitungen der CT-Dosisreferenzwerte zu untersuchen und rechtfertigende Gründe zu dokumentieren.…”

“…B. bei bewusstlosen, immobilen oder unkooperativen Patienten oft an seine Grenzen. Einen vielversprechenden Lösungsansatz bieten hier neuartige 3D-Kamerasysteme, die mittels Infrarottechnik Oberflächen-und Tiefendaten aufzeichnen [12]. In neueren Com-putertomografen werden dazu mittels maschinellen Lernens Avatare als digitale Zwillinge in die Oberflächeninformation der gelagerten Patienten eingepasst.…”

“…In everyday clinical practice, this procedure is often limited, e. g. with unconscious, immobile or uncooperative patients. New 3D camera systems, which record surface and depth data using infrared technology offer a promising solution to this issue [12]. In newer computer tomographic systems, avatars are fitted as digital twins into the surface information of the positioned patients using machine learning.…”

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

“…Pfitzner et al . [20] described the estimation of the body weight of a person who is in front of an RGB‐D camera with three different poses: lying, standing, and walking.…”