“BAM!” Depth-Based Body Analysis in Critical Care

Martínez, Manuel Martínez; Schauerte, Boris; Stiefelhagen, Rainer

doi:10.1007/978-3-642-40261-6_56

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…Depth data has been extensively employed for estimating human poses during rest or sleep due to their invul-nerability to the darkness during night time. Martinez et al proposed a bed aligned map (BAM) descriptor based on depth information collected from a Microsoft Kinect camera to monitor the patient's sleeping position (not the full pose) and body movements while in bed [17]. They also reported the estimation results for simulated covered cases, yet no real human data validation was given.…”

Section: General Human Pose Estimationmentioning

confidence: 99%

“…Nonetheless, given the importance of this topic in healthcare applications, in the last decade, consistent effort has been made in order to address the in-bed pose estimation problem by employing other sensing modalities including pressure mapping systems [15], [16], depth sensing [17], as well as infrared imaging [18]. Yet, the scale of data in these work are limited by having only a few participants and none of the work has publicly released their datasets to the machine learning/computer vision community.…”

Section: Introductionmentioning

confidence: 99%

“…(i) Modality coverage: Mainstream imaging modalities for in-bed human pose are covered in the SLP including: RGB [29], [30], LWIR [18], Depth [17] and PM (pressure map) [32].…”

Section: Introducing Slp Datasetmentioning

confidence: 99%

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Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

Liu¹,

Huang²,

Fu³

et al. 2020

Preprint

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Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. One of this tasks is in-bed human pose estimation, which has significant values in many healthcare applications. In-bed pose monitoring in natural settings could involve complete darkness or full occlusion. Furthermore, the lack of publicly available in-bed pose datasets hinders the use of many successful pose estimation algorithms for this task. In this paper, we introduce our Simultaneously-collected multimodal Lying Pose (SLP) dataset, which includes in-bed pose images from 109 participants captured using multiple imaging modalities including RGB, long wave infrared, depth, and pressure map. We also present a physical hyper parameter tuning strategy for ground truth pose label generation under extreme conditions such as lights off and being fully covered by a sheet/blanket. SLP design is compatible with the mainstream human pose datasets, therefore, the state-of-the-art 2D pose estimation models can be trained effectively with SLP data with promising performance as high as 95% at PCKh@0.5 on a single modality. The pose estimation performance can be further improved by including additional modalities through collaboration.

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Section: General Human Pose Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

Liu¹,

Huang²,

Fu³

et al. 2020

Preprint

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show abstract

“…CNN is famous in many different domains, and it is also currently gaining popularity for classifying physiological input signals (such as ECG, and EEG) of ERS. Martinez et al (2013) [94] used CNN for classifying mental states (i.e., excitement, relaxation, fun, and anxiety) using SC and BV pulse signals. In another research, different statistical features were obtained from the benchmark dataset DEAP and passed to the CNN model for emotional state classification [95].…”

Section: -3-1-convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

et al. 2022

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Emotion recognition systems (ERS) have become a popular research field to contribute to human-machine interaction in different areas. Different kinds of applications on ERS can serve different purposes. Artificial intelligence (AI) and the internet of things (IoT) are the technologies behind such applications. The main objective of this study is to enable researchers and developers to search for the most suitable options to develop an emotional state recognition system. More specifically, this paper presents work on ERS, which is built using physiological signals extracted from biosensors. It also presents details of how the extracted physiological signals are used to identify the user's emotional state. In this review, the sensors are categorized based on their modality: contact-based sensors and contactless sensors. Next, the ERS process is presented together with the reported results for each described technique. Articles from four different research databases were reviewed, of which 147 articles from 2009 to 2021 were referred to that are related to ERS using physiological signals. This paper should be significant for researchers developing systems that integrate human emotion recognition capability. The findings reported here can guide them in choosing suitable methods for their systems. Doi: 10.28991/ESJ-2022-06-05-017 Full Text: PDF

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“…Grimm et al [16] used a single depth camera to recognize sleeping posture. In doing so, they used Bed aligned maps (BAMs) [17] using Convolutional Neural Networks (CNN) model for classification and achieved 94.0% accuracy. Many studies on deep-learning-based posture estimates have been published [18][19][20][21][22].…”

Section: -Literature Reviewmentioning

confidence: 99%

SleepCon: Sleeping Posture Recognition Model using Convolutional Neural Network

Jesmeen

Bhuvaneswari²,

Mazbah³

et al. 2022

Emerg Sci J

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Recognition of sleep patterns and posture has sparked interest in various clinical applications. Sleep postures can be monitored autonomously and constantly to provide useful information for decreasing health risks. Existing systems mostly use images to train the model to learn based on many sensors. For example, a camera, pressure sensor, and electrocardiogram. In this study, a model (named as SleepCon) was designed using deep learning, which will have the capability to train with any threshold image obtained from any sensor. This paper presented a system where data was obtained from a camera installed on the top of a mattress. The camera located the movement of the body posture on the mattress while the subject was lying down on the mattress. In doing so, CNN and other pre-processed steps took place to collect data and then analyze the data to recognize different sleep postures. This model was stored for use in real-time applications. The system can recognize the three major postures, i.e., left, right, and supine. A real-time application is also developed and operates the stored SleepCon model through an accompanying desktop application for detecting the posture live. The accuracy of classification was greater than 90%, while the actual application accuracy was 100% after carrying out the experiment on the SleepCon model. Doi: 10.28991/ESJ-2023-07-01-04 Full Text: PDF

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“BAM!” Depth-Based Body Analysis in Critical Care

Cited by 21 publications

References 11 publications

Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

SleepCon: Sleeping Posture Recognition Model using Convolutional Neural Network

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