Bodypart localization for pressure ulcer prevention

Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fibers Embedded in Stretchable Polydimethylsiloxane

We report the design and testing of a sensor pad based on optical and flexible materials for the development of pressure monitoring devices. This project aims to create a flexible and low-cost pressure sensor based on a two-dimensional grid of plastic optical fibers embedded in a pad of flexible and stretchable polydimethylsiloxane (PDMS). The opposite ends of each fiber are connected to an LED and a photodiode, respectively, to excite and measure light intensity changes due to the local bending of the pressure points on the PDMS pad. Tests were performed in order to study the sensitivity and repeatability of the designed flexible pressure sensor.

Simultaneously-Collected Multimodal Lying Pose Dataset: Enabling In-Bed Human Pose Monitoring

Computer vision field has achieved great success in interpreting semantic meanings from images, yet its algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. Among these tasks is in-bed human pose monitoring with significant value in many healthcare applications. In-bed pose monitoring in natural settings involves pose estimation in complete darkness or full occlusion. The lack of publicly available in-bed pose datasets hinders the applicability of many successful human 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 (LWIR), depth, and pressure map. We also present a physical hyper parameter tuning strategy for ground truth pose label generation under adverse vision conditions. The 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 the SLP data with promising performance as high as 95% at PCKh@0.5 on a single modality. The pose estimation performance of these models can be further improved by including additional modalities through the proposed collaborative scheme.

BodyPressure - Inferring Body Pose and Contact Pressure From a Depth Image

Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method that infers contact pressure between a human body and a mattress from a depth image. Specifically, we focus on using a depth image from a downward facing camera to infer pressure on a body at rest in bed occluded by bedding, which is directly applicable to the prevention of pressure injuries in healthcare. Our approach involves augmenting a real dataset with synthetic data generated via a soft-body physics simulation of a human body, a mattress, a pressure sensing mat, and a blanket. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data. The network contains an embedded human body mesh model and uses a white-box model of depth and pressure image generation. Our network successfully infers body pose, outperforming prior work. It also infers contact pressure across a 3D mesh model of the human body, which is a novel capability, and does so in the presence of occlusion from blankets.

Pressure Ulcers from the Medico-Legal Perspective: A Case Report and Literature Review

INTRODUCTION

The identification of professional liability profiles related to the development of pressure injuries is a very thorny issue from a medico-legal perspective. This is because no matter how strict the applied prevention protocols applied may be, the development of such injuries is largely dependent on endogenous factors. This paper aims to investigate the medico-legal issues related to this topic through the exposition of one case of medico-legal litigation and a traditional review of the literature.

METHODS

We performed a literature search using three databases (Pubmed, Scopus, and Web Of Science), restricting the search to the period between 2001 and 2021. We used "pressure ulcers" and "jurisprudence" as the main keywords. From an initial library of 236 articles, our selection resulted in 12 articles, which were included in the review.

RESULTS

We identified the ever-increasing expectations of patients and the concept of automatic attribution of responsibility when a pressure ulcer develops as the primary reasons for the increase in litigation over the past 20 years. The related corrective measures are numerous: a strict adherence to guidelines, an adequate documentation of preventive measures, a risk assessment, family involvement, and a successful collaboration between physicians and government institutions.

CONCLUSIONS

The biological complexity of the pathogenetic development of pressure ulcers makes the subject very delicate from the medico-legal point of view. In principle, it is possible to state that a very large proportion of such injuries are preventable, but that there remains a percentage of them that cannot be prevented. In such cases, only a proper documentary demonstration of the adequacy of preventive measures can exclude liability profiles.

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring

Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient's in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person's pose, and locate and track high-risk regions over time so that necessary attention can be provided.

Pressure Injury Prevention: A Survey

Pressure injuries are caused by prolonged pressure to an area of the body, which can result in open wounds that descend to the bone. Pressure injuries should not occur in healthcare settings, and yet, they still affect 2.5 million patients in the United States and have an impact on quality of life. Pressure injuries come at a cost of $11 billion in the United States, and 90% of pressure injuries are a secondary condition. In this paper, we survey the literature on preventative techniques to address pressure injures, which we classify into two categories: active prevention strategies and sensor-based risk-factor monitoring. Within each category of techniques, we discuss the literature and assess each class of strategies based on its commercial availability, results of clinical trials when available, the ability for the strategy to save time for healthcare staff, and whether the technique can be tuned to an individual. Based on our findings, the most promising current solutions, supplementary to nursing guidelines, are electrical stimulation, pressure monitoring, and inertial measurement unit monitoring. We also find a need for a clinical software system that can easily integrate with custom sensors, use custom analysis algorithms, and provide visual feedback to the healthcare staff.

In-Bed Pose Estimation: Deep Learning With Shallow Dataset

This paper presents a robust human posture and body parts detection method under a specific application scenario known as in-bed pose estimation. Although the human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, the in-bed pose estimation using camera-based vision methods has been ignored by the CV community because it is assumed to be identical to the general purpose pose estimation problems. However, the in-bed pose estimation has its own specialized aspects and comes with specific challenges, including the notable differences in lighting conditions throughout the day and having pose distribution different from the common human surveillance viewpoint. In this paper, we demonstrate that these challenges significantly reduce the effectiveness of the existing general purpose pose estimation models. In order to address the lighting variation challenge, the infrared selective (IRS) image acquisition technique is proposed to provide uniform quality data under various lighting conditions. In addition, to deal with the unconventional pose perspective, a 2- end histogram of oriented gradient (HOG) rectification method is presented. The deep learning framework proves to be the most effective model in human pose estimation; however, the lack of large public dataset for in-bed poses prevents us from using a large network from scratch. In this paper, we explored the idea of employing a pre-trained convolutional neural network (CNN) model trained on large public datasets of general human poses and fine-tuning the model using our own shallow (limited in size and different in perspective and color) in-bed IRS dataset. We developed an IRS imaging system and collected IRS image data from several realistic life-size mannequins in a simulated hospital room environment. A pre-trained CNN called convolutional pose machine (CPM) was fine-tuned for in-bed pose estimation by re-training its specific intermediate layers. Using the HOG rectification method, the pose estimation performance of CPM improved significantly by 26.4% in the probability of correct key-point (PCK) criteria at PCK0.1 compared to the model without such rectification. Even testing with only well aligned in-bed pose images, our fine-tuned model still surpassed the traditionally tuned CNN by another 16.6% increase in pose estimation accuracy.

In-bed posture classification using deep autoencoders

Pressure ulcers are high prevalence complications among bed-bound patients which are not only extremely painful and difficult to treat, but also impose a great burden in our health-care system. We target automatic posture detection which is a key module in all pressure ulcer monitoring platforms. Using data collected from a commercially-available pressure mapping system, we applied deep neural networks to automatically classify in-bed posture using features extracted from the histogram of gradient technique. High accuracy of up to 98% was achieved in classifying five different in-bed postures for more than 60,000 pressure images.

Body-Earth Mover's Distance: A Matching-Based Approach for Sleep Posture Recognition

Sleep posture is a key component in sleep quality assessment and pressure ulcer prevention. Currently, body pressure analysis has been a popular method for sleep posture recognition. In this paper, a matching-based approach, Body-Earth Mover's Distance (BEMD), for sleep posture recognition is proposed. BEMD treats pressure images as weighted 2D shapes, and combines EMD and Euclidean distance for similarity measure. Compared with existing work, sleep posture recognition is achieved with posture similarity rather than multiple features for specific postures. A pilot study is performed with 14 persons for six different postures. The experimental results show that the proposed BEMD can achieve 91.21% accuracy, which outperforms the previous method with an improvement of 8.01%.

Clustering-based limb identification for pressure ulcer risk assessment

Bedridden patients have a high risk of developing pressure ulcers. Risk assessment for pressure ulceration is critical for preventive care. For a reliable assessment, we need to identify and track the limbs continuously and accurately. In this paper, we propose a method to identify body limbs using a pressure mat. Three prevalent sleep postures (supine, left and right postures) are considered. Then, predefined number of limbs (body parts) are identified by applying Fuzzy C-Means (FCM) clustering on key attributes. We collected data from 10 adult subjects and achieved average accuracy of 93.2% for 10 limbs in supine and 7 limbs in left/right postures.

Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach.