40 research outputs found
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks
Rehabilitation is important to improve quality of life for mobility-impaired
patients. Smart walkers are a commonly used solution that should embed
automatic and objective tools for data-driven human-in-the-loop control and
monitoring. However, present solutions focus on extracting few specific metrics
from dedicated sensors with no unified full-body approach. We investigate a
general, real-time, full-body pose estimation framework based on two RGB+D
camera streams with non-overlapping views mounted on a smart walker equipment
used in rehabilitation. Human keypoint estimation is performed using a
two-stage neural network framework. The 2D-Stage implements a detection module
that locates body keypoints in the 2D image frames. The 3D-Stage implements a
regression module that lifts and relates the detected keypoints in both cameras
to the 3D space relative to the walker. Model predictions are low-pass filtered
to improve temporal consistency. A custom acquisition method was used to obtain
a dataset, with 14 healthy subjects, used for training and evaluating the
proposed framework offline, which was then deployed on the real walker
equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage
and 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms
when deployed on the constrained hardware of the walker. We present a novel
approach to patient monitoring and data-driven human-in-the-loop control in the
context of smart walkers. It is able to extract a complete and compact body
representation in real-time and from inexpensive sensors, serving as a common
base for downstream metrics extraction solutions, and Human-Robot interaction
applications. Despite promising results, more data should be collected on users
with impairments, to assess its performance as a rehabilitation tool in
real-world scenarios.Comment: Accepted for publication in Expert Systems with Application
Heartbeat detection by Laser Doppler Vibrometry and Machine Learning
none6openAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, EmanueleAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, Emanuel
Fall detection for elderly-people monitoring using learned features and recurrent neural networks
AbstractElderly care is becoming a relevant issue with the increase of population ageing. Fall injuries, with their impact on social and healthcare cost, represent one of the biggest concerns over the years. Researchers are focusing their attention on several fall-detection algorithms. In this paper, we present a deep-learning solution for automatic fall detection from RGB videos. The proposed approach achieved a mean recall of 0.916, prompting the possibility of translating this approach in the actual monitoring practice. Moreover to enable the scientific community making research on the topic the dataset used for our experiments will be released. This could enhance elderly people safety and quality of life, attenuating risks during elderly activities of daily living with reduced healthcare costs as a final result
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Abstract
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
Preterm infants’ limb-pose estimation from depth images using convolutional neural networks
Preterm infants' limb-pose estimation is a crucial but challenging task,
which may improve patients' care and facilitate clinicians in infant's
movements monitoring. Work in the literature either provides approaches to
whole-body segmentation and tracking, which, however, has poor clinical value,
or retrieve a posteriori limb pose from limb segmentation, increasing
computational costs and introducing inaccuracy sources. In this paper, we
address the problem of limb-pose estimation under a different point of view. We
proposed a 2D fully-convolutional neural network for roughly detecting limb
joints and joint connections, followed by a regression convolutional neural
network for accurate joint and joint-connection position estimation. Joints
from the same limb are then connected with a maximum bipartite matching
approach. Our analysis does not require any prior modeling of infants' body
structure, neither any manual interventions. For developing and testing the
proposed approach, we built a dataset of four videos (video length = 90 s)
recorded with a depth sensor in a neonatal intensive care unit (NICU) during
the actual clinical practice, achieving median root mean square distance
[pixels] of 10.790 (right arm), 10.542 (left arm), 8.294 (right leg), 11.270
(left leg) with respect to the ground-truth limb pose. The idea of estimating
limb pose directly from depth images may represent a future paradigm for
addressing the problem of preterm-infants' movement monitoring and offer all
possible support to clinicians in NICUs
Preterm infants' pose estimation with spatio-temporal features
Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm-infants' limb pose estimation that features spatio-temporal information to detect and track limb joint position from depth videos with high reliability
Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals
Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening
Beware the Sirens: Prototyping an Emergency Vehicle Detection System for Smart Cars
Vehicle drivers should be able to react coherently in anomalous circumstances, such as the quick arrival of an emergency vehicle with sirens wailing. This situation requires all regular vehicles to give way or slow down, depending on the road and traffic conditions. In this paper, we address an automatic system that assists the driver in reacting to the arrival of an emergency vehicle by employing audio and video algorithms based on Deep Learning. More specifically, by leveraging sound recognition algorithms, the vehicle is able to detect the arrival of the emergency vehicle by its siren sound. In such an event, by making use of computer vision algorithms, the vehicle intelligence can monitor the driver’s gaze and awareness towards the emergency vehicle and assess his/her awareness. The paper describes the process of integrating these technologies into a commercial car, the creation of new datasets and the challenges encountered