A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants

• URL: http://arxiv.org/abs/2303.16867v1
• Date: Wed, 29 Mar 2023 17:24:21 GMT
• Title: A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants
• Authors: Shaotong Zhu, Michael Wan, Elaheh Hatamimajoumerd, Kashish Jain, Samuel Zlota, Cholpady Vikram Kamath, Cassandra B. Rowan, Emma C. Grace, Matthew S. Goodwin, Marie J. Hayes, Rebecca A. Schwartz-Mette, Emily Zimmerman, Sarah Ostadabbas
• Abstract summary: Non-nutritive sucking is a potential biomarker for developmental delays. One barrier to clinical assessment of NNS stems from its sparsity. Our method is based on an underlyingNS action recognition algorithm.
• Score: 15.049449914396462
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present an end-to-end computer vision pipeline to detect non-nutritive sucking (NNS) -- an infant sucking pattern with no nutrition delivered -- as a potential biomarker for developmental delays, using off-the-shelf baby monitor video footage. One barrier to clinical (or algorithmic) assessment of NNS stems from its sparsity, requiring experts to wade through hours of footage to find minutes of relevant activity. Our NNS activity segmentation algorithm solves this problem by identifying periods of NNS with high certainty -- up to 94.0\% average precision and 84.9\% average recall across 30 heterogeneous 60 s clips, drawn from our manually annotated NNS clinical in-crib dataset of 183 hours of overnight baby monitor footage from 19 infants. Our method is based on an underlying NNS action recognition algorithm, which uses spatiotemporal deep learning networks and infant-specific pose estimation, achieving 94.9\% accuracy in binary classification of 960 2.5 s balanced NNS vs. non-NNS clips. Tested on our second, independent, and public NNS in-the-wild dataset, NNS recognition classification reaches 92.3\% accuracy, and NNS segmentation achieves 90.8\% precision and 84.2\% recall.

Related papers

• Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks [50.32980443749865]
  Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biologicalability. Current SNNs struggle to balance accuracy and latency in neuromorphic datasets. We propose Step-wise Distillation (HSD) method, tailored for neuromorphic datasets.
  arXiv  Detail & Related papers  (2024-09-19T06:52:34Z)
• When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron [7.478056407323783]
  Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. We propose a novel method for image decoding based on temporal attention (TAID) and an adaptive Leaky-Integrate-and-Fire neuron model.
  arXiv  Detail & Related papers  (2024-06-05T08:21:55Z)
• Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as a Neurodevelopmental Cue [11.1943906461896]
  Non-nutritive sucking (NNS) plays a crucial role in assessing healthy early development. NNS activity has been proposed as a potential safeguard against sudden infant death syndrome (SIDS) We introduce a vision-based algorithm designed for non-contact detection of NNS activity using baby monitor footage in natural settings.
  arXiv  Detail & Related papers  (2023-10-24T19:26:07Z)
• Activity Recognition From Newborn Resuscitation Videos [3.54339629317433]
  A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania. We propose a two-step deep neural network system, ORAA-net, to do activity recognition during newborn resuscitation. The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %.
  arXiv  Detail & Related papers  (2023-03-14T11:04:32Z)
• Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism [65.70943212672023]
  Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
  arXiv  Detail & Related papers  (2021-10-19T00:03:57Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures [58.720142291102135]
  Detailed analysis of seizure semiology is critical for management of epilepsy patients. We present GESTURES, a novel architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We show that an STCNN trained on a HAR dataset can be used in combination with an RNN to accurately represent arbitrarily long videos of seizures.
  arXiv  Detail & Related papers  (2021-06-22T18:40:31Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Automatic classification of multiple catheters in neonatal radiographs with deep learning [2.256008196530956]
  We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs.
  arXiv  Detail & Related papers  (2020-11-14T21:27:21Z)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)
• Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks [36.16846259899793]
  Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs.
  arXiv  Detail & Related papers  (2020-07-11T14:35:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.