Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Khan, Muhammad Hassan; Schneider, Manuel; Farid, Muhammad Shahid; Grzegorzek, Marcin

doi:10.3390/s18103202

Cited by 24 publications

(26 citation statements)

References 68 publications

(92 reference statements)

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“…Nevertheless, it must be emphasized that the method was evaluated on a very small dataset. Khan et al [ 102 ] proposed a method for monitoring infants at home. They collected RGB data of 10 subjects using an additional RGB camera included in Microsoft Kinect.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

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Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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“…The authors in Reference [13] concluded that the selection of kernel function in SVM along with the optimal values of hyperparameters plays a critical role concerning the data. The authors in Reference [47] have also used SVM as a classification tool in their research. Yazdansepas et al [48] proposed a method to recognize the human activities of 77 subjects.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad

Khan

Nisar

et al. 2021

Sensors

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Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

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“…However, as highlighted in [17], monitoring each limb individually is crucial to assist clinicians in the health-assessment process. With such a view, in [18] RGB images are processed to detect infant's limb skeleton with a learning-based approach. The histogram of oriented gradients is used as feature to train a structured support vector machine aimed at retrieving limb joints, which are then connected knowing the spatial relations between infants' body parts.…”

Section: A Related Workmentioning

confidence: 99%

Preterm Infants’ Pose Estimation With Spatio-Temporal Features

Moccia

Migliorelli

Carnielli

et al. 2020

IEEE Trans. Biomed. Eng.

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Objective: 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 joints from depth videos with high reliability. Methods: Limb-pose estimation is performed using a deeplearning framework consisting of a detection and a regression convolutional neural network (CNN) for rough and precise joint localization, respectively. The CNNs are implemented to encode connectivity in the temporal direction through 3D convolution. Assessment of the proposed framework is performed through a comprehensive study with sixteen depth videos acquired in the actual clinical practice from sixteen preterm infants (the babyPose dataset). Results: When applied to pose estimation, the median root mean square distance, computed among all limbs, between the estimated and the ground-truth pose was 9.06 pixels, overcoming approaches based on spatial features only (11.27 pixels). Conclusion: Results showed that the spatio-temporal features had a significant influence on the pose-estimation performance, especially in challenging cases (e.g., homogeneous image intensity). Significance: This paper significantly enhances the state of art in automatic assessment of preterm infants' health status by introducing the use of spatio-temporal features for limb detection and tracking, and by being the first study to use depth videos acquired in the actual clinical practice for limb-pose estimation. The babyPose dataset has been released as the first annotated dataset for infants' pose estimation.

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Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Cited by 24 publications

References 68 publications

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Preterm Infants’ Pose Estimation With Spatio-Temporal Features

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