Computer Vision to Automatically Assess Infant Neuromotor Risk

Chambers, Claire; Seethapathi, Nidhi; Saluja, Rachit; Loeb, Helen; Pierce, Samuel R.; Bogen, Daniel K.; Prosser, Laura A.; Johnson, Michelle J.; Kording, Konrad

doi:10.1109/tnsre.2020.3029121

Cited by 65 publications

(46 citation statements)

References 41 publications

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“…In this way, markerless motion tracking might open possibilities towards development of conventional machine learning or deep learning approaches for the assessment of complex movement disorders such as dyskinetic CP. Similar methodologies are currently developed for generalized movement analysis 14 and automatic gait assessment in CP 15 .…”

Section: Resultsmentioning

confidence: 99%

Using DeepLabCut for tracking body landmarks in videos of children with dyskinetic cerebral palsy: a working methodology

Haberfehlner

Ven

Burg

et al. 2022

Preprint

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Background: Markerless motion tracking is a promising technique to capture human movements and postures. It could be a clinically feasible tool to objectively assess movement disorders within dyskinetic cerebral palsy (CP). Here, we aim to evaluate markerless motion tracking on clinically recorded video data , i.e. to assess its accuracy against manual labeling. Method: 94 video recordings of 33 participants (dyskinetic CP, 8-23 years; GMFCS IV-V) from a previous clinical trial were used. Twenty-second clips were cut during lying down. Video image resolution was 0.4 cm per pixel. Automatic tracking was performed in DeepLabCut. We evaluated a model that was pretrained on a human healthy adult data set with an increasing number of manually labeled frames (0, 1, 2, 6, 10, 15 and 20 frames per video). To assess generalizability, we used 80% of videos for the model development and evaluated the generalizability of the model using the remaining 20%. For evaluation the mean absolute error (MAE) between DeepLabCuts prediction of the position of body points and manual labels was calculated. Results: Using just the pre-trained adult human model yielded a MAE of 121 pixels. An MAE of 4.5 pixels (about 1.5 cm) could be achieved by adding 15-20 manual labels. When applied to unseen video clips (i.e. generalization set), the MAE was 33 pixels with a dedicated model trained on 20 frames per videos. Conclusion: Markerless motion tracking with a standard pre-trained model is insufficiently accurate to automatically assess movement disorders in dyskinetic CP. However, manually adding labels improves the model performance substantially. In addition, the methodology proposed within our study is applicable to check the accuracy of DeepLabCut application within other clinical data sets.

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Section: Resultsmentioning

confidence: 99%

Using DeepLabCut for tracking body landmarks in videos of children with dyskinetic cerebral palsy: a working methodology

Haberfehlner

Ven

Burg

et al. 2022

Preprint

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“…We therefore chose for the current study also OpenPose which has been successfully used in recent studies to classify infant movements. 27 , 28 , 57 …”

Section: Methodsmentioning

confidence: 99%

Open video data sharing in developmental science and clinical practice

Marschik¹,

Kulvičius²,

Flügge³

et al. 2023

iScience

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“…Recently, a ‘normative’ reference database of infant movements has been created using 85 videos found online [ 42 ]. Two physical therapists estimated the age of the infants.…”

Section: Data Acquisition Collection and Labellingmentioning

confidence: 99%

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

Leo

Bernava

Carcagnì

et al. 2022

Sensors

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Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.

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Computer Vision to Automatically Assess Infant Neuromotor Risk

Cited by 65 publications

References 41 publications

Using DeepLabCut for tracking body landmarks in videos of children with dyskinetic cerebral palsy: a working methodology

Using DeepLabCut for tracking body landmarks in videos of children with dyskinetic cerebral palsy: a working methodology

Open video data sharing in developmental science and clinical practice

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

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