Estimating infant upper extremities motion with an RGB-D camera and markerless deep neural network tracking: A validation study

Balta, Diletta; Kuo, HsinHung; Wang, J; Porco, I G; Schladen, Manon Maitland; Cereatti, Andrea; Lum, Peter S.; Croce, Ugo Della

doi:10.1109/embc48229.2022.9871928

Cited by 1 publication

(2 citation statements)

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“…In this work we applied a markerless method to the RGB images recorded from a commercial RGB-D camera and used selected upper body PoIs extracted from the RGB video frames together with the recorded depth information to reconstruct 3D PoI kinematics [ 31 ] from which both some novel and already published metrics were calculated. The metrics used in this study were originally proposed to quantify GM [ 16 , 36 ], given the demonstrated power of GM assessment to predict the development of movement disorders very early in infancy [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

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Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study

Balta

Kuo²,

Wang³

et al. 2022

Sensors

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Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentation in the home may be used to estimate quantitative metrics predictive of movement disorders. This study explored if infants’ GM may be successfully evaluated in a familiar environment by processing the 3D trajectories of points of interest (PoI) obtained from recordings of a single commercial RGB-D sensor. The RGB videos were processed using an open-source markerless motion tracking method which allowed the estimation of the 2D trajectories of the selected PoI and a purposely developed method which allowed the reconstruction of their 3D trajectories making use of the data recorded with the depth sensor. Eight infants’ GM were recorded in the home at 3, 4, and 5 months of age. Eight GM metrics proposed in the literature in addition to a novel metric were estimated from the PoI trajectories at each timepoint. A pediatric neurologist and physiatrist provided an overall clinical evaluation from infants’ video. Subsequently, a comparison between metrics and clinical evaluation was performed. The results demonstrated that GM metrics may be meaningfully estimated and potentially used for early identification of movement disorders.

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Section: Discussionmentioning

confidence: 99%

“…The performance of the markerless method described above was evaluated both on a physical model [ 31 ] as well as on real infants [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study

Balta

Kuo²,

Wang³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Estimating infant upper extremities motion with an RGB-D camera and markerless deep neural network tracking: A validation study

Cited by 1 publication

References 8 publications

Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study

Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study

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