Infant upper body 3D kinematics estimated using a commercial RGB-D sensor and a deep neural network tracking processing tool

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

doi:10.1109/memea54994.2022.9856585

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…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

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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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“…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

Self Cite

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show abstract

“…Target tracking is one of the current hot spots in computer vision, and has both a wide research range and practical significance. Its applications range from civilian medical imaging [1] and industrial detection to military missile guidance and unmanned aerial vehicle operation [2]. For target tracking in practical applications, visual tracking is particularly challenging because of the limited target information, as only objects in one frame and instantaneous changes in the appearance of objects can be employed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Target Tracking Scheme Based on Attention Mechanism in Complex Scenes

Wang

Yang

et al. 2022

Electronics

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In recent years, target tracking algorithms based on deep learning have realized significant progress, especially the Siamese neural network structure, which has a simple structure and excellent scalability. Although these methods provide excellent generalization capabilities, they fail to perform the task of learning target information discrimination smoothly due to being affected by distractors such as background clutter, occlusion, and target size. To solve this problem, in this paper we propose a newly improved Siamese network target tracking algorithm based on an attention mechanism. We introduce a channel attention module and a spatial attention module into the original network to improve the problem of insufficient semantic extraction ability of the convolutional layer of the tracking algorithm in complex environments. A channel attention mechanism enhances the feature extraction ability by using the network to learn the importance of each channel and establish the relationship between channels, while a spatial attention mechanism strengthens the feature extraction ability by establishing the importance of spatial position in locating the target or carrying out a certain degree of deformation. In this paper, the above two models are combined to improve the robustness of trackers without sacrificing tracking speed. We conduct a comprehensive experiment on the Object Tracking Benchmark dataset. The experimental results show that our algorithm outperforms other real-time trackers in both accuracy and robustness in most complex environments.

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Infant upper body 3D kinematics estimated using a commercial RGB-D sensor and a deep neural network tracking processing tool

Cited by 2 publications

References 23 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

A Novel Target Tracking Scheme Based on Attention Mechanism in Complex Scenes

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