Things to Do in L.A. Tap to enable a layout that focuses on the article. Mark Chaney filmed the Serve Robotics device repeatedly swerving into his path. This is read by an automated voice. Please report any issues or inconsistencies here . A collision between a delivery robot and a man using a mobility scooter in West Hollywood received more than 26 million views.

ABSTRACT Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convo-lutional Network (GCN) models have shown promise in automatically predicting CP risk from infant videos, their "black-box" nature raises concerns about clinical explainabil-ity. To address this, we introduce a perturbation framework tailored for infant movement features and use it to compare two explainable AI (XAI) methods: Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM). First, we identify significant and non-significant body keypoints in very low and very high risk infant video snippets based on the XAI attribution scores. We then conduct targeted velocity and angular perturbations, both individually and in combination, on these keypoints to assess how the GCN model's risk predictions change. Our results indicate that velocity-driven features of the arms, hips, and legs have a dominant influence on CP risk predictions, while angular perturbations have a more modest impact. Our findings demonstrate the use of XAI-driven movement analysis for early CP prediction, and offer insights into potential movement-based biomarker discovery that warrant further clinical validation. Index T erms-- explainable AI, CAM, Grad-CAM, skeleton data, Cerebral Palsy 1. INTRODUCTION Cerebral Palsy (CP) is the most common motor disability in childhood, affecting approximately 2.11 per 1,000 live births worldwide [1]. Early detection of CP is crucial for initiating timely interventions that can significantly improve outcomes and quality of life for affected individuals [2].

Early detection of Cerebral Palsy (CP) is crucial for effective intervention and monitoring. This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements. Specifically, we use XAI evaluation metrics -- namely faithfulness and stability -- to quantitatively assess the reliability of Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) in this specific medical application. We utilize a unique dataset of infant movements and apply skeleton data perturbations without distorting the original dynamics of the infant movements. Our CP prediction model utilizes an ensemble approach, so we evaluate the XAI metrics performances for both the overall ensemble and the individual models. Our findings indicate that both XAI methods effectively identify key body points influencing CP predictions and that the explanations are robust against minor data perturbations. Grad-CAM significantly outperforms CAM in the RISv metric, which measures stability in terms of velocity. In contrast, CAM performs better in the RISb metric, which relates to bone stability, and the RRS metric, which assesses internal representation robustness. Individual models within the ensemble show varied results, and neither CAM nor Grad-CAM consistently outperform the other, with the ensemble approach providing a representation of outcomes from its constituent models.

Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions. Spontaneous motor activity, or 'kinetics', is shown to provide a powerful surrogate measure of upcoming neurodevelopment. However, its assessment is by and large qualitative and subjective, focusing on visually identified, age-specific gestures. Here, we follow an alternative approach, predicting infants' neurodevelopmental maturation based on data-driven evaluation of individual motor patterns. We utilize 3D video recordings of infants processed with pose-estimation to extract spatio-temporal series of anatomical landmarks, and apply adaptive graph convolutional networks to predict the actual age. We show that our data-driven approach achieves improvement over traditional machine learning baselines based on manually engineered features.

Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We highlight a significant difference between CP infants' frequency of human movement and that of the healthy group, which improves prediction performance. However, the existing deep learning-based methods did not use the frequency information of infants' movement for CP prediction. This paper proposes a frequency attention informed graph convolutional network and validates it on two consumer-grade RGB video datasets, namely MINI-RGBD and RVI-38 datasets. Our proposed frequency attention module aids in improving both classification performance and system interpretability. In addition, we design a frequency-binning method that retains the critical frequency of the human joint position data while filtering the noise. Our prediction performance achieves state-of-the-art research on both datasets. Our work demonstrates the effectiveness of frequency information in supporting the prediction of CP non-intrusively and provides a way for supporting the early diagnosis of CP in the resource-limited regions where the clinical resources are not abundant.

Children with cerebral palsy might soon use technology to gain some independence. UC Riverside researchers are developing robotic sleeves that provide arm control to kids with cerebral palsy-related mobility issues. Rather than augment the arm like an exoskeleton, the technology will use voltage sensors to detect muscle contractions and predict what the wearer wants to do, like bend the elbow. Inflatable bladders will then push the arm toward the intended destination. Soft robotics will play an important role.

There are about 2 3 CP patients in 1000 children in the UK [1], which is similar to other developed countries. Although CP cannot be completely cured at present, early prediction of CP and intervention are considered as a paramount part of the treatment. Current clinical early prediction of CP is investigated by General Movement Assessment (GMA) [2]. GMA can be done in person by GM assessors to assess an infant, or it can be done via watching an RGB video that has recorded the general movements of the infant. However, the GMA training is time-and resourceconsuming, making it challenging to cope with the high demand for CP prediction. To tackle this problem, we propose automating this process by analyzing the general movements of infants from RGB videos. This allows the early prediction to cover even the lower-risk population. Motivated by the encouraging results reported in recent research based on skeletal data [3, 4, 5, 6, 7, 8, 9, 10], the 2D joint locations of the infant are extracted from RGB videos as the input of the system for CP prediction. The computational intelligence of our system is implemented with a graph convolution network, a kind of deep artificial neural network that models relational data very well, making it suitable for skeleton data.

Promoting exploratory movements through contingent feedback can positively influence motor development in infancy. Our ongoing work gears toward the development of a robot-assisted contingency learning environment through the use of small aerial robots. This paper examines whether aerial robots and their associated motion controllers can be used to achieve efficient and highly-responsive robot flight for our purpose. Infant kicking kinematic data were extracted from videos and used in simulation and physical experiments with an aerial robot. The efficacy of two standard of practice controllers was assessed: a linear PID and a nonlinear geometric controller. The ability of the robot to match infant kicking trajectories was evaluated qualitatively and quantitatively via the mean squared error (to assess overall deviation from the input infant leg trajectory signals), and dynamic time warping algorithm (to quantify the signal synchrony). Results demonstrate that it is in principle possible to track infant kicking trajectories with small aerials robots, and identify areas of further development required to improve the tracking quality.

California startup Cionic and Yves Béhar's design studio Fuseproject have developed a bionic wearable that uses electric pulses and artificial intelligence to correct muscle movements in people with limited mobility. The Neural Sleeve is designed to be wrapped around the leg and uses functional electrical stimulation (FES) to help with the walking difficulties that can be caused by multiple sclerosis, cerebral palsy, spinal cord injuries and strokes. "Think of it as a way to sort of remote control your own leg," said Béhar, who worked with Cionic to make the FES technology usable and scalable. "What the algorithms do and what the electrodes do is they deliver that right sequence. And when the brain has relearned and re-acquired the knowledge of how to fire those muscles, the sleeve is not needed anymore."

ATHENS, June 17, (Reuters) - Alexandra Kerlidou sits in her wheelchair on stage in Athens. With only the shift of her eyes across a computer screen, the 21-year-old fills the air with harp music. The student with cerebral palsy, who cannot use her hands or speak, is playing the "Eyeharp", gaze-controlled digital software which allows people with disabilities to play music, something she had never thought possible. "I felt strange, I had never imagined such a thing," said Alexandra, using a speech-generating computer program as she described trying the "Eyeharp" for the first time in her home on Lesbos with creator Zacharias Vamvakousis. A computer scientist and musician, Vamvakousis was inspired to create the program after a musician friend was hurt in a motorcycle accident shortly before they were to play a concert together.