A key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to capture human low limbs information, and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.

Predicting human falls from wearable devices is a complex task due to the inherent diversity and causality of multivariate physical changes, where each instance exhibits a unique style of motion events and their spatio-temporal causal dependencies. Consequently, we propose a multichannel causal model that utilizes the Granger causality test to explicitly delineate these internal configurations of motion events and their causal relationships from a spatio-temporal perspective. Particularly, our model incorporates a multi-head attention mechanism with a distillation component to capture the spatio-temporal dependencies among multiple channels of motion sensors in an end-to-end fashion. Empirical evaluations conducted on two benchmark datasets, as well as one in-house dataset collected by ourselves, indicate that our model significantly surpasses state-of-the-art approaches.

Cognitive load assessment task faces a significant challenge regarding the neglect of rich spatio-temporal dependencies and causal dependencies in multi-channel physiological signals. To this end, we present a multi-channel spatio-temporal causal representations model that explicitly characterize the inherent causal structural variability and spatio-temporal dependencies within a single channel and interrelationships among multiple channels. Particularly, a causal structure is constructed by optimizing a score-based causal function under the constraint of causal Markov property. It can effectively disentangle the latent spatio-temporal feature variables into two groups: causal representation and task-irrelevant representation. Empirical evaluations on two public datasets and one in-house dataset suggest our model significantly outperforms the state-of-the-art methods.

The primary challenge in cognitive load recognition is the inherent diversity and causality of multivariate physiological changes, as each instance exhibits a distinctive configuration of physiological events and their spatio-temporal causal dependencies. This leads us to define a causal graph designed by prior knowledge about cognitive load to identify the latent factors hidden in the multi-instance bags constructed by the observed instances of multiple physiological channels. In particular, our model introduces the multi-instance causal representation to explicitly disentangle the unique causal configurations of a particular cognitive load state as a variable number of temporal causal variables and spurious causal variables. In addition, GADF maps are constructed to capture the inherent spatio-temporal dependency among multivariate signals in a 2D structural space. A domain adapter is employed to reduce domain bias by effectively transferring the train domain to the test domain in such continuous latent space. Empirical evaluations on two benchmark datasets and two in-house datasets collected by ourselves suggest our model significantly outperforms the state- of-the-art approaches.

Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as images, thanks to the significant role that AI plays in imitation and production. However, interpretability and controllability remain challenges. Existing AI methods often face challenges in producing images that are both flexible and controllable while considering causal relationships within the images. To address this issue, we have developed a novel method for causal controllable image generation (CCIG) that combines causal representation learning with bi-directional generative adversarial networks (GANs). This approach enables humans to control image attributes while considering the rationality and interpretability of the generated images and also allows for the generation of counterfactual images. The key of our approach, CCIG, lies in the use of a causal structure learning module to learn the causal relationships between image attributes and joint optimization with the encoder, generator, and joint discriminator in the image generation module. By doing so, we can learn causal representations in image’s latent space and use causal intervention operations to control image generation. We conduct extensive experiments on a real-world dataset, CelebA. The experimental results illustrate the effectiveness of CCIG.

Falls are the leading cause of unintentional human injury , having become a public health event of strong social concern. The fall prediction technology based on wearable inertial sensors is a relatively reliable solution in human activity monitoring, a user scenario with mobility and high information privacy sensitivity, and has the advantages of low cost, small size, and high precision. However, a key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to represent human low limbs information and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.