Perception and cognition are core processes that transform external sensory signals into internal representations for knowledge construction and understanding, and in visually impaired individuals, this transformation is reorganized through auditory and tactile feedback. To explain how perceptual information evolves into stable cognitive representations under limited sensory bandwidth, this study proposes Phase Transition Hypothesis of Perception and Cognition. The proposed hypothesis models the perceptual–cognitive process as a dynamic phase transition, in which sensory information evolves from fragmented perception into organized cognition. To counteract perceptual bias induced by information collapse, the Perceptual Dynamic Optimization Mechanism adaptively regulates sensory deviations to stabilize the perceptual–cognitive transition, whereas the Cognitive Potential Model, derived from the Free-Energy Principle, elucidates how stable and self-organizing cognition emerges from this dynamic process. A cognitive simulation system and a blind writing navigation experiment are conducted to validate the hypothesis. Experiments demonstrate the proposed adaptive correction of perceptual bias and the phase transition mechanism from perception to cognition.

Eye movement data has become an important char-acterization in the analysis of children with autism spectrum disorder (ASD). Current eye movement measurement meth-ods require specialized expensive equipment, calibration, and trained personnel, limiting their use in general ASD screening, especially in resource-scarce environments. Therefore, collecting eye movement features based on the standard RGB camera of a mobile phone or tablet has many advantages over professional equipment. The system design is based on the Android tablet design, and the screen is divided into two parts to display the normal children and the ASD children paintings. The eye movement data of children is obtained through the front camera, so as to provide data support for future data analysis. Taking the different cooperation degrees of children into account, two collection modes are designed: 1) directly displaying the stimuli in a loop (image mode); and 2) providing the background video interspersed with the stimulus display (video mode). The demo video of the proposed system is available at: https://doi.org/10.6084/m9.figshare.21346806.v1.

This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. Existing non-contact breath assessments achieve satisfactory results under restricted conditions when human body stays relatively still. When someone moves forward, the breath signals detected by depth camera are hidden within signals of trunk displacement and deformation, and the signal length is short due to the short stay time, posing great challenges for us to establish models. To over-come these challenges, multiple region of interests (ROIs) based signal extraction and selection method is proposed to automatically obtain the signal informative to breath from depth video. Subsequently, graph signal analysis (GSA) is adopted as a spatial-temporal filter to wipe the components unrelated to breath. Finally, a classifier for identifying deep breath is established based on the selected breath-informative signal. In validation experiments, the proposed approach outperforms the comparative methods with the accuracy, precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2%, respectively. This system can be extended to public places to provide timely and ubiquitous help for those who may have or are going through physical or mental trouble.

This paper presents a low-cost and unobtrusive intelligent respiratory monitoring system. To achieve low-cost and remote measurement of respiratory signal, an RGB camera collaborated with marker tracking is used as data acquisition sensor, and a Raspberry Pi is used as data processing platform. To overcome challenges in actual applications, the signal processing algorithms are designed for removing sudden body movements and smoothing the raw signal. To discover more specific information in the respiratory signal, respiratory rate is estimated by a translational cross point algorithm, and respiratory pattern is identified by recurrent neural network. Finally, the obtained decision-making information and some original information are sent to user's smartphone via a cloud service platform. For estimating respiratory rate, the Bland-Altman plot demonstrates the satisfactory results with agreement ranges of -0.13 ± 5.85 bpm. With respect to the classification of breathing patterns, the results validate that the system has the good performance with the accuracy, precision, recall, and F1 of 92.5%, 92.5%, 93.3%, and 92.9%, respectively. This work may contribute to the development of low-cost and non-contact respiratory monitoring products specific to home or work health care.

The aim of this study is to develop an affordable and remote intelligent respiratory monitoring system. To achieve low-cost and remote measurement of respiratory signal, an RGB camera collaborated with marker tracking is used as a data acquisition sensor, and a Raspberry Pi is used as a data processing platform. To overcome challenges in actual applications, the signal processing algorithms are designed for removing sudden body movements and smoothing the raw signal. Subsequently, respiratory rate (RR) is estimated by a translational cross-point algorithm, and the respiratory pattern is identified by the recurrent neural network. For estimating RR, the translational cross-point algorithm performs better than other methods with root-mean-square error (RMSE) of 3.29 bpm. With respect to the classification of breathing patterns, the established neural network performs better than support vector machine-based classifiers with the accuracy, precision, recall, and F1 of 89.0%, 89.0%, 90.5%, and 89.0%, respectively. The obtained decision-making information and some original information are sent to the user’s smartphone via a cloud service platform. In a way, due to its low-price, noncontact, and portable merits, the established system can be seen as a “respiratory consultant” by your side.

Due to the loss of vision, the appreciation of the realworld scene and the images displayed on the screen becomes almost impossible for blind people. In an effort to meet the needs of the blind community, we develop a wearable visually assistive device to help them perceive images. With the help of various multimedia information processing technologies, the proposed device can first acquire image information through a depth camera, then implement an image-to-text transformation using image caption technology, and finally the obtained text sequence is fed back to the user via voice. In this way, blind people are able to perceive the outside world, thus creating an unprecedented experience for them. The main technical specifications of the system are: distance perception range is 0.1m to 10m; RGB field of view is 69.4°×42.5°×77°; depth field of view is 91.2°×65.5°×100.6°; maximum weight is 3.05kg. Two demo videos of the proposed navigation system which are respectively recorded for real-world scene and screen image are available at: https://doi.org/10.6084/m9.figshare.12520499.v1.

Indoor navigation is urgently needed by blind people in their everyday lives. In this paper, we design an assistive cane with visual odometry based on actual requirements of the blind to aid them in attaining safe indoor navigation. Compared to the state-of-the-art indoor navigation systems, the proposed device is portable, compact, and adaptable. The main specifications of the system are: the perception range is respectively from 0.10m to 2.10m, and 0.08m to 1.60m for width and length dimensions; the maximum weight is 2.1kg; the detection range is from 0.15m and 3.00m; the cruising ability is about 8h; and the objects whose heights are below 80cm can be detected. The demo video of the proposed navigation system is available at: https://doi.org/10.6084/m9.figshare.12399572.v1.