Abstract

Abstract

Abstract

Abstract
Biofeedback is a promising tool to complement conventional physical therapy by fostering active participation of neurologically impaired patients during treatment. This work aims at a user-centered design and usability assessment for different age groups of a novel wearable augmented reality application composed of a multimodal sensor network and corresponding control strategies for personalized biofeedback during gait training. The proposed solution includes wearable AR glasses that deliver visual cues controlled in real-time according to mediolateral center of mass position, sagittal ankle angle, or tibialis anterior muscle activity from inertial and EMG sensors. Control strategies include positive and negative reinforcement conditions and are based on the user's performance by comparing real-time sensor data with an automatically user-personalized threshold. The proposed solution allows ambulatory practice on daily scenarios, physiotherapists' involvement through a laptop screen, and contributes to further benchmark biofeedback regarding the type of sensor. Although old healthy adults with low academic degrees have a preference for guidance from an expert person, excellent usability scores (SUS scores: 81.25-96.87) were achieved with young and middle-aged healthy adults and one neurologically impaired patient.

Abstract
Underwater wireless communications are crucial for supporting multiple maritime activities, such as environmental monitoring and offshore wind farms. However, the challenging underwater environment continues to pose obstacles to the development of long-range, broadband underwater wireless communication systems. State of the art solutions are limited to long range, narrowband acoustics and short range, broadband radio or optical communications. This precludes real-time wireless transmission of imagery over long distancesIn this paper, we propose SAGE, a semantic-oriented underwater communications approach to enable real-time wireless imagery transmission over noisy and narrowband channels. SAGE extracts semantically relevant information from images at the sender located underwater and generates a text description that is transmitted to the receiver at the surface, which in turn generates an image from the received text description. SAGE is evaluated using BLIP for image-to-text and Stable Diffusion for text-to-image, showing promising image similarity between the original and the generated images, and a significant reduction in latency up to a hundred-fold, encouraging further research in this area. © 2024 IFIP.

Abstract
Post-hoc explanation methods have often been criticised for abstracting away the decision-making process of deep neural networks. In this work, we would like to provide natural language descriptions for what different layers of a vision backbone have learned. Our DeViL method generates textual descriptions of visual features at different layers of the network as well as highlights the attribution locations of learned concepts. We train a transformer network to translate individual image features of any vision layer into a prompt that a separate off-the-shelf language model decodes into natural language. By employing dropout both per-layer and per-spatial-location, our model can generalize training on image-text pairs to generate localized explanations. As it uses a pre-trained language model, our approach is fast to train and can be applied to any vision backbone. Moreover, DeViL can create open-vocabulary attribution maps corresponding to words or phrases even outside the training scope of the vision model. We demonstrate that DeViL generates textual descriptions relevant to the image content on CC3M, surpassing previous lightweight captioning models and attribution maps, uncovering the learned concepts of the vision backbone. Further, we analyze fine-grained descriptions of layers as well as specific spatial locations and show that DeViL outperforms the current state-of-the-art on the neuron-wise descriptions of the MILANNOTATIONS dataset.