Longitudinal evaluation of balance quality using a modified bathroom scale: usability and acceptability

We adapted a commercial bathroom scale in order to acquire the raw data from the weight sensors and then to send them to a server via a mobile phone. We investigated the usability and acceptability of the device in a long-term experiment with 22 elderly users that produced more than 5000 weight recordings. Four basic variables were extracted from the vertical force measurements and the stabilogram. The technology was accepted unreservedly, presumably because it did not differ from devices usually encountered in the home. The quantitative results showed a high variability of day-to-day measurement, which was countered by taking a moving average. A balance index was able to identify changes in balance over time. The preliminary results appear promising

Structure-Enhanced Attentive Learning for Spine Segmentation from Ultrasound Volume Projection Images

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis

Joint Spine Segmentation and Noise Removal from Ultrasound Volume Projection Images with Selective Feature Sharing.

Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: 1) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; 2) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis