Several therapy routines require deep breathing exercises as a key component
and patients undergoing such therapies must perform these exercises regularly.
Assessing the outcome of a therapy and tailoring its course necessitates
monitoring a patient's compliance with the therapy. While therapy compliance
monitoring is routine in a clinical environment, it is challenging to do in an
at-home setting. This is so because a home setting lacks access to specialized
equipment and skilled professionals needed to effectively monitor the
performance of a therapy routine by a patient. For some types of therapies,
these challenges can be addressed with the use of consumer-grade hardware, such
as earphones and smartphones, as practical solutions. To accurately monitor
breathing exercises using wireless earphones, this paper proposes a framework
that has the potential for assessing a patient's compliance with an at-home
therapy. The proposed system performs real-time detection of breathing phases
and channels with high accuracy by processing a $\mathbf{500}$ ms audio signal
through two convolutional neural networks. The first network, called a channel
classifier, distinguishes between nasal and oral breathing, and a pause. The
second network, called a phase classifier, determines whether the audio segment
is from inhalation or exhalation. According to $k$-fold cross-validation, the
channel and phase classifiers achieved a maximum F1 score of $\mathbf{97.99\%}$
and $\mathbf{89.46\%}$, respectively. The results demonstrate the potential of
using commodity earphones for real-time breathing channel and phase detection
for breathing therapy compliance monitoring.

利用商品耳机实时监测呼吸频道和阶段以评估呼吸疗法的遵守情况的潜力。

基于无线耳机的呼吸运动实时监测：一种深度学习方法

Wireless Earphone-based Real-Time Monitoring of Breathing Exercises: A  Deep Learning Approach

Introduction: In the realm of human-computer interaction and behavioral
research, accurate real-time gaze estimation is critical. Traditional methods
often rely on expensive equipment or large datasets, which are impractical in
many scenarios. This paper introduces a novel, geometry-based approach to
address these challenges, utilizing consumer-grade hardware for broader
applicability. Methods: We leverage novel face landmark detection neural
networks capable of fast inference on consumer-grade chips to generate accurate
and stable 3D landmarks of the face and iris. From these, we derive a small set
of geometry-based descriptors, forming an 8-dimensional manifold representing
the eye and head movements. These descriptors are then used to formulate linear
equations for predicting eye-gaze direction. Results: Our approach demonstrates
the ability to predict gaze with an angular error of less than 1.9 degrees,
rivaling state-of-the-art systems while operating in real-time and requiring
negligible computational resources. Conclusion: The developed method marks a
significant step forward in gaze estimation technology, offering a highly
accurate, efficient, and accessible alternative to traditional systems. It
opens up new possibilities for real-time applications in diverse fields, from
gaming to psychological research.

该论文介绍了一种基于几何的方法来解决现有的真实时间凝视估计系统所面临的挑战，利用普通消费级硬件生成准确、稳定的人脸和虹膜的三维地标，从而预测眼睛凝视方向，实现低计算资源要求下的高精度、实时的眼睛凝视估计。

基于几何的低成本眼睛注视检测：使用基于深度学习产生的面部标志点

Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks  Generated through Deep Learning

Emotion recognition is the task of classifying perceived emotions in people.
Previous works have utilized various nonverbal cues to extract features from
images and correlate them to emotions. Of these cues, situational context is
particularly crucial in emotion perception since it can directly influence the
emotion of a person. In this paper, we propose an approach for high-level
context representation extraction from images. The model relies on a single cue
and a single encoding stream to correlate this representation with emotions.
Our model competes with the state-of-the-art, achieving an mAP of 0.3002 on the
EMOTIC dataset while also being capable of execution on consumer-grade hardware
at approximately 90 frames per second. Overall, our approach is more efficient
than previous models and can be easily deployed to address real-world problems
related to emotion recognition.

本文提出了一种从图像中提取高级语境表示的方法，利用单个线索和单个编码流与情感相关联，并且该模型的执行效率比以前的模型更高，其在 EMOTIC 数据集上达到了 0.3002 的 mAP 并且能够在消费级硬件上以每秒大约 90 帧的速度执行，容易部署在解决与情感识别相关的现实问题中。

图像情感识别的高层次语境表示

High-Level Context Representation for Emotion Recognition in Images

Reconstructing dense, volumetric models of real-world 3D scenes is important
for many tasks, but capturing large scenes can take significant time, and the
risk of transient changes to the scene goes up as the capture time increases.
These are good reasons to want instead to capture several smaller sub-scenes
that can be joined to make the whole scene. Achieving this has traditionally
been difficult: joining sub-scenes that may never have been viewed from the
same angle requires a high-quality camera relocaliser that can cope with novel
poses, and tracking drift in each sub-scene can prevent them from being joined
to make a consistent overall scene. Recent advances, however, have
significantly improved our ability to capture medium-sized sub-scenes with
little to no tracking drift: real-time globally consistent reconstruction
systems can close loops and re-integrate the scene surface on the fly, whilst
new visual-inertial odometry approaches can significantly reduce tracking drift
during live reconstruction. Moreover, high-quality regression forest-based
relocalisers have recently been made more practical by the introduction of a
method to allow them to be trained and used online. In this paper, we leverage
these advances to present what to our knowledge is the first system to allow
multiple users to collaborate interactively to reconstruct dense, voxel-based
models of whole buildings using only consumer-grade hardware, a task that has
traditionally been both time-consuming and dependent on the availability of
specialised hardware. Using our system, an entire house or lab can be
reconstructed in under half an hour and at a far lower cost than was previously
possible.

本论文利用实时全局一致的重建系统和新的视觉惯性里程计方法，结合高质量的基于回归森林的定位方法，实现了第一款允许多用户合作使用普通硬件在半小时内重建整个建筑的系统。