Good posture and form are essential for safe and productive exercising. Even
in gym settings, trainers may not be readily available for feedback.
Rehabilitation therapies and fitness workouts can thus benefit from recommender
systems that provide real-time evaluation. In this paper, we present an
algorithmic pipeline that can diagnose problems in exercise techniques and
offer corrective recommendations, with high sensitivity and specificity in
real-time. We use MediaPipe for pose recognition, count repetitions using
peak-prominence detection, and use a learnable physics simulator to track
motion evolution for each exercise. A test video is diagnosed based on
deviations from the prototypical learned motion using statistical learning. The
system is evaluated on six full and upper body exercises. These real-time
recommendations, counseled via low-cost equipment like smartphones, will allow
exercisers to rectify potential mistakes making self-practice feasible while
reducing the risk of workout injuries.

通过使用 MediaPipe 进行姿势识别、峰值检测来计算重复次数以及使用可学习的物理模拟来追踪运动演变，本文提出了一个算法流程，可以实时检测和纠正运动技术问题，并通过统计学习基于典型学习运动来诊断测试视频。这个低成本设备如智能手机支持的实时推荐系统将帮助锻炼者纠正潜在的错误，使得自我练习成为可行的同时降低锻炼伤害的风险。

使用可学习的物理知识进行实时锻炼姿势建议

Using Learnable Physics for Real-Time Exercise Form Recommendations

Current evaluations of Continual Learning (CL) methods typically assume that
there is no constraint on training time and computation. This is an unrealistic
assumption for any real-world setting, which motivates us to propose: a
practical real-time evaluation of continual learning, in which the stream does
not wait for the model to complete training before revealing the next data for
predictions. To do this, we evaluate current CL methods with respect to their
computational costs. We conduct extensive experiments on CLOC, a large-scale
dataset containing 39 million time-stamped images with geolocation labels. We
show that a simple baseline outperforms state-of-the-art CL methods under this
evaluation, questioning the applicability of existing methods in realistic
settings. In addition, we explore various CL components commonly used in the
literature, including memory sampling strategies and regularization approaches.
We find that all considered methods fail to be competitive against our simple
baseline. This surprisingly suggests that the majority of existing CL
literature is tailored to a specific class of streams that is not practical. We
hope that the evaluation we provide will be the first step towards a paradigm
shift to consider the computational cost in the development of online continual
learning methods.

该研究提出了一种实用的、实时的、基于计算成本的 Continual Learning 方法评估方法，并使用包含 3900 万个时间戳标记的图像的 CLOC 数据集展开了大量实验，发现现有的 CL 方法并不适用于现实场景，并且介绍了记忆取样策略和正则化方法等常用 CL 组件，证实了对计算成本进行考虑是开发在线 Continual Learning 方法的必要基础。

在线连续学习中的实时评估：新的希望

Real-Time Evaluation in Online Continual Learning: A New Hope

Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher.

采用新的神经网络结构实现移动图像处理，使用对输入 / 输出图像成对的训练集，预测局部仿射模型的系数，使用新的切片节点对上采样的变换进行边缘保持，对高分辨率图像在智能手机上进行毫秒级的处理，并在大类图像操作中匹配最先进的逼近技术质量。