Cognitive diagnosis is a crucial task in computational education, aimed at
evaluating students' proficiency levels across various knowledge concepts
through exercises. Current models, however, primarily rely on students'
answered exercises, neglecting the complex and rich information contained in
un-interacted exercises. While recent research has attempted to leverage the
data within un-interacted exercises linked to interacted knowledge concepts,
aiming to address the long-tail issue, these studies fail to fully explore the
informative, un-interacted exercises related to broader knowledge concepts.
This oversight results in diminished performance when these models are applied
to comprehensive datasets. In response to this gap, we present the
Collaborative-aware Mixed Exercise Sampling (CMES) framework, which can
effectively exploit the information present in un-interacted exercises linked
to un-interacted knowledge concepts. Specifically, we introduce a novel
universal sampling module where the training samples comprise not merely raw
data slices, but enhanced samples generated by combining weight-enhanced
attention mixture techniques. Given the necessity of real response labels in
cognitive diagnosis, we also propose a ranking-based pseudo feedback module to
regulate students' responses on generated exercises. The versatility of the
CMES framework bolsters existing models and improves their adaptability.
Finally, we demonstrate the effectiveness and interpretability of our framework
through comprehensive experiments on real-world datasets.

通过综合实验，本研究表明 CMES 框架的有效性和可解释性，该框架可以利用未交互锻炼中与广泛知识概念相对应的信息，提高现有模型的适应性。

一种基于协作感知的混合采样方法增强认知诊断

Enhancing Cognitive Diagnosis using Un-interacted Exercises: A  Collaboration-aware Mixed Sampling Approach

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

Maintaining proper form while exercising is important for preventing injuries
and maximizing muscle mass gains. Detecting errors in workout form naturally
requires estimating human's body pose. However, off-the-shelf pose estimators
struggle to perform well on the videos recorded in gym scenarios due to factors
such as camera angles, occlusion from gym equipment, illumination, and
clothing. To aggravate the problem, the errors to be detected in the workouts
are very subtle. To that end, we propose to learn exercise-oriented image and
video representations from unlabeled samples such that a small dataset
annotated by experts suffices for supervised error detection. In particular,
our domain knowledge-informed self-supervised approaches (pose contrastive
learning and motion disentangling) exploit the harmonic motion of the exercise
actions, and capitalize on the large variances in camera angles, clothes, and
illumination to learn powerful representations. To facilitate our
self-supervised pretraining, and supervised finetuning, we curated a new
exercise dataset, \emph{Fitness-AQA}
(https://github.com/ParitoshParmar/Fitness-AQA), comprising of three
exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by
expert trainers for multiple crucial and typically occurring exercise errors.
Experimental results show that our self-supervised representations outperform
off-the-shelf 2D- and 3D-pose estimators and several other baselines. We also
show that our approaches can be applied to other domains/tasks such as pose
estimation and dive quality assessment.

通过自监督的学习和对锻炼形式的理解，我们提出了一种在容易出现镜头角度、遮挡和光照变化的体育馆场景下，能够有效对运动员姿势进行检测的方法。我们还针对此目的创建了一个包含三种训练动作的新锻炼数据集，并且其自监督特征的表现超过了现有的 2D 和 3D 姿态估计器。