Measuring the similarity between motions and established motion models is
crucial for motion analysis, recognition, generation, and adaptation. To
enhance similarity measurement across diverse contexts, invariant motion
descriptors have been proposed. However, for rigid-body motion, few invariant
descriptors exist that are bi-invariant, meaning invariant to both the body and
world reference frames used to describe the motion. Moreover, their robustness
to singularities is limited. This paper introduces a novel Bi-Invariant Local
Trajectory-Shape descriptor (BILTS) and a corresponding dissimilarity measure.
Mathematical relationships between BILTS and existing descriptors are derived,
providing new insights into their properties. The paper also includes an
algorithm to reproduce the motion from the BILTS descriptor, demonstrating its
bidirectionality and usefulness for trajectory generation. Experimental
validation using datasets of daily-life activities shows the higher robustness
of the BILTS descriptor compared to the bi-invariant ISA descriptor. This
higher robustness supports the further application of bi-invariant descriptors
for motion recognition and generalization.

该研究提出了一种新颖的双不变局部轨迹形状描述符（BILTS）及相应的差异度量方法，通过与现有描述符之间的数学关系进行推导，揭示了它们的属性，实验证明相比于双不变 ISA 描述符，BILTS 描述符具有更高的鲁棒性，可用于运动识别和泛化。

BILTS: 一种新颖的刚体运动的双不变局部轨迹形状描述符

BILTS: A novel bi-invariant local trajectory-shape descriptor for  rigid-body motion

With the rapid development of electronic science and technology, the research
on wearable devices is constantly updated, but for now, it is not comprehensive
for wearable devices to recognize and analyze the movement of specific sports.
Based on this, this paper improves wearable devices of table tennis sport, and
realizes the pattern recognition and evaluation of table tennis players' motor
skills through artificial intelligence. Firstly, a device is designed to
collect the movement information of table tennis players and the actual
movement data is processed. Secondly, a sliding window is made to divide the
collected motion data into a characteristic database of six table tennis
benchmark movements. Thirdly, motion features were constructed based on feature
engineering, and motor skills were identified for different models after
dimensionality reduction. Finally, the hierarchical evaluation system of motor
skills is established with the loss functions of different evaluation indexes.
The results show that in the recognition of table tennis players' motor skills,
the feature-based BP neural network proposed in this paper has higher
recognition accuracy and stronger generalization ability than the traditional
convolutional neural network.

通过改进可穿戴设备对乒乓球运动的识别和分析，本文实现了对乒乓球运动员运动技能的模式识别和评估，采用特征工程构建运动特征，在维度降低后为不同模型识别鉴定运动技能，结果表明，相较于传统的卷积神经网络，本文提出的基于特征的 BP 神经网络在乒乓球运动员运动技能识别中具有更高的识别准确性和更强的泛化能力。

基于人工智能的乒乓球运动员运动技能识别与评估系统设计

Design of Recognition and Evaluation System for Table Tennis Players'  Motor Skills Based on Artificial Intelligence

In this paper, we introduce a method to automatically reconstruct the 3D
motion of a person interacting with an object from a single RGB video. Our
method estimates the 3D poses of the person and the object, contact positions,
and forces and torques actuated by the human limbs. The main contributions of
this work are three-fold. First, we introduce an approach to jointly estimate
the motion and the actuation forces of the person on the manipulated object by
modeling contacts and the dynamics of their interactions. This is cast as a
large-scale trajectory optimization problem. Second, we develop a method to
automatically recognize from the input video the position and timing of
contacts between the person and the object or the ground, thereby significantly
simplifying the complexity of the optimization. Third, we validate our approach
on a recent MoCap dataset with ground truth contact forces and demonstrate its
performance on a new dataset of Internet videos showing people manipulating a
variety of tools in unconstrained environments.

本文提出了一种从单个 RGB 视频自动重建与对象的人交互的 3D 运动的方法，估计人和物体的 3D 姿势，接触位置，被人类肢体激活的力和扭矩，方法主要集中在联合估计运动和动作力，运用大规模的轨迹优化问题，并且从输入视频自动识别出人与物体或地面之间的接触位置和时间，于真实数据集上验证了方法并展示了它在新的互联网视频数据集中的性能。