A person's movement or relative positioning effectively generates raw
electrical signals that can be read by computing machines to apply various
manipulative techniques for the classification of different human activities.
In this paper, a stratified multi-structural approach based on a Residual
network ensembled with Residual MobileNet is proposed, termed as FusionActNet.
The proposed method involves using carefully designed Residual blocks for
classifying the static and dynamic activities separately because they have
clear and distinct characteristics that set them apart. These networks are
trained independently, resulting in two specialized and highly accurate models.
These models excel at recognizing activities within a specific superclass by
taking advantage of the unique algorithmic benefits of architectural
adjustments. Afterward, these two ResNets are passed through a weighted
ensemble-based Residual MobileNet. Subsequently, this ensemble proficiently
discriminates between a specific static and a specific dynamic activity, which
were previously identified based on their distinct feature characteristics in
the earlier stage. The proposed model is evaluated using two publicly
accessible datasets; namely, UCI HAR and Motion-Sense. Therein, it successfully
handled the highly confusing cases of data overlap. Therefore, the proposed
approach achieves a state-of-the-art accuracy of 96.71% and 95.35% in the UCI
HAR and Motion-Sense datasets respectively.

基于 Residual 网络和 Residual MobileNet 集成的分层多结构方法（FusionActNet）能够有效地对不同的人类活动进行分类识别，并在 UCI HAR 和 Motion-Sense 数据集上取得了 96.71% 和 95.35% 的准确率。

解码人类活动：分析可穿戴加速度计与陀螺仪数据进行活动识别

Decoding Human Activities: Analyzing Wearable Accelerometer and  Gyroscope Data for Activity Recognition

Evaluating the individual movements for teammates in soccer players is
crucial for assessing teamwork, scouting, and fan engagement. It has been said
that players in a 90-min game do not have the ball for about 87 minutes on
average. However, it has remained difficult to evaluate an attacking player
without receiving the ball, and to reveal how movement contributes to the
creation of scoring opportunities for teammates. In this paper, we evaluate
players who create off-ball scoring opportunities by comparing actual movements
with the reference movements generated via trajectory prediction. First, we
predict the trajectories of players using a graph variational recurrent neural
network that can accurately model the relationship between players and predict
the long-term trajectory. Next, based on the difference in the modified
off-ball evaluation index between the actual and the predicted trajectory as a
reference, we evaluate how the actual movement contributes to scoring
opportunity compared to the predicted movement. For verification, we examined
the relationship with the annual salary, the goals, and the rating in the game
by experts for all games of a team in a professional soccer league in a year.
The results show that the annual salary and the proposed indicator correlated
significantly, which could not be explained by the existing indicators and
goals. Our results suggest the effectiveness of the proposed method as an
indicator for a player without the ball to create a scoring chance for
teammates.

利用图变分循环神经网络对足球比赛中球员的轨迹进行预测，并对球员在没有球的情况下对进球做出贡献的能力进行评估，结果显示提出的指标与球员的年薪相关性显著，表明该方法可以有效评估球员在没有球的情况下为队友创造进球机会的能力。

通过轨迹预测评估足球中为队友创造得分机会的能力

Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction

Understanding movement described in text documents is important since text
descriptions of movement contain a wealth of geographic and contextual
information about the movement of people, wildlife, goods, and much more. Our
research makes several contributions to improve our understanding of movement
descriptions in text. First, we show how interpreting geographic movement
described in text is challenging because of general spatial terms, linguistic
constructions that make the thing(s) moving unclear, and many types of temporal
references and groupings, among others. Next, as a step to overcome these
challenges, we report on an experiment with human subjects through which we
identify multiple important characteristics of movement descriptions (found in
text) that humans use to differentiate one movement description from another.
Based on our empirical results, we provide recommendations for computational
analysis using movement described in text documents. Our findings contribute
towards an improved understanding of the important characteristics of the
underused information about geographic movement that is in the form of text
descriptions.

研究文本中描述的运动是有挑战性的，由于空间 term，语言构造以及时间参照等多因素影响。研究通过实验，发现人们用来区分不同运动描述的特征，为基于文本的运动计算分析提出了建议。