Motivated by the growing interest in enhancing intuitive physical
Human-Machine Interaction (HRI/HVI), this study aims to propose a robust
tactile hand gesture recognition system. We performed a comprehensive
evaluation of different hand gesture recognition approaches for a large area
tactile sensing interface (touch interface) constructed from conductive
textiles. Our evaluation encompassed traditional feature engineering methods,
as well as contemporary deep learning techniques capable of real-time
interpretation of a range of hand gestures, accommodating variations in hand
sizes, movement velocities, applied pressure levels, and interaction points.
Our extensive analysis of the various methods makes a significant contribution
to tactile-based gesture recognition in the field of human-machine interaction.

本研究旨在提出一个强大的触觉手势识别系统，通过对各种手势识别方法进行综合评估，包括传统的特征工程方法和能够实时解释各种手势的当代深度学习技术，以适应手的大小、运动速度、施加压力水平和交互点的变化，我们对各种方法的深入分析为人机交互领域的基于触觉的手势识别做出了重要贡献。

增强人机交互的触觉手势识别技术进展

Advancements in Tactile Hand Gesture Recognition for Enhanced  Human-Machine Interaction

Mobile Internet user credit assessment is an important way for communication
operators to establish decisions and formulate measures, and it is also a
guarantee for operators to obtain expected benefits. However, credit evaluation
methods have long been monopolized by financial industries such as banks and
credit. As supporters and providers of platform network technology and network
resources, communication operators are also builders and maintainers of
communication networks. Internet data improves the user's credit evaluation
strategy. This paper uses the massive data provided by communication operators
to carry out research on the operator's user credit evaluation model based on
the fusion LightGBM algorithm. First, for the massive data related to user
evaluation provided by operators, key features are extracted by data
preprocessing and feature engineering methods, and a multi-dimensional feature
set with statistical significance is constructed; then, linear regression,
decision tree, LightGBM, and other machine learning algorithms build multiple
basic models to find the best basic model; finally, integrates Averaging,
Voting, Blending, Stacking and other integrated algorithms to refine multiple
fusion models, and finally establish the most suitable fusion model for
operator user evaluation.

通过使用通信运营商提供的大规模数据，本文基于融合 LightGBM 算法对运营商用户信用评估模型进行了研究，首先通过数据预处理和特征工程方法提取与用户评估相关的关键特征，构建具有统计学意义的多维特征集合；然后，使用线性回归、决策树、LightGBM 等机器学习算法构建多个基础模型，找出最佳基础模型；最后，整合平均、投票、混合、堆叠等集成算法，优化多个融合模型，最终建立适用于运营商用户评估的最佳融合模型。