儿童注意力缺陷与多动障碍辅助检测及康复训练行为数据

利用数字疗法，优化儿童注意力缺陷与多动障碍的辅助检测与训练本项目通过结合虚拟现实软硬件，实现对视听觉认知行为测训中的行为数据的收集与分析。我们使用遗传算法和模拟退火算法等智能算法来实现认知训练方案的生成。在这个过程中，我们通过认知任务项目中的行为数据进行量化，得出多个指标（如商数值、正确率、测试结果等），并设定方案生成的要求和约束条件，将生成训练方案实际问题转化为多目标优化的数学模型。同时，我们建立了一个常模数据库，招募了不同性别、年龄的受试者作为常模样本，采集了他们的操作行为学数据。通过相关数据分析方法，形成了基于虚拟现实认知测训的常模数据库，从而计算得出商数结论和商数等级这两个指标。

Using digital therapeutics to optimize auxiliary detection and training for children with Attention Deficit and Hyperactivity Disorder (ADHD). This project integrates virtual reality (VR) hardware and software to collect and analyze behavioral data generated during visual-auditory cognitive behavioral assessment and training. We utilize intelligent algorithms such as genetic algorithms and simulated annealing algorithms to generate customized cognitive training programs. During this process, we quantify behavioral data collected from cognitive task items to derive multiple indicators including quotient values, accuracy rates, test outcomes, and others. We also define requirements and constraints for program generation, and transform the practical problem of developing training programs into a multi-objective optimization mathematical model. Meanwhile, we established a normative database: we recruited subjects of diverse genders and age ranges as normative samples, and collected their operational behavioral data. Through relevant data analysis methods, we constructed the normative database for VR-based cognitive assessment and training, and thus calculated the two indicators of quotient results and quotient grades.

搜集汇总

数据集介绍

特点

该数据集专注于儿童注意力缺陷与多动障碍的辅助检测与康复训练，包含1017条视听觉认知行为数据，利用虚拟现实技术和智能算法进行数据分析和训练方案优化。