Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental
condition characterized by inattention, hyperactivity, and impulsivity, which
can significantly impact an individual's daily functioning and quality of life.
Occupational therapy plays a crucial role in managing ADHD by fostering the
development of skills needed for daily living and enhancing an individual's
ability to participate fully in school, home, and social situations. Recent
studies highlight the potential of integrating Large Language Models (LLMs)
like ChatGPT and Socially Assistive Robots (SAR) to improve psychological
treatments. This integration aims to overcome existing limitations in mental
health therapy by providing tailored support and adapting to the unique needs
of this sensitive group. However, there remains a significant gap in research
exploring the combined use of these advanced technologies in ADHD therapy,
suggesting an opportunity for novel therapeutic approaches.
Thus, we integrated two advanced language models, ChatGPT-4 Turbo and
Claude-3 Opus, into a robotic assistant to explore how well each model performs
in robot-assisted interactions. Additionally, we have compared their
performance in a simulated therapy scenario to gauge their effectiveness
against a clinically validated customized model. The results of this study show
that ChatGPT-4 Turbo excelled in performance and responsiveness, making it
suitable for time-sensitive applications. Claude-3 Opus, on the other hand,
showed strengths in understanding, coherence, and ethical considerations,
prioritizing safe and engaging interactions. Both models demonstrated
innovation and adaptability, but ChatGPT-4 Turbo offered greater ease of
integration and broader language support. The selection between them hinges on
the specific demands of ADHD therapy.

注意力缺陷多动障碍是一种神经发育性疾病，占据个人日常功能和生活质量的重要位置，职业治疗通过促进日常生活技能的发展和增强个人在学校、家庭和社交场合的全面参与能力，在控制心理治疗中发挥着关键作用。最近的研究突出了集成大型语言模型（如 ChatGPT）和社会辅助机器人（SAR）以改善心理治疗的潜力，通过提供量身定制的支持并适应这一敏感人群的独特需求来克服现有心理健康治疗的局限性。然而，目前在 ADHD 治疗中探索这些先进技术的综合应用仍存在重大差距，这为新的治疗方法提供了机会。因此，我们将 ChatGPT-4 Turbo 和 Claude-3 Opus 两种先进的语言模型集成到一个机器人助手中，以探索每个模型在机器人辅助交互中的性能。此外，我们还比较了它们在模拟治疗场景中的表现，以衡量其对临床验证的定制模型的效果。研究结果显示，ChatGPT-4 Turbo 在性能和响应能力方面表现出色，适用于时间敏感的应用。而 Claude-3 Opus 在理解力、连贯性和伦理考虑方面表现出优势，重视安全和有吸引力的互动。两个模型都展示了创新和适应性，但 ChatGPT-4 Turbo 提供了更高的集成便利性和更广泛的语言支持。选择其中一个模型要视 ADHD 治疗的具体要求而定。

探索机器人助手与 ChatGPT 和 Claude 在增强 ADHD 疗法中的功效：创新治疗范式

Exploring the Efficacy of Robotic Assistants with ChatGPT and Claude in  Enhancing ADHD Therapy: Innovating Treatment Paradigms

In recent years, the preliminary diagnosis of Attention Deficit Hyperactivity
Disorder (ADHD) using electroencephalography (EEG) has garnered attention from
researchers. EEG, known for its expediency and efficiency, plays a pivotal role
in the diagnosis and treatment of ADHD. However, the non-stationarity of EEG
signals and inter-subject variability pose challenges to the diagnostic and
classification processes. Topological Data Analysis (TDA) offers a novel
perspective for ADHD classification, diverging from traditional time-frequency
domain features. Yet, conventional TDA models are restricted to single-channel
time series and are susceptible to noise, leading to the loss of topological
features in persistence diagrams.This paper presents an enhanced TDA approach
applicable to multi-channel EEG in ADHD. Initially, optimal input parameters
for multi-channel EEG are determined. Subsequently, each channel's EEG
undergoes phase space reconstruction (PSR) followed by the utilization of
k-Power Distance to Measure (k-PDTM) for approximating ideal point clouds.
Then, multi-dimensional time series are re-embedded, and TDA is applied to
obtain topological feature information. Gaussian function-based Multivariate
Kernel Density Estimation (MKDE) is employed in the merger persistence diagram
to filter out desired topological feature mappings. Finally, persistence image
(PI) method is utilized to extract topological features, and the influence of
various weighting functions on the results is discussed.The effectiveness of
our method is evaluated using the IEEE ADHD dataset. Results demonstrate that
the accuracy, sensitivity, and specificity reach 85.60%, 83.61%, and 88.33%,
respectively. Compared to traditional TDA methods, our method was effectively
improved and outperforms typical nonlinear descriptors. These findings indicate
that our method exhibits higher precision and robustness.

近年来，利用脑电图（EEG）进行注意力缺陷多动障碍（ADHD）预诊断吸引了研究人员的关注。本文提出一种改进的拓扑数据分析方法，用于多通道 EEG 信号在 ADHD 中的应用。结果表明，该方法具有更高的精确度和鲁棒性。

多通道脑电拓扑特征搜索方法：在注意力缺陷多动障碍分类中的应用

Topological Feature Search Method for Multichannel EEG: Application in  ADHD classification

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral
disorder worldwide. While extensive research has focused on machine learning
methods for ADHD diagnosis, most research relies on high-cost equipment, e.g.,
MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the
action characteristics of ADHD are desired. Skeleton-based action recognition
has gained attention due to the action-focused nature and robustness. In this
work, we propose a novel ADHD diagnosis system with a skeleton-based action
recognition framework, utilizing a real multi-modal ADHD dataset and
state-of-the-art detection algorithms. Compared to conventional methods, the
proposed method shows cost-efficiency and significant performance improvement,
making it more accessible for a broad range of initial ADHD diagnoses. Through
the experiment results, the proposed method outperforms the conventional
methods in accuracy and AUC. Meanwhile, our method is widely applicable for
mass screening.

本文提出了一种基于骨骼动作识别的注意力缺陷多动障碍诊断系统，利用真实的多模式 ADHD 数据集和先进的检测算法。与传统方法相比，该方法在成本效益和性能方面均表现出色，适用于广泛的初步 ADHD 诊断和大规模筛查。