Individuals with diverse motor abilities often benefit from intensive and
specialized rehabilitation therapies aimed at enhancing their functional
recovery. Nevertheless, the challenge lies in the restricted availability of
neurorehabilitation professionals, hindering the effective delivery of the
necessary level of care. Robotic devices hold great potential in reducing the
dependence on medical personnel during therapy but, at the same time, they
generally lack the crucial human interaction and motivation that traditional
in-person sessions provide. To bridge this gap, we introduce an AI-based system
aimed at delivering personalized, out-of-hospital assistance during
neurorehabilitation training. This system includes a rehabilitation training
device, affective signal classification models, training exercises, and a
socially interactive agent as the user interface. With the assistance of a
professional, the envisioned system is designed to be tailored to accommodate
the unique rehabilitation requirements of an individual patient. Conceptually,
after a preliminary setup and instruction phase, the patient is equipped to
continue their rehabilitation regimen autonomously in the comfort of their
home, facilitated by a socially interactive agent functioning as a virtual
coaching assistant. Our approach involves the integration of an interactive
socially-aware virtual agent into a neurorehabilitation robotic framework, with
the primary objective of recreating the social aspects inherent to in-person
rehabilitation sessions. We also conducted a feasibility study to test the
framework with healthy patients. The results of our preliminary investigation
indicate that participants demonstrated a propensity to adapt to the system.
Notably, the presence of the interactive agent during the proposed exercises
did not act as a source of distraction; instead, it positively impacted users'
engagement.

通过整合交互式的社交感知虚拟助理到神经康复机器人框架中，本研究的主要目标是重现与面对面康复会议相关的社交方面。我们还进行了一项可行性研究，测试了健康患者对该框架的反应，初步结果表明参与者表现出了适应系统的倾向。值得注意的是，在所提出的锻炼中，交互式助理的存在并没有分散注意力，而是积极地促进了用户的参与度。

机器人神经康复训练的社交互动代理：概念提出与概念验证研究

Socially Interactive Agents for Robotic Neurorehabilitation Training:  Conceptualization and Proof-of-concept Study

Electrocardiograms (ECGs), a medical monitoring technology recording cardiac
activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is
based on the analysis of the deformation of the signal shapes due to irregular
heart rates associated with heart diseases. Due to the infeasibility of manual
examination of large volumes of ECG data, this paper aims to propose an
automated AI based system for ECG-based arrhythmia classification. To this
front, a deep learning based solution has been proposed for ECG-based
arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10
sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from
PhysioNet with four different types of arrhythmias were used. The sampling
frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG
signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was
estimated and stacked row wise to obtain a single image from 12 channels,
resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF
scalograms are fed to the pretrained convolutional neural network (CNN), 1D
CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia
classification. The fine-tuned CNN models obtained the best test accuracy of
about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia
classification.

本文提出了一种基于人工智能的自动化心电图分类系统，利用深度学习和卷积神经网络模型对心电图信号进行分析和识别以实现准确的心律失常分类。

利用心电图信号的堆叠时频标图图像进行心律失常分类的深度学习模型

Deep Learning Models for Arrhythmia Classification Using Stacked  Time-frequency Scalogram Images from ECG Signals

The demand for psychological counseling has grown significantly in recent
years, particularly with the global outbreak of COVID-19, which has heightened
the need for timely and professional mental health support. Online
psychological counseling has emerged as the predominant mode of providing
services in response to this demand. In this study, we propose the Psy-LLM
framework, an AI-based system leveraging Large Language Models (LLMs) for
question-answering in online psychological consultation. Our framework combines
pre-trained LLMs with real-world professional Q&A from psychologists and
extensively crawled psychological articles. The Psy-LLM framework serves as a
front-end tool for healthcare professionals, allowing them to provide immediate
responses and mindfulness activities to alleviate patient stress. Additionally,
it functions as a screening tool to identify urgent cases requiring further
assistance. We evaluated the framework using intrinsic metrics, such as
perplexity, and extrinsic evaluation metrics, with human participant
assessments of response helpfulness, fluency, relevance, and logic. The results
demonstrate the effectiveness of the Psy-LLM framework in generating coherent
and relevant answers to psychological questions. This article concludes by
discussing the potential of large language models to enhance mental health
support through AI technologies in online psychological consultation.

该研究提出了 Psy-LLM 框架，这是利用大型语言模型（LLMs）进行在线心理咨询问答的基于人工智能的系统，旨在提供心理健康支持，并通过评估证明了其在生成连贯和相关答案方面的有效性。