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

Flexible endoscopes for colonoscopy present several limitations due to their
inherent complexity, resulting in patient discomfort and lack of intuitiveness
for clinicians. Robotic devices together with autonomous control represent a
viable solution to reduce the workload of endoscopists and the training time
while improving the overall procedure outcome. Prior works on autonomous
endoscope control use heuristic policies that limit their generalisation to the
unstructured and highly deformable colon environment and require frequent human
intervention. This work proposes an image-based control of the endoscope using
Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit
adaptive behaviour in convoluted sections of the colon tract. DVC learns a
mapping between the endoscopic images and the control signal of the endoscope.
A first user study of 20 expert gastrointestinal endoscopists was carried out
to compare their navigation performance with DVC policies using a realistic
virtual simulator. The results indicate that DVC shows equivalent performance
on several assessment parameters, being more safer. Moreover, a second user
study with 20 novice participants was performed to demonstrate easier human
supervision compared to a state-of-the-art heuristic control policy. Seamless
supervision of colonoscopy procedures would enable interventionists to focus on
the medical decision rather than on the control problem of the endoscope.

本研究通过使用深度强化学习提出一种基于图像的内窥镜控制方法，能在大肠中出现复杂的部位展现出适应性行为，相比现有启发式控制策略在评估参数上表现相当甚至更安全，并且展示出更易于人类监督的特点，这将使介入医生能够将注意力集中在医疗决策上而不是内镜控制问题上。