Extended Reality, artificial intelligence, and big data technologies offer new opportunities for advancing rehabilitation diagnosis and training. This study presents a method for extracting behavioral features from a visual search task conducted in an immersive virtual reality environment. To identify motion patterns specific to individual patients, we employ a topological mapping approach based on Growing Neural Gas (GNG), which adapts its structure dynamically using node activation and error-based edge management. While GNG effectively captures spatial characteristics, it lacks the ability to model temporal relationships and is sensitive to hyperparameter settings. To address these limitations, we introduce a spatiotemporal topological clustering method, along with a hierarchical framework that enables segmentation at multiple levels of granularity. Furthermore, to evaluate feasibility, we conducted a visual search task with three patients, including one with USN, and performed a comparative analysis of their extracted motion features.

|Unilateral Spatial Neglect (USN) is characterized by an attention deficit to one side of space, where individuals struggle to perceive stimuli on that side without a lack of sensation. Traditional paper-pencil tasks like line cancellation and copying tests are commonly used to assess USN, but they have limitations in evaluating neglect areas confined to a two-dimensional plane. Immersive VR systems and multimodal sensing systems offer a more sensitive approach for diagnosis and training. In related works, AR/VR systems and eye-tracking devices are utilized for measuring, evaluating, and creating assessment tasks for USN. However, these approaches can only analyze relationships between perception and movements in specific environments. In this study, we propose a method for cognitive modeling based on perceiving-acting cycle in USN, utilizing computational intelligence techniques to establish a structured coupling framework, aiming to contribute to a novel and effective approach for understanding and addressing USN.

This paper presents a method for human motion segmentation aimed at motion analysis in healthcare and rehabilitation. Motion segmentation involves extracting small movements, known as motion primitives, from a sequence of behavioral patterns. While previous works have utilized unsupervised clustering methods as effective approaches for motion segmentation, many of these methods require prior knowledge to enhance performance. To overcome these challenges, we propose a hierarchical topological clustering method capable of representing spatiotemporal features using GNG and the Pulse Neuron Model. Additionally, we present experiments and discussions to validate the applicability of the proposed method for motion analysis in exercise.

Theaim of this study is to model the cognitive processes based on a perceiving-acting cycle in patients with unilateral spatial neglect (USN). USN is the inability to perceive features of the environment, body, or objects on one side. To extract the cognitive characteristics of USN patients in a multifaceted manner, we constructed a multimodal sensing system using immersive VR. In this paper, we present a system that predicts movement of a subject while performing a search task using the measurement results and an LSTM neural network.

In this study, we aim to develop a measurement system for evaluating walking ability in daily life. Health promotion is one of the most important tasks to improve quality of life and quality of community for elderly people. Disabilities related to loss of independence in performing activities of daily living can lead to their social isolation and loneliness that can induce immobility and depression, producing the vicious cycle. Various methods have been proposed to measure lower limb joint angles and positions by using wearable systems and motion capture systems. However, such systems are too expensive and big for elderly's daily self-monitoring. This paper presents a method of lower limb joint angle estimation using a Kinect sensor. The sensor has a built-in processor to detect joint positions. However, inverse kinematics problem is required to be addressed in order to derive the joint angles. We therefore propose a hybrid approach for lower limb joint angle estimation using genetic algorithm and feed forward neural network.

Unilateral spatial neglect (USN) is defined as failure to report, respond, or adjust to novel or meaningful stimuli presented to the side opposite a brain lesion. This symptom deteriorates the rehabilitation efficiency and hinders activities of daily living. USN is classified into 2 sub-categories: body-centered neglect and object-centered neglect. No optimal intervention has been proposed for treating object-centered neglect. Thus, we developed a visual cueing system using immersive virtual reality to improve object-centered neglect. A feasibility study showed 2 typical measurements for object-centered neglect tended to improve the symptom, these results provide preliminary evidence for designing successful rehabilitation programs using a visual cueing system in patients with object-centered neglect.