Home-Based Rehabilitation System for Stroke Survivors: A Clinical Evaluation.

Recently, a home-based rehabilitation system for stroke survivors (Baptista et al. Comput. Meth. Prog. Biomed. 176:111-120 2019), composed of two linked applications (one for the therapist and another one for the patient), has been introduced. The proposed system has been previously tested on healthy subjects. However, for a fair evaluation, it is necessary to carry out a clinical study considering stroke survivors. This work aims at evaluating the home-based rehabilitation system on 10 chronic post-stroke spastic patients. For this purpose, each patient carries out two exercises implying the motion of the spastic upper limb using the home-based rehabilitation system. The impact of the color-based 3D skeletal feedback, guiding the patients during the training, is studied. The Time Variable Replacement (TVR)-based average distance, as well as the average postural angle used in Baptista et al. (Comput. Meth. Prog. Biomed. 176:111-120 2019), are reported to compare the movement and the posture of the patient with and without showing the feedback proposals, respectively. Furthermore, three different questionnaires, specifically designed for this study, are used to evaluate the user experience of the therapist and the patients. Overall, the reported results suggest the relevance of the proposed system for home-based rehabilitation of stroke survivors.

Localized Trajectories for 2D and 3D Action Recognition.

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.

DermSynth3D: Synthesis of in-the-wild annotated dermatology images.

In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis. However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions, insufficient annotations, and non-standardized image acquisitions. To address these shortcomings, we propose a novel framework called DermSynth3D. DermSynth3D blends skin disease patterns onto 3D textured meshes of human subjects using a differentiable renderer and generates 2D images from various camera viewpoints under chosen lighting conditions in diverse background scenes. Our method adheres to top-down rules that constrain the blending and rendering process to create 2D images with skin conditions that mimic in-the-wild acquisitions, ensuring more meaningful results. The framework generates photo-realistic 2D dermatological images and the corresponding dense annotations for semantic segmentation of the skin, skin conditions, body parts, bounding boxes around lesions, depth maps, and other 3D scene parameters, such as camera position and lighting conditions. DermSynth3D allows for the creation of custom datasets for various dermatology tasks. We demonstrate the effectiveness of data generated using DermSynth3D by training DL models on synthetic data and evaluating them on various dermatology tasks using real 2D dermatological images. We make our code publicly available at https://github.com/sfu-mial/DermSynth3D.

Home self-training: Visual feedback for assisting physical activity for stroke survivors.

BACKGROUND AND OBJECTIVE: With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective is to empower the rehabilitation of post-stroke patients at the comfort of their homes by supporting them while exercising without the physical presence of the therapist. METHODS: A novel low-cost home-based training system is introduced. This system is designed as a composition of two linked applications: one for the therapist and another one for the patient. The therapist prescribes personalized exercises remotely, monitors the home-based training and re-adapts the exercises if required. On the other side, the patient loads the prescribed exercises, trains the prescribed exercise while being guided by color-based visual feedback and gets updates about the exercise performance. To achieve that, our system provides three main functionalities, namely: 1) Feedback proposals guiding a personalized exercise session, 2) Posture monitoring optimizing the effectiveness of the session, 3) Assessment of the quality of the motion. RESULTS: The proposed system is evaluated on 10 healthy participants without any previous contact with the system. To analyze the impact of the feedback proposals, we carried out two different experimental sessions: without and with feedback proposals. The obtained results give preliminary assessments about the interest of using such feedback. CONCLUSIONS: Obtained results on 10 healthy participants are promising. This encourages to test the system in a realistic clinical context for the rehabilitation of stroke survivors.