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.

Background: Recently, a home-based rehabilitation system for stroke survivors [1], 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. Aim: This work aims at evaluating the home-based rehabilitation system on 10 chronic post-stroke spastic patients. Methods and Procedures: In this paper, 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 [1], 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. Outcomes and Results: The postural angle of the patient decreases in the presence of the postural color-based feedback. The reported TVR-based average distance for the simplest exercise also decreases with the use of the motion-based feedback, meaning that the patient follows the proposed motion more closely when guided by the color-based feedback. However, it is not straightforward to analyze the mitigated TVR-based average distance for the second exercise. In general, the answers to the questionnaires of the therapist and the patients are very positive.

SPARK represents the first edition of the SPAcecraft Recognition leveraging Knowledge of space environment competition organized by the Interdisciplinary Centre for Security, Reliability and Trust (SnT) in conjunction with the 2021 IEEE International Conference in Image Processing (ICIP 2021). By providing a unique synthetic dataset composed of 150k annotated multi-modal images, SPARK aims at encouraging researchers to develop innovative solutions for space target recognition and detection. This paper introduces the proposed dataset and provides a global analysis of the results obtained for the 17 submissions.

Over the last few decades, action recognition applications have attracted the growing interest of researchers, especially with the advent of RGB-D cameras. These applications increasingly require fast processing. Therefore, it becomes important to include the computational latency in the evaluation criteria.