Ștefan-Daniel Achirei

There are numerous methods available for evaluating leg length discrepancy (LLD), ranging from classic clinical techniques to advanced systems based on sophisticated and expensive equipment, as well as rudimentary manual adjustment mechanisms for the prosthesis by specialists. However, unilateral amputee patients often face difficulties in accessing these solutions. They either lack the necessary equipment or do not have a medical specialist available to assist them in preventing postural imbalances. This study proposes the first smartphone-based computer vision system that evaluates and automatically compensates for leg length discrepancy in transtibial prostheses, offering a low-cost, accessible, and fully autonomous alternative to existing solutions. The method was tested using complex metrological systems. The application of the proposed method demonstrated its effectiveness in correcting simulated LLD for various values. Experimental validation demonstrated the system’s ability to restore symmetry in simulated LLD cases within the 1–10 mm range, achieving a relative compensation error of 2.44%. The proposed method for correcting LLD, based on computer vision and integrated into a smartphone, represents a significant advancement in restoring symmetry for unilaterally amputated patients. This technology could provide an accessible, efficient solution, thereby reducing the need for frequent prosthetist visits and enhancing user autonomy.

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

The main goal of this study is to develop a gaming platform for people who have had a stroke and are going through various stages of neuromotor rehabilitation. The main idea is that regular recovery activities are boring and repetitive, which might make people less interested in being in therapy. By including enjoyable but practical games into workout routines, there is potential to enhance patients’ motivation. To ensure the accuracy and reliability of the system, a validation process was conducted with the help of two stroke patients now undergoing rehabilitation at the Clinical Rehabilitation Hospital in Iași. The parameters were adjusted based on individual patient characteristics, and a total of 12 tests were conducted, from which various results were obtained. At the end of the clinical trial, feedback was gathered from patients in order to get first-hand perspectives from the intended users of the system and identify areas for potential system enhancement. In the end, this research has demonstrated some of the benefits and potential uses of virtual reality (VR) technology in patient rehabilitation, and the participants in the clinical study have found it to be positive and useful.

The growing share of the population over the age of 65 is putting pressure on the social health insurance system, especially on institutions that provide long-term care services for the elderly or to people who suffer from chronic diseases or mental disabilities. This pressure can be reduced through the assisted living of the patients, based on an intelligent system for monitoring vital signs and home automation. In this regard, since 2008, the European Commission has financed the development of medical products and services through the ambient assisted living (AAL) program—Ageing Well in the Digital World. The SmartCare Project, which integrates the proposed Computer Vision solution, follows the European strategy on AAL. This paper presents an indoor human activity recognition (HAR) system based on scene understanding. The system consists of a ZED 2 stereo camera and a NVIDIA Jetson AGX processing unit. The recognition of human activity is carried out in two stages: all humans and objects in the frame are detected using a neural network, then the results are fed to a second network for the detection of interactions between humans and objects. The activity score is determined based on the human–object interaction (HOI) detections.

Individuals are highly accurate for visually understanding natural scenes. By extracting and extrapolating data we reach the highest stage of scene understanding. In the past few years it proved to be an essential part in computer vision applications. It goes further than object detection by bringing machine perceiving closer to the human one: integrates meaningful information and extracts semantic relationships and patterns. Researchers in computer vision focused on scene understanding algorithms, the aim being to obtain semantic knowledge from the environment and determine the properties of objects and the relations between them. For applications in robotics, gaming, assisted living, augmented reality, etc a fundamental task is to be aware of spatial position and capture depth information. First part of this paper focuses on deep learning solutions for scene recognition following the main leads: low-level features and object detection. In the second part we present extensively the most relevant datasets for visual scene understanding. We take into consideration both directions having in mind future applications.