Alexandros Lioulemes

Guided physical therapy through the use of the Barrett WAM robotic arm

Physical therapy is a crucial part of the rehabilitation process during recovery from an injury that has resulted in motor function loss. Newly introduced technologies can enhance traditional physical therapy, first, by complementing the experts work, and second, by providing a platform for rich data collection and analysis. In this work, we present a prototype adaptive rehabilitation instrument, based on the use of robotic arm, which can be dynamically controlled to guide the exercise motion of the upper extremities, in patients with motor disabilities. Our proposed method, enables simultaneous active and passive control of the robotic arm, to produce adaptive force feedback for motion guidance, and allow for data collection, for patient motor function assessment.

Self-managed patient-game interaction using the barrett WAM arm for motion analysis

In this paper, we present a framework for physical rehabilitation, that uses a combination of video gaming and robotic technology to allow the monitoring and progress tracking of a person during physical therapy. The system, called MAGNI, uses the advanced control capabilities of the Barrett WAM Arm robot and a custom-made video game. The MAGNI system helps the patient to complete a rehabilitation session through a user-system, game-based interaction program, involving exercises prescribed by a therapist. The system can control and supervise the rehabilitation sessions to ensure compliance and safe exercising. It uses motion analysis to provide an evaluation of the patients progress over time. The MAGNI system records the position of the subjects hand during game interaction with the robotic arm and analyzes this data using pattern matching and machine learning algorithms, in order to guide self-managed physical therapy. Our experiments show that we can accurately classify user motion activity between a set of different exercises, and measure user compliance with the prescribed regimens.

Safety challenges in using AR.Drone to collaborate with humans in indoor environments

This paper presents an Unmanned Aerial Vehicle (UAV), based on the AR.Drone platform, which can perform an autonomous navigation in indoor (e.g. corridor, hallway) and industrial environments (e.g. production line). It also has the ability to avoid pedestrians while they are working or walking in the vicinity of the robot. The only sensor in our system is the front camera. For the navigation part our system rely on the vanishing point algorithm, the Hough transform for the wall detection and avoidance, and the HOG descriptors for pedestrian detection using SVM classifier. Our experiments show that our vision navigation procedures are reliable and enable the aerial vehicle to fly without humans intervention and coordinate together in the same workspace. We are able to detect human motion with high confidence of 85% in a corridor and to confirm our algorithm in 80% successful flight experiments.

Robot-aided rehabilitation using force analysis

The estimation of human arm forces is an important factor in physical therapy, especially in robotic-aided physical therapy. Force measurements reveal the rehabilitation progress of patients with poor upper extremity motor function. In this work, we managed to record and analyse the upper arm forces of the patient while executing upper extremity exercises. Our analysis of these forces allows the robot to identify the patients motion capability and apply active motion control to the patients arm when their arms forces deviate from the desired motion set-up by the therapist.

A Motion and Force Analysis System for Human Upper-limb Exercises

This paper describes a novel system that can demonstrate the potential to track and estimate the torques that affect the human arm of an individual that performs rehabilitation exercises with the use of Kinect v2. The system focuses on eliminating the jerky motions captured by the Kinect with the incorporation of robotic mechanics methodologies that have been applied in the field of robotic mechanical design. In order to achieve this results, the system takes full advantage of the dynamic and kinematic formulas that describe the motion rigid bodies. Lastly, a simulation experiment is depicted to demonstrate the results of the system.

MAGNI: A Real-Time Robot-Aided Game-Based Tele-Rehabilitation System

During the last two decades, robotic rehabilitation has become widespread, particularly for upper limb physical rehabilitation. Major findings prove that the efficacy of robot-assisted rehabilitation can be increased by motivation and engagement, which is offered by exploiting the opportunities of gamification and exergaming. This paper presents a tele-rehabilitation framework to enable interaction between therapists and patients and is a combination of a graphical user interface and a high dexterous robotic arm. The system, called MAGNI, integrates a 3D exercise game with a robotic arm, operated by therapist in order to assign in real-time the prerecorded exercises to the patients. We propose a game that can be played by a patient who has suffered an injury to their arm (e.g. Stroke, Spinal Injury, or some physical injury to the shoulder itself). The experimental results and the feedback from the participants show that the system has the potential to impact how robotic physical therapy addresses specific patient’s needs and how occupational therapists assess patient’s progress over time.

Adaptive User and Haptic Interfaces for Smart Assessment and Training

My research is focusing on developing smart robotic rehabilitation interfaces that use machine intelligence to adjust the level of difficulty, assess physical and mental obstacles on the part of the user, and provide analysis of the multi-sensing data collected in real time as the user exercises. The main goal of the interfaces is to engage the patient in repetitive exercise sessions and to provide better data visualization to the therapist for the patients recovery progress. In this doctoral consortium, I will present three prototype user interfaces that can be applied in assistive environments and enhance the productivity and interaction among therapist and patient. The data processing and the decision making algorithms compose the core components of this study.

3D mapping of visual attention for smart rehabilitation

The estimation of human attention as input modality has been suggested as a method for an advanced human-computer interaction. With an increasing interest and development of augmented reality tools, the advent of Microsoft HoloLens glasses and increasingly affordable wearable eye-tracking devices, monitoring the human attention will soon become ubiquitous. Also visual heat-maps have become very popular and simpler to create in the 2D space over the last few years. They are very compelling and can be effective in summarizing and communicating data. The innovation in our work is the implementation of visual 3D heat-maps of the real world combined with advanced Computer Vision libraries. Finally, we have incorporated the visual 3D heat-maps for rehabilitation purposes that deal with the loss of concentration in children with learning disabilities, or disabled patients to select items of interest for them across a room.

An intelligent Interactive Learning and Adaptation framework for robot-based vocational training

In this paper, we propose an Interactive Learning and Adaptation framework for Human-Robot Interaction in a vocational setting. We show how Interactive Reinforcement Learning (RL) techniques can be applied to such HRI applications in order to promote effective interaction. We present the framework by showing two different use cases in a vocational setting. In the first use case, the robot acts as a trainer, assisting the user while the user is solving the Towers of Hanoi problem. In the second use case, a robot and a human operator collaborate towards solving a synergistic construction or assembly task. We show how RL is used in the proposed framework and discuss its effectiveness in the two different vocational use cases, the Robot Assisted Training and the Human-Robot Collaboration case.

A Dialogue System for Ensuring Safe Rehabilitation

Dialogue Systems DS are intelligent user interfaces, able to provide intuitive and natural interaction with their users, through a variety of modalities. We present, here, a DS whose purpose is to ensure that patients are consistently and correctly performing rehabilitative exercises, in a tele-rehabilitation scenario. More specifically, our DS operates in collaboration with a remote rehabilitation system, where users suffering from injuries, degenerative disorders and others, perform exercises at home under the remote supervision of a therapist. The DS interacts with the users and makes sure that they perform their prescribed exercises correctly and according to the specified, by the therapist, protocol. To this end, various sensors are utilized, such as Microsofts Kinect, the Wi-Patch and others.