Session 1-3:

Technology, Robotics and Healthcare

Date: Friday 2nd October 2020. 10:30-12:00

Scientific chair:

Zoom room: 

Date: Thursday 1st October 2020. 14:00-15:30

Scientific chair:

Zoom room: 

Physical room:

10:30-10:50: Sensor Based Wearable Assistive Device for visually Impaired People. SP. Rajamohana, Zbigniew M. Wawrzyniak, E.G Radhika, S. Priya, S. Madhumitaa, and S. Surabhi.
Abstract: In this paper, we present a sensor based wearable assistive gadget to help visually impaired individuals stroll without anyone else through the roads, explore in open places, and look for help. The principle segments of the framework are a microcontroller board, ultrasonic sensors, flame sensor, GSM and GPS modules. The proposed framework utilizes a lot of sensors to follow the way and alert the user of impediments before them. The user is alerted by vibrations on different fingers based on the direction of the obstacle, which is useful notwithstanding when the user has hearing misfortune and in the noisy environment alongside visual impairment. In addition, the flame sensor is utilized to distinguish the fire in the environment and caution the visually impaired individual through vibration. When the emergency button is pressed the system alerts the blind individual’s concerned persons with the system location that is sent as a message. We tried the prototype model and confirmed its usefulness and viability. The proposed framework has a larger number of highlights than other comparative systems. We anticipate that it should be a valuable device to improve the personal satisfaction of Visually Impaired People.
11:00-11:20: H'ability, Virtual reality at the service of the handicap. Bérengère HENRION.
Abstract: H'ability is the interactive virtual reality solution for people suffering from hemiplegia (paralysis of half of the body). Our objective is to make everyday rehabilitation sessions in health institutions or at home with remote medical monitoring attractive and entertaining.
11:30-11:50: Combined computational and driven-modelling modelling of blood coagulation to predict the response of individual patients to anticoagulant therapy. Anass Bouchnita
Abstract: Venous thrombosis is a dangerous medical condition characterized by the obstruction of blood flow in a vessel due to the formation of a large thrombus. The resulting thrombus can also detach and migrate with the flow resulting in pulmonary embolism. Anticoagulant drugs, such as warfarin, heparin, dabigatran, and rivaroxaban, are administered to reduce the coagulability of blood and prevent the development of venous thrombosis. However, excessive dosing of anticoagulants increases the risk of recurrent bleedings. To predict the response of venous thrombosis patients to anticoagulants, we develop a computational model of thrombus formation under flow which considers the characteristics of individual patients. The action of anticoagulant drugs is introduced using a pharmacokinetics-pharmacodynamics (PK-PD) model. Then, we generate a population of virtual patients and evaluate their response to different anticoagulant treatment protocols using parallel simulations of the developed model. We analyse the obtained dataset and use it to train a Machine-Learning algorithm that accurately and instantaneously predicts the response of patients to anticoagulant therapy.