Rehabibotics: Using Humanoid Robots to Convey Rehabilitation Therapies to Disabled People

Rehabibotics: Using Humanoid Robots to Convey Rehabilitation Therapies to Disabled People

 

Rehabibotics is a project conducted by Instituto de Robótica para la Dependecia that aims to develop a sistem capable of creating rehabilitation therapies for disabled individuals, through games, dances or other psychomotor activities that makes use of humanoid robots. It is proved that the use of humanoid robots in psychomotor therapies helps to ensure their effectiveness. However, using this kind of robots is still very difficult for non-specialised people. The main objective of this research project is to implement a new system that could allow the automatic generation of thses terapheutic routines to the assisting personeel and also to objectivelly measure its application and effectiveness. In fact, the project consists in three objectives:

1.- Elaborate an automatic system able to create therapies, either cognitives and/or motor, through, for example, capturing the motion of different humans and reproducing them in humanoids robots.

2.- Analyse the emotions the patients show during their interactuation with the robots or with other therapeutic games they could be playing with in order to determine the grade of acceptance and performance of the therapies, all analysed in a controlled, assisted environment.

3.- Analyse te evolution of the patients in whom the therapies with robots are applied in order to objectivelly determine their effectiveness.

REEM-and-JAINENDRA

From a scientific and technological poi t of view,  the more specific objectives to reach are:

(a) Visually capture a person’s behaviour using RGB-D cameras (like Microsoft Kinect http://www.microsoft.com/en-us/kinectforwindows/).
(b) Obtention of human activity models and development of effective methods to learn from these models
(c) Translation of human activity models to a robotic architecture (for example to the humanoid robot Nao d’Aldebaran Robotics http://www.aldebaran.com/en).
(d) Interpretation of the visual information to later interpretate the expressions form the patients, with spetial intrest on the facialnexpressions, but also in other corporal movements.
(e) Interactuation on the robot’s behaviour based on the analysis of the emotions the patients show during the therapies
(f) Development of measuring methodologies for the effectiveness of the therapies on disabled patients in order to evaluate the impact of the robotic therapy.
This research, in which our member Jainendra Shukla is working at, is performed in collaboration of other different entities and conducted by the Institut Català de la Robòtica per a la Discapacitat (http://www.institutcatalarobotica.org/). The Intelligent Robotics and Computer Vision group will perform the research and also several experimental test in different scnenarios and patients.

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3D Facial Recostruction and printing for radiotherapy application

3D Facial Recostruction and printing for radiotherapy application

This project, supported by the Assossiació Oncològica Amadeu Pelegrí and with the collaboration of the Hospital Sant Joan de Reus, aims to develop a new methodology for designing individualised tissue equivalent plastic facial models for further application in radiotherapy treatments currently performed at local hospitals.

tn the original clinical procedure, the staff at the hospital were required to construct a negative plaster cast of the patitent face after the first intake visit. This negative was later used as a model to obtain a positive plaster of the face, in which a wax bolus was created after the corresponding ongolotit analysis. Then, a silicione rubber model was created and, at the end, with the bolus in place and the tumour border marked,  the treatment planning was performed.

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This project’s objective is to find a new methodology that lightens the amount of work and materials used in this procedure by obtaining a plastic cast via 3D printing. One of the main advantages of this new approach is the expected method of obtaining she initial data, which will be done by performing a 3D scan of the patient. This 3D scan, which is completely harmless, will not require the utilization of a negative cast, as well as it will ease stress on the patient, reducing the disconfort this treatment usually have.

The final results for this project are expected to be shown by the end of 2016, for a later implementation in radiology treatments at mid 2017.

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IA-BIOBREAST: Pixel-level analysis of texture features and fusion of multimodal information

Research Project TIN2012-37171-C02-02 (2013-2015)

Analisis de Caracteristicas Texturales a Nivel de Pixel y Fusion de Informacion Multimodal

Detection based on screening programs in high-risk populations is a major asset in the struggle against Breast Cancer. Currently, screening programs in most developed countries focus in analysing digital mammography images. Computer Aided Diagnosis (CAD) systems provide decisive help in this task. Nevertheless, latest tendencies imply that not all women should follow the same screening protocol (i.e. systematic mammography every two years), but they should be stratified according to several criteria. This criteria stand mainly for cancer risk biomarkers such as breast density or previous lesion evolution. The capacity to stratify patients improves diagnostic efficiency by using different medical imaging modalities. Specifically, personalised screening programs make use of the image modality that provide the better information for each patient type.

biobreast

In order for these biomarkers to reach their full potential in clinical practice, their implementation should allow for fully autamatic computation in large patient populations. Additionally, they should also allow the evaluation and interpretation of the specific values for every patient. Finally, they should make the most of all available image modalities. Computer Vision techniques already present via diagnostic equipment represent a very impostant improvement in everyday clinical practice. Nevertheless, further improvement is still needed and the implementation of new algorithms will make a difference in the coming years. This demand, arises both from epidemiologists and radiologists and satisfying it will play a key role in the achievement of personalised screening programs. Automatic generation of Biomarkers is the next major milestone in this direction.

The IA-BioBreast project aims at researching image analysis methods that focus on the development of two specific biomarkers: breast density and temporal evolution of existing lesions. In order to achieve this goal, new microtexture-specific techniques will be developed using algorithms for feature extraction, selection and clossification. On the other hand, image registration algorithms will be researched for two main applications: combining images of different modalities (breast X-ray mammography, MRI and UltraSound) and registering temporal studies within the same image modality. Finally, automatic lesion detection algorithms will also be developed by using image segmentation techniques. The adequacy of the project results to clinical practice will be analysed by using a CAD (Computer Aided Diagnosis) system able to process the results from all the techniques developed. By including all these aspects, the project focuses in using computer vision techniques and developing novel algorithms for segmentation, feature extraction and selection, classification and registration.

The project will benefit from the involvement of several health centres as well as the interest shown by CAD developing companies. Not only these partners will provide data during the development of the project but they will also take an active role in it. Moreover, they will also make it possible to evaluate the methods at a higher, clinical level once they reach completion stages.

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