Browsing by Author "Bermúdez i Badia, Sergi"
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- AdaptNow: a revamped look for the web: an online web enhancement tool for the elderlyPublication . Dias, Roberto; Bermúdez i Badia, SergiElderly population will become the largest age group of our society in the next twenty years. Consequently, we need to be able to accommodate technologies to the needs of this population. AdaptNow is a web-based application that allows users to adapt existing webpages and turn them more accessible and user friendly. Users can do so directly from any web browser thanks to AdaptNow's user personalization and automatic adaptation artificial intelligence algorithms. In this paper we present the design and implementation of AdaptNow, a solution that improves navigation on the web for elderly users.
- AI-Rehab: a framework for AI driven neurorehabilitation training - the profiling challengePublication . Fermé, Eduardo; Bermúdez i Badia, Sergi; Sirsat, Manisha; Almeida, YuriOne of the health clinic challenges is rehabilitation therapy cognitive impairment that can happen after brain injury, dementia and in normal cognitive decline due to aging. Current cognitive rehabilitation therapy has been shown to be the most effective way to address this problem. However, a) it is not adaptive for every patient, b) it has a high cost, and c) it is usually implemented in clinical environments. The Task Generator (TG) is a free tool for the generation of cognitive training tasks. However, TG is not designed to adapt and monitor the cognitive progress of the patient. Hence, we propose in the BRaNT project an enhancement of TG with belief revision and machine learning techniques, gamification and remote monitoring capabilities to enable health professionals to provide a long-term personalized cognitive rehabilitation therapy at home. The BRaNT is an interdisciplinary effort that addresses scientific limitations of current practices as well as provides solutions towards the sustainability of health systems and contributes towards the improvement of quality of life of patients. This paper proposes the AI-Rehab framework for the BRaNT, explains profiling challenge in the situation of insufficient data and presents an alternate AI solutions which might be applicable once enough data is available.
- An assistive mobile platform for delivering knowledge of performance feedbackPublication . Neves, Davide; Vourvopoulos, Athanasios; Cameirão, Mónica S.; Bermúdez i Badia, SergiUpper limb motor deficits caused by stroke have a big impact on a person’s daily activities and independence. One strategy for promoting motor relearning consists on the delivery of meaningful feedback during rehabilitative training. In this paper we describe the development and first evaluation of a system that combines a portable arm orthosis device and a mobile application running on a tablet in order to provide knowledge of performance to stroke patients while performing therapy. Here we present preliminary results and discuss the potential of this technology.
- An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercisesPublication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, SergiTechnology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient’s quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.
- An integrative framework for tailoring virtual reality based motor rehabilitation after strokePublication . Cameirão, Mónica S.; Bermúdez i Badia, SergiStroke is a leading cause of life-lasting motor impairments, undermining the quality of life of stroke survivors and their families, and representing a major chal lenge for a world population that is ageing at a dramatic rate. Important technologi cal developments and neuroscientific discoveries have contributed to a better under standing of stroke recovery. Virtual Reality (VR) arises as a powerful tool because it allows merging contributions from engineering, human computer interaction, reha bilitation medicine and neuroscience to propose novel and more effective paradigms for motor rehabilitation. However, despite evidence of the benefits of these novel training paradigms, most of them still rely on the choice of particular technologi cal solutions tailored to specific subsets of patients. Here we present an integrative framework that utilizes concepts of human computer confluence to 1) enable VR neu rorehabilitation through interface technologies, making VR rehabilitation paradigms accessible to wide populations of patients, and 2) create VR training environments that allow the personalization of training to address the individual needs of stroke patients. The use of these features is demonstrated in pilot studies using VR training environments in different configurations: as an online low-cost version, with a myo electric robotic orthosis, and in a neurofeedback paradigm. Finally, we argue about the need of coupling VR approaches and neurocomputational modelling to further study stroke and its recovery process, aiding on the design of optimal rehabilitation programs tailored to the requirements of each user.
- An integrative virtual reality cognitive-motor intervention approach in stroke rehabilitation: a pilot studyPublication . Faria, A. L.; Vourvopoulos, A.; Cameirão, M. S.; Fernandes, J. C.; Bermúdez i Badia, SergiStroke is one of the most common causes of acquired disability, leaving numerous adults with cognitive and motor impairments, and affecting patient’s capability to live independently. In post-stroke it is imperative to initiate a process of intensive rehabilitation and personalized objectives to maximize functional cognitive and motor recovery. Virtual Reality (VR) technology is being widely applied to rehabilitation of stroke, however, not in an integrative manner. Like traditional rehabilitation, these new tools mostly focus either in the cognitive or in the motor domain, which can take to a reduced impact in the performance of activities of daily living, most of them dual-task. Assuming the existence of cognitive and motor recovery interdependence, RehabNet proposes a holistic approach. Here we present a one-month long pilot study with three stroke patients whose training was a game-like VR version of the Toulouse-Piéron cancellation test, adapted to be performed by repetitive arm reaching movements. A standardized motor and cognitive assessment was performed pre and post intervention. The first results on this intervention support a holistic model for rehabilitation of stroke patients, sustaining interdependence on cognitive and motor recovery. Furthermore, we observed that the impact of the integrative VR approach generalizes to the performance of the activities of daily living.
- Applications and Issues for physiological computing systems: an introduction to the special issuePublication . Fairclough, Stephen; Silva, Hugo Plácido da; Gamboa, Hugo; Gilleade, Kiel; Bermúdez i Badia, SergiThe prospect of connecting the brain and body to a technological device can elicit a broad range of responses from potential users. Early adopters are thrilled by the possibility of a device that can interface directly to the human nervous system. For the vast majority, interest is tempered by caution, as nascent varieties of physiological computing systems raise as many questions as answers about how we will interact with computers in the future.
- Augmented Human Assistance (AHA)Publication . Bermúdez i Badia, Sergi; Odekerken-Schröder, Gaby; Mahr, Dominik; Čaić, Martina; Lee, Min Hun; Siewiorek, Dan; Smailagic, Assim; Gamboa, Hugo; Belo, David; Carnide, Maria Filomena Araújo da Costa Cruz; Baptista, Maria de Fátima Marcelina; Simão, Hugo; Avelino, João; Sousa, Honorato; Paulino, Teresa; Gonçalves, Afonso; Cardona, John Muñoz; Cameirão, Mónica S.; Bernardino, Alexandre; Gouveia, Élvio RúbioAging and sedentarism are two main challenges for social and health systems in modern societies. To face these challenges a new generation of ICT based solutions is being developed to promote active aging, prevent sedentarism and find new tools to support the large populations of patients that suffer chronic conditions as result of aging. Such solutions have the potential to transform healthcare by optimizing resource allocation, reducing costs, improving diagno ses and enabling novel therapies, thus increasing quality of life. The primary goal of the “AHA: Augmented Human Assistance” project is to de velop novel assistive technologies to promote exercise among the elderly and patients of motor disabilities. For exercise programs to be effective, it is essential that users and patients comply with the prescribed schedule and perform the ex ercises following established protocols. Until now this has been achieved by hu man monitoring in rehabilitation and therapy session, where the clinicians or therapists permanently accompany users or patient. In many cases, exercises are prescribed for home performance, in which case it is not possible to validate their execution. In this context, the AHA project is an integrative and cross-discipli nary approach of 4 Portuguese universities, the CMU, and 2 Portuguese industry partners, that combines innovation and fundamental research in the areas of hu man-computer interaction, robotics, serious games and physiological computing (see partner list in Appendix A). In the project, we capitalize on recent innova tions and aim at enriching the capabilities and range of application of assistive devices via the combination of (1) assistive robotics; (2) technologies that use well-understood motivational techniques to induce people to do their exercises in the first place, and to do them correctly and completely; (3) tailored and relevant guidance in regard to health care and social support and activities; and (4) tech nologies to self-monitoring and sharing of progress with health-care provider enabling clinicians to fine-tune the exercise regimen to suit the participant’s ac tual progress. We highlight the development of a set of exergames (serious games controlled by the movement of the user’s body limbs) specifically designed for the needs of the target population according to best practices in sports and human kinetics sciences. The games can be adapted to the limitations of the users (e.g. to play in a sitting position) so a large fraction of the population can benefit from them. The games can be executed with biofeedback provided from wearable sensors, to pro duce more controlled exercise benefits. The games can be played in multi-user settings, either in cooperative or competitive mode, to promote the social rela tions among players. The games contain regional motives to trigger memories from the past and other gamification techniques that keep the users involved in the exercise program. The games are projected in the environment through aug mented reality techniques that create a more immersive and engaging experience than conventional displays. Virtual coach techniques are able to monitor the cor rectness of the exercise and provide immediate guidance to the user, as well as providing reports for therapists. A socially assistive robot can play the role of the coach and provide an additional socio-cognitive dimension to the experience to complement the role of the therapist. A web service that records the users’ per formances and allows the authorized therapists to access and configure the exer cise program provides a valuable management tool for caregivers and clinical staff. It can also provide a social network for players, increasing adherence to the therapies. We have performed several end-user studies that validate the proposed ap proaches. Together, or in isolation, these solutions provide users, caregivers, health professionals and institutions, valuable tools for health promotion, disease monitoring and prevention.
- Automatic cognitive fatigue detection using wearable fNIRS and machine learningPublication . Varandas, Rui; Lima, Rodrigo; Bermúdez i Badia, Sergi; Silva, Hugo; Gamboa, HugoWearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtru sively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.
- Benefits of virtual reality based cognitive rehabilitation through simulated activities of daily living: a randomized controlled trial with stroke patientsPublication . Faria, Ana Lúcia; Andrade, Andreia; Soares, Luísa; Bermúdez i Badia, SergiBackground: Stroke is one of the most common causes of acquired disability, leaving numerous adults with cognitive and motor impairments, and affecting patients’ capability to live independently. There is substancial evidence on post-stroke cognitive rehabilitation benefits, but its implementation is generally limited by the use of paper-and-pencil methods, insufficient personalization, and suboptimal intensity. Virtual reality tools have shown potential for improving cognitive rehabilitation by supporting carefully personalized, ecologically valid tasks through accessible technologies. Notwithstanding important progress in VR-based cognitive rehabilitation systems, specially with Activities of Daily Living (ADL’s) simulations, there is still a need of more clinical trials for its validation. In this work we present a one-month randomized controlled trial with 18 stroke in and outpatients from two rehabilitation units: 9 performing a VR-based intervention and 9 performing conventional rehabilitation. Methods: The VR-based intervention involved a virtual simulation of a city – Reh@City - where memory, attention, visuo-spatial abilities and executive functions tasks are integrated in the performance of several daily routines. The intervention had levels of difficulty progression through a method of fading cues. There was a pre and postintervention assessment in both groups with the Addenbrooke Cognitive Examination (primary outcome) and the Trail Making Test A and B, Picture Arrangement from WAIS III and Stroke Impact Scale 3.0 (secondary outcomes). Results: A within groups analysis revealed significant improvements in global cognitive functioning, attention, memory, visuo-spatial abilities, executive functions, emotion and overall recovery in the VR group. The control group only improved in self-reported memory and social participation. A between groups analysis, showed significantly greater improvements in global cognitive functioning, attention and executive functions when comparing VR to conventional therapy. Conclusions: Our results suggest that cognitive rehabilitation through the Reh@City, an ecologically valid VR system for the training of ADL’s, has more impact than conventional methods. Trial registration: This trial was not registered because it is a small sample study that evaluates the clinical validity of a prototype virtual reality system.