Loading...
4 results
Search Results
Now showing 1 - 4 of 4
- 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.
- A dataset for the automatic assessment of functional senior fitness tests using kinect and physiological sensorsPublication . Bernardino, Alexandre; Vismara, Christian; Bermúdez i Badia, Sergi; Gouveia, Élvio; Baptista, Fátima; Carnide, Filomena; Oom, Simão; Gamboa, HugoThis work presents a dataset of functional fitness tests acquired with Kinect v2 and physiological sensors. The dataset contains both young and senior subjects executing a number of fitness tests meeting scientific standards of reliability and validity. The main objective is the ability to assess lower body strength, endurance, gait speed, agility and balance from the data obtained from commercially accessible devices. The dataset can be used to develop algorithms to automate the assessment of fitness levels in low-cost computer based systems for use at home, gymnasiums or care centers.
- 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.
- 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.