Collaborative Computer-Assisted Cognitive Rehabilitation System

Oliver, Miguel; Molina, José Pascual; Fernández-Caballero, Antonio; González, Pascual

doi:10.14201/adcaij2017635774

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…In this work, we have presented a new version of our previous cognitive rehabilitation system [ 17 , 18 ] for enabling at-home rehabilitation (telerehabilitation). This new proposal has focused on the autonomy of the therapist and the patient, enabling them to carry out the rehabilitation tasks whenever and wherever they want.…”

Section: Discussionmentioning

confidence: 99%

“…This system is an extension of the one presented in previous works such as [ 17 , 18 ]. In the first one [ 17 ], we included some relevant social features to improve motivation and reduce the feeling of social isolation. This proposal provides the opportunity to design multiuser therapies offering a more entertaining environment.…”

Section: Ambient Intelligence Environment For Cognitive Telerehabimentioning

confidence: 99%

“…The exercise is divided in several steps, with the interface showing the second one. This interface, as well as the method for creating rehabilitation exercises, has been explained in our previous works [ 17 , 18 ]. As a new part of the interface, the section to manage the difficulty levels is shown.…”

Section: Ambient Intelligence Environment For Cognitive Telerehabimentioning

confidence: 99%

“…Besides the sensors included in our proposal, the use of haptic (vibrotactile) actuators [ 16 ] provides support to the patient when carrying out some exercise, offering a multi-sensory experience, since it complements the visual and aural channels with other stimuli. Our proposal, which is based on our previous works [ 17 , 18 ], introduces as a new feature the use of stress sensors, haptic actuators and the inclusion of a FIS for controlling the exercise execution, which are used to replace, somehow, the therapist supervision and support when the patient carries out the exercises. This gives more autonomy to the therapist who can design exercises, monitor patients, and diagnose errors in the execution of exercises, from anywhere at any time.…”

Section: Introductionmentioning

confidence: 99%

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Ambient Intelligence Environment for Home Cognitive Telerehabilitation

Oliver

Teruel

Molina

et al. 2018

Sensors

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Higher life expectancy is increasing the number of age-related cognitive impairment cases. It is also relevant, as some authors claim, that physical exercise may be considered as an adjunctive therapy to improve cognition and memory after strokes. Thus, the integration of physical and cognitive therapies could offer potential benefits. In addition, in general these therapies are usually considered boring, so it is important to include some features that improve the motivation of patients. As a result, computer-assisted cognitive rehabilitation systems and serious games for health are more and more present. In order to achieve a continuous, efficient and sustainable rehabilitation of patients, they will have to be carried out as part of the rehabilitation in their own home. However, current home systems lack the therapist’s presence, and this leads to two major challenges for such systems. First, they need sensors and actuators that compensate for the absence of the therapist’s eyes and hands. Second, the system needs to capture and apply the therapist’s expertise. With this aim, and based on our previous proposals, we propose an ambient intelligence environment for cognitive rehabilitation at home, combining physical and cognitive activities, by implementing a Fuzzy Inference System (FIS) that gathers, as far as possible, the knowledge of a rehabilitation expert. Moreover, smart sensors and actuators will attempt to make up for the absence of the therapist. Furthermore, the proposed system will feature a remote monitoring tool, so that the therapist can supervise the patients’ exercises. Finally, an evaluation will be presented where experts in the rehabilitation field showed their satisfaction with the proposed system.

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Section: Discussionmentioning

confidence: 99%

Section: Ambient Intelligence Environment For Cognitive Telerehabimentioning

confidence: 99%

Section: Ambient Intelligence Environment For Cognitive Telerehabimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ambient Intelligence Environment for Home Cognitive Telerehabilitation

Oliver

Teruel

Molina

et al. 2018

Sensors

Self Cite

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“…The feasibility was validated, and factors such as positive reinforcement, training, and research related to human interaction were identified as necessary for developing these types of systems. Another article proposed a model for assistive technology, joining physical and cognitive rehabilitation [ 8 ]. This approach is interesting in merging both approaches and prescribing rehabilitation, giving therapists opportunities to customize the best rehabilitation exercise for their patients.…”

Section: Introductionmentioning

confidence: 99%

A Model for Cognitive Personalization of Microtask Design

Paulino

Guimarães

Correia

et al. 2023

Sensors

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The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker’s cognitive profile. There are two common methods for assessing a crowd worker’s cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model’s performance.

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