Alzheimer's disease (AD) is a neurodegenerative disorder that can cause a significant impairment in physical and cognitive functions. Gait disturbances are also reported as a symptom of AD. Previous works have used Convolutional Neural Networks (CNNs) to analyze data provided by motion sensors that monitor Alzheimer's patients. However, these works have not explored continual learning algorithms that allow the CNN to configure itself as it receives new data from these sensors. This work proposes a method aimed at enabling CNNs to learn from a continuous stream of data from motion sensors without having full access to previous data. The CNN identifies the stage of AD from the analysis of data provided by motion sensors. The work includes an experimentation with data captured by accelerometers that monitored the activity of 35 Alzheimer's patients for a week in a daycare center. The CNN achieves an accuracy of 86,94%, 86,48% and 84,37% for 2, 3 and 4 experiences respectively. The proposal provides advantages to working with a continuous stream of data so that the CNN are constantly self-configuring without the intervention of a human. The work can be considered as promising and helpful in finding deep learning solutions in medical cases in which patients are constantly monitored.

Dementia represents one of the great problems to be solved in medicine for a society that is becoming increasingly long-lived. One of the main causes of dementia is Alzheimer’s disease, which accounts for 80% of cases. There is currently no cure for this disease, although there are treatments to try to alleviate its effects, which is why detecting Alzheimer’s disease in its early stages is crucial to slow down its evolution and thus help sufferers. One of the symptoms of the disease that manifests in its early stages is apraxia, difficulties in carrying out voluntary movements. In the clinical setting, apraxia is typically assessed by asking the patient to imitate hand gestures that are performed by the examiner. To automate this test, this paper proposes a system that, based on a video of the patient making the gesture, evaluates its execution. This evaluation is done in two steps, first extracting the skeleton of the hands and then using a similarity function to obtain an objective score of the execution of the gesture. The results obtained in an experiment with several patients performing different gestures are shown, showing the effectiveness of the proposed method. The system is intended to serve as a diagnostic tool, enabling medical experts to detect possible mobility impairments in patients that may have signs of Alzheimer’s disease.

Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions’ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human–machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system’s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.

Se presenta la actividad en el ámbito de la Interacción Persona-Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) de la Universidad de Cantabria.