Francisco Martín Rico

Social robots in advanced dementia.

Aims: Pilot studies applying a humanoid robot (NAO), a pet robot (PARO) and a real animal (DOG) in therapy sessions of patients with dementia in a nursing home and a day care center. Methods:In the nursing home, patients were assigned by living units, based on dementia severity, to one of the three parallel therapeutic arms to compare: CONTROL, PARO and NAO (Phase 1) and CONTROL, PARO, and DOG (Phase 2). In the day care center, all patients received therapy with NAO (Phase 1) and PARO (Phase 2). Therapy sessions were held 2 days per week during 3 months. Evaluation, at baseline and follow-up, was carried out by blind raters using: the Global Deterioration Scale (GDS), the Severe Mini Mental State Examination (sMMSE), the Mini Mental State Examination (MMSE), the Neuropsychiatric Inventory (NPI), the Apathy Scale for Institutionalized Patients with Dementia Nursing Home version (APADEM-NH), the Apathy Inventory (AI) and the Quality of Life Scale (QUALID). Statistical analysis included descriptive statistics and non-parametric tests performed by a blinded investigator. Results: In the nursing home, 101 patients (Phase 1) and 110 patients (Phase 2) were included. There were no significant differences at baseline. The relevant changes at follow-up were: (Phase 1) patients in the robot groups showed an improvement in apathy; patients in NAO group showed a decline in cognition as measured by the MMSE scores, but not the sMMSE; the robot groups showed no significant changes between them; (Phase 2) QUALID scores increased in the PARO group. In the day care center, 20 patients (Phase 1) and 17 patients (Phase 2) were included. The main findings were: (Phase 1) improvement in the NPI irritability and the NPI total score; (Phase 2) no differences were observed at follow-up.

MYRABot+: A feasible robotic system for interaction challenges

This paper describes the development of a low cost robotic platform named MYRABot+, which is able to make the required tasks in order to assist a human in a domestic environment. We also believe that the best way to measure its performance and validate its behavior is taking part in a robotic challenge. This robotic platform explicitly uses low cost components, making it an accessible platform in monetary terms. In addition, we have designed a component-oriented software architecture that allows an easy implementation of simple HRI tasks. We demonstrate the feasibility of this proposal by taking part in the competition RoCKIn@home. There we show the basic abilities needed to work in a house environment, such as selflocalization, navigation, human dialog, and object manipulation. Our goal is to be able to evaluate and to compare it with other robotic platforms.

Deep Learning and Bayesian Networks for Labelling User Activity Context Through Acoustic Signals

Context awareness in autonomous robots is usually performed combining localization information, objects identification, human interaction and time of the day. We think that gathering environmental sounds we can improve context recognition. With that purpose, we have designed, developed and tested an Environment Recognition Component (ERC) that provides an extra input to our Context-Awareness Component (CAC) and increases the rate of labeling correctly users’ activities. First element, the Environment Recognition Component (ERC) uses convolutional neural networks to classify acoustic signals and providing information to the Context-Awareness Component (CAC) which infers the user activity using a hierarchical Bayesian network. The work described in this paper evaluates the results of the labeling process in two HRI scenarios: robot and user sharing room and robot, and when the human and the robot are in different rooms. The results showed better accuracy when the ERC uses acoustic signals.

Analysis and Evaluation of a Low-Cost Robotic Arm for @Home Competitions

This paper reviews the design design, construction and performance of an affordable robotic arm of four degrees of freedom based on an Arduino controller in a home-like environment. This paper describes the kinematic design of our 4 DOF arm and the physical restrictions that this design imposes. We have also proposed two types of end-effectors to address two types of manipulation tasks: to grasp objects and to push different light switches. The arm was on board of the MYRABot platform and both were evaluated in the RoCKIn competition. This competition involves grasping and manipulation tasks that are described in the paper as well. Comments on the results of the competition and their implication in further improvement of the robot are also described in the paper.