Aims: Testing the effect of the experimental robot-based therapeutic sessions for patients with dementia in: a controlled study of parallel groups of nursing home patients comparing the effects of therapy sessions utilizing a humanoid robot (NAO), an animal-shaped robot (PARO), or a trained dog (DOG), with conventional therapy (CONTROL) on symptoms of dementia; and an experience for patients who attend a day care center, comparing symptom prevalence and severity before and after sessions utilizing NAO and PARO. Methods: In the nursing home, patients were randomly assigned by blocks, based on dementia severity, to one of the 3 therapeutic groups 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/week for 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) imp

Humanoids have increasingly become the focus of attention in robotics research in recent years, especially in service and personal assistance robotics. This paper presents the application developed for humanoid robots in the therapy of dementia patients as a cognitive stimulation tool. The behaviour of the robot during the therapy sessions is visually programmed in a session script that allows music to play, physical movements (dancing, exercises, etc.), speech synthesis and interaction with the human monitor. The application includes the control software on board the robot and some tools like the visual script generator or a monitor to supervise the robot behaviour during the sessions. The robot application's impact on the patient's health has been studied. Experiments with real patients have been performed in collaboration with a centre of research in neurodegenerative diseases. Initial results show a slight or mild improvement in neuropsychiatric symptoms over other traditional therapy methods.

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.

Robots have begun to populate the everyday environments of human beings. These social robots must perform their tasks without disturbing the people with whom they share their environment. This paper proposes a navigation algorithm for robots that is acceptable to people. Robots will detect the personal areas of humans, to carry out their tasks, generating navigation routes that have less impact on human activities. The main novelty of this work is that the robot will perceive the moods of people to adjust the size of proxemic areas. This work will contribute to making the presence of robots in human-populated environments more acceptable. As a result, we have integrated this approach into a cognitive architecture designed to perform tasks in human-populated environments. The paper provides quantitative experimental results in two scenarios: controlled, including social navigation metrics in comparison with a traditional navigation method, and non-controlled, in robotic competitions where different studies of social robotics are measured.