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.

Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.

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.

Abstract-This paper presents the application developed for humanoid robots which are used in therapy of dementia patients, as a cognitive stimulation tool. It has been created using BICA, a component oriented framework for programming robot applications, which is also described. The developed robotherapy application includes the control software onboard the robot and some tools like the visual script generator or several monitoring tools to supervise the robot behavior along the sessions. The behavior of the robot along the therapy sessions is visually programmed in a session script that allows music playing, physical movements (dancing, exercises...), speech synthesis and interaction with the human monitor. The monitoring tools allow the therapist interaction with the robot through its buttons, a tablet or a Wiimote device. Experiments with real dementia patients have been performed in collaboration with a research center in neurological diseases. Initial results show a slight (or mild) improvement in neuropsychiatric symptoms over other traditional therapy methods.

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