PHAROS 2.0—A PHysical Assistant RObot System Improved

Abstract

There are great physical and cognitive benefits for older adults who are engaged in active aging, a process that should involve daily exercise. In our previous work on the PHysical Assistant RObot System (PHAROS), we developed a system that proposed and monitored physical activities. The system used a social robot to analyse, by means of computer vision, the exercise a person was doing. Then, a recommender system analysed the exercise performed and indicated what exercise to perform next. However, the system needed certain improvements. On the one hand, the vision system captured the movement of the person and indicated whether the exercise had been done correctly or not. On the other hand, the recommender system was based purely on a ranking system that did not take into account temporal evolution and preferences. In this work, we propose an evolution of PHAROS, PHAROS 2.0, incorporating improvements in both of the previously mentioned aspects. In the motion capture aspect, we are now able to indicate the degree of completeness of each exercise, identifying the part that has not been done correctly, and a real-time performance correction. In this way, the recommender system receives a greater amount of information and so can more accurately indicate the exercise to be performed. In terms of the recommender system, an algorithm was developed to weigh the performance, temporal evolution and preferences, providing a more accurate recommendation, as well as expanding the recommendation to a batch of exercises, instead of just one.

Socially Assistive Robots for Older Adults and People with Autism: An Overview

“…Another proposal is PHAROS [30,31], a socially assistive robot that monitors and evaluates the daily physical exercise done at the user's home (see Figure 1). For this, machine learning techniques (i.e., a convolutional neural network (CNN) together with a recurrent neural network (RNN)) are used to properly identify and evaluate the exercise performance.…”

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

“…With respect to the integration of action recognition solutions in ARDs, different studies revealed that this capability is fundamental for the interactions with the users [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Most of the approaches need a set of sensors to be installed in the home of the user, e.g., [ 30 , 31 , 32 ].…”

“…Finally, some works offer a real-time action recognition capability using the cameras installed directly within the robotic platforms [ 35 , 36 , 37 , 38 ].…”

Value Sensitive Design to Achieve the UN SDGs with AI: A Case of Elderly Care Robots

“…Physical Assistant Robot System (PHAROS) 2.0, is a socially assistive robot that monitors, evaluates, and advises older adults in performing physical activities and exercises at home (Martinez-Martin et al, 2019 ). This has been developed to help healthcare professionals evaluate the physical activity of elderly patients with illness outside of a hospital setting, and stimulate their overall health status (bodily and cognitively).…”

“…All those tasks are accomplished with the use of machine learning techniques, such as Deep Learning (DL), which lets the system learn data features. Specifically, PHAROS 2.0 uses a combination of recurrent neural networks (RNN) and convolutional neural network (CNN) to identify and evaluate the physical exercises of users (Martinez-Martin et al, 2019 , 2020 ). We examine the design of the PHAROS 2.0 prototype using the framework described above (i.e., Fig.…”