Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities

Abstract

The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought.

A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives

“…One of the HIMTAE’s main features is that it analyses physiological and psychological data to estimate the subject’s mood, with the help of AI [ 44 ]. Although it is true that good results have been achieved in this field, many of the tests have been performed in laboratory environments [ 45 , 46 ], i.e., the entire process has been under constant surveillance.…”

“…We also set an overlap of 10% between consecutive windows to reduce the boundary effect when signals are filtered. The process of feature extraction from the various sensors included in the Empatica e4 wristband is detailed in [ 44 ]. Features in both time and frequency domains are calculated for each component.…”

“…These will be part of the dataset necessary to train the machine-learning algorithms to predict the user’s mood. In particular, several tables in [ 44 ] show 72 features from the 3-axis accelerometer (minimal and maximal values, standard deviations, etc. ), 13 features of peripheral skin temperature (means of absolute values, mean value of power spectral density, etc.…”

“…Taking the experiments described in [ 44 ] as a starting point, as were taken into account in [ 42 ], an Android application was designed to capture psychologically relevant data ( Figure 3 ). The system uses 2 tools, the first of which consists of EMAs (ecological momentary assessments) based on the mental/mood state model proposed by JA Russell [ 46 ], such that the user is asked 5 times a day about their levels of happiness and activity, dividing these parameters into 5 different discrete levels ranging from 0–4.…”

“…Frequency analysis was carried out to contrast the correlation between the arousal (or activeness) and pleasure (or happiness) dimensions with the anxiety levels registered ( Table 1 ). The results concluded that correlations between affective dimensions and anxiety states were not statistically significant [ 44 ].…”