Empowering Smart Aging: Insights into the Technical Architecture of the e-VITA Virtual Coaching System for Older Adults.

With a substantial rise in life expectancy throughout the last century, society faces the imperative of seeking inventive approaches to foster active aging and provide adequate aging care. The e-VITA initiative, jointly funded by the European Union and Japan, centers on an advanced virtual coaching methodology designed to target essential aspects of promoting active and healthy aging. This paper describes the technical framework underlying the e-VITA virtual coaching system platform and presents preliminary feedback on its use. At its core is the e-VITA Manager, a pivotal component responsible for harmonizing the seamless integration of various specialized devices and modules. These modules include the Dialogue Manager, Data Fusion, and Emotional Detection, each making distinct contributions to enhance the platform's functionalities. The platform's design incorporates a multitude of devices and software components from Europe and Japan, each built upon diverse technologies and standards. This versatile platform facilitates communication and seamless integration among smart devices such as sensors and robots while efficiently managing data to provide comprehensive coaching functionalities.

A Multi-Sensor Fusion Approach Based on PIR and Ultrasonic Sensors Installed on a Robot to Localise People in Indoor Environments.

This work illustrates an innovative localisation sensor network that uses multiple PIR and ultrasonic sensors installed on a mobile social robot to localise occupants in indoor environments. The system presented aims to measure movement direction and distance to reconstruct the movement of a person in an indoor environment by using sensor activation strategies and data processing techniques. The data collected are then analysed using both a supervised (Decision Tree) and an unsupervised (K-Means) machine learning algorithm to extract the direction and distance of occupant movement from the measurement system, respectively. Tests in a controlled environment have been conducted to assess the accuracy of the methodology when multiple PIR and ultrasonic sensor systems are used. In addition, a qualitative evaluation of the system's ability to reconstruct the movement of the occupant has been performed. The system proposed can reconstruct the direction of an occupant with an accuracy of 70.7% and uncertainty in distance measurement of 6.7%.

Usability and Feasibility Assessment of a Social Assistive Robot for the Older People: Results from the GUARDIAN Project.

In Italy, many people aged over 65 cannot live independently, causing an overall decrease in their quality of life and a need for social and health care. Due to the lack of both formal and informal caregivers, technological solutions become of paramount importance in this scenario. This article describes the user-centered development of the GUARDIAN ecosystem, consisting of a social robot integrated with two mobile applications which aim to monitor, coach, and keep the older user company in order to prolong his/her independence at home. In particular, the advancements from the alpha to the beta prototype of the ecosystem are described, achieved through the feedback collected from 41 end users-older people and their carers-that have tested the system for 6 weeks. By enhancing human-robot interaction, a positive improvement in terms of usability and acceptability of the system was retrieved. However, to increase the perceived usefulness and the impact on older users' lives, it is necessary to make the entire system more customizable, and more capable in providing support for daily activities.

A systematic review of multidomain and lifestyle interventions to support the intrinsic capacity of the older population.

Introduction: The focus on intrinsic capacity (IC) could help clinicians to design interventions to improve the health of the older population. This review aims to map the current state of the art in the field of multi-domain interventions based on the IC framework, to allow health professionals in identifying personalized clinical interventions, oriented to empower the older people with a holistic and positive approach. Methods: A systematic review of the literature was conducted in July 2021 analyzing manuscripts and articles of the last 10.5 years from PubMed, Scopus, Embase, Google Scholar and Elsevier databases. A total of 12 papers were included. Results: The majority of successful interventions are based on a goal setting approach where the older people are involved in the definition of the strategy to follow to remain active and independent. None of the study have used the IC as a framework to design a clinical intervention. Conclusion: To the best of our knowledge, no other reviews are reported in the literature regarding the IC. Our study offers several research directions, which may take the existing debates to the next level.

The Problem of Monitoring Activities of Older People in Multi-Resident Scenarios: An Innovative and Non-Invasive Measurement System Based on Wearables and PIR Sensors.

This paper presents an innovative multi-resident activity detection sensor network that uses the Bluetooth Low Energy (BLE) signal emitted by tags worn by residents and passive infrared (PIR) motion sensors deployed in the house to locate residents and monitor their activities. This measurement system solves the problem of monitoring older people and measuring their activities in multi-resident scenarios. Metrics are defined to analyze and interpret the collected data to understand daily habits and measure the activity level (AL) of older people. The accuracy of the system in detecting movements and discriminating residents is measured. As the sensor-to-person distance increases, the system decreases its ability to detect small movements, while still being able to detect large ones. The accuracy in discriminating the identity of residents can be improved by up to 96% using the Decision Tree (DT) classifier. The effectiveness of the measurement system is demonstrated in a real multi-resident scenario where two older people are monitored during their daily life. The collected data are processed, obtaining the AL and habits of the older people to assess their behavior.

Development of a measurement setup to detect the level of physical activity and social distancing of ageing people in a social garden during COVID-19 pandemic.

This study defines a methodology to measure physical activity (PA) in ageing people working in a social garden while maintaining social distancing (SD) during COVID-19 pandemic. A real-time location system (RTLS) with embedded inertial measurement unit (IMU) sensors is used for measuring PA and SD. The position of each person is tracked to assess their SD, finding that the RTLS/IMU can measure the time in which interpersonal distance is not kept with a maximum uncertainty of 1.54 min, which compared to the 15-min. limit suggested to reduce risk of transmission at less than 1.5 m, proves the feasibility of the measurement. The data collected by the accelerometers of the IMU sensors are filtered using discrete wavelet transform and used to measure the PA in ageing people with an uncertainty-based thresholding method. PA and SD time measurements were demonstrated exploiting the experimental test in a pilot case with real users.

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning.

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis.

Coaching Through Technology: A Systematic Review into Efficacy and Effectiveness for the Ageing Population.

Background: Despite the evidence on the positive role of self-management, the adoption of health coaching strategies for older people is still limited. To address these gaps, recent efforts have been made in the ICT sector in order to develop systems for delivering coaching and overcoming barriers relating to scarcity of resources. The aim of this review is to examine the efficacy of personal health coaching systems for older adults using digital virtual agents. Methods: A systematic review of the literature was conducted in December 2019 analyzing manuscripts from four databases over the last 10 years. Nine papers were included. Results: Despite the low number of studies, there was evidence that technology-integrated interventions can deliver benefits for health over usual care. However, the review raises important questions about how to maintain benefits and permanence of behavior change produced by short-term interventions. Conclusion: These systems offer a potential tool to reduce costs, minimize therapist burden and training, and expand the range of clients who can benefit from them. It is desirable that in the future the number of studies will grow, considering other aspects such as the role of the virtual coaches' characteristics, social-presence, empathy, usability, and health literacy.

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis.

A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters.

A Smart Sensing Architecture for Domestic Monitoring: Methodological Approach and Experimental Validation.

Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident's daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user's behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user's physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home.

Cardiorespiratory interactions: Noncontact assessment using laser Doppler vibrometry.

The application of a noncontact physiological recording technique, based on the method of laser Doppler vibrometry (LDV), is described. The effectiveness of the LDV method as a physiological recording modality lies in the ability to detect very small movements of the skin, associated with internal mechanophysiological activities. The method is validated for a range of cardiovascular variables, extracted from the contour of the carotid pulse waveform as a function of phase of the respiration cycle. Data were obtained from 32 young healthy participants, while resting and breathing spontaneously. Individual beats were assigned to four segments, corresponding with inspiration and expiration peaks and transitional periods. Measures relating to cardiac and vascular dynamics are shown to agree with the pattern of effects seen in the substantial body of literature based on human and animal experiments, and with selected signals recorded simultaneously with conventional sensors. These effects include changes in heart rate, systolic time intervals, and stroke volume. There was also some evidence for vascular adjustments over the respiration cycle. The effectiveness of custom algorithmic approaches for extracting the key signal features was confirmed. The advantages of the LDV method are discussed in terms of the metrological properties and utility in psychophysiological research. Although used here within a suite of conventional sensors and electrodes, the LDV method can be used on a stand-alone, noncontact basis, with no requirement for skin preparation, and can be used in harsh environments including the MR scanner.