Heartbeat detection by Laser Doppler Vibrometry and Machine Learning

none6openAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, EmanueleAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, Emanuel

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 ofhealth coaching strategies for older people is still limited. To address these gaps, recent efforts havebeen made in the ICT sector in order to develop systems for delivering coaching and overcomingbarriers relating to scarcity of resources. The aim of this review is to examine the efficacy of personalhealth coaching systems for older adults using digital virtual agents.Methods: A systematic reviewof the literature was conducted in December 2019 analyzing manuscripts from four databases overthe last 10 years. Nine papers were included.Results: Despite the low number of studies, there wasevidence 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 ofbehavior change produced by short-term interventions.Conclusion: These systems offer a potentialtool to reduce costs, minimize therapist burden and training, and expand the range of clients who canbenefit from them. It is desirable that in the future the number of studies will grow, considering otheraspects such as the role of the virtual coaches’ characteristics, social-presence, empathy, usability,and health literac

LOCALIZATION OF OLDER PEOPLE IN AN INDOOR SCENARIO: A MEASUREMENT SYSTEM BASED ON PIR SENSORS INSTALLED ON A SOCIAL ROBOT

The indoor localization of older people (> 65 years old) can address the challenges for the creation of personalized Ambient and Assisted Living (AAL) experiences for a varied aging population in the smart living spaces, such as Smart Homes [1]. Methods based on sensors network, such as RGB camera and microphones, were widely used for older people localization in indoor environment [2]. However, in the last years the increased privacy concerns with these sensors have led researchers to move towards new technologies. Thus, systems based on wearable devices like smartwatches and mobile phones have gradually been involved in localizing older people in indoor environments [3]. Anyway, pitfalls related to reliability, validity, and accuracy of data need to be addressed for wider acceptance and adoption of wearables [3]. Passive infrared (PIR) motion sensors are typical house sensors for the detection of people. Frequently, these sensors are mounted on the ceiling of a room allowing a room-based localization system [4]. The objective of this work is to introduce in the indoor environment a non-invasive and inexpensive system to localize people in every room of an apartment [5]. The system is composed of PIR motion sensors mounted on the head of a mobile social robot (Misty II). To predict the direction information related to people’s movements a measurement procedure is developed, in which a Decision Tree (DT) classifier algorithm is tested

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

Metrological characterization and signal processing of a wearable sensor for the measurement of heart rate variability

This paper presents a methodology for the processing of the Photoplethysmography (PPG) signal measured using a smartwatch during motion tests. For statistical validation, signals from 15 healthy subjects have been collected while the subjects are walking on a treadmill. The motion artifacts (MAs) of the PPG signal have been removed demonstrating that the 37% of the signals are affected by MAs. Then, the experimental performance assessment of the PPG signal, from which the heart rate variability (HRV) has been extracted, by measuring the RR intervals, is compared to the RR intervals extracted from ECG signals measured using a multi-parametric chest belt that is considered as a reference sensor. The uncertainty of the PPG sensor in the measurement of the RR intervals is ± 169 ms, (with a coverage factor k = 2) if compared to the reference method, which in percentage is 30%

Characterization of porosity and defects on composite materials using X-ray computed tomography and image processing

This paper deals with the development of a measurement procedure to characterize anomalies, i.e. voids and defects, in four composite material (CM) samples. For this aim, four CM samples, each of them characterized by specific manufacturing techniques, have been analyzed. The first one (CM1) has Teflon defects, the second one (CM2) has undergone a low-degree manufacturing process and thus judged too porous at quality control, the third one (CM3) has passed the interlaminar shear strength (ILSS) test and so is expected to have a low-level of anomalies, unlike the fourth one (CM4), which has failed at ILSS test. An industrial X-ray computed tomography (CT) has been used to scan the CM samples and a specific image processing technique has been developed to measure the number and dimension of anomalies within them. The calculated amount of anomalies seems to be within the acceptable range identified in literature, always below 5%, showing the goodness of manufacturing process, and furthermore a threshold level of 0.09 mm has been statistically calculated to discriminate between voids and the other kinds of defects