Using Wearable Devices in a Healthcare Facility: An Empirical Study with Alzheimer's Patients

Smart Wearables are considered a very promising solution for monitoring and helping people affected by cognitive decline or dementia and, in particular, Alzheimer Disease (AD). Nonetheless, the acceptability and wearability of such devices for AD patients pose certain challenges. To address this, an empirical study has been conducted with a group of patients with mild to moderate AD, wearing wristbands E4 by Empatica for a duration of three months. The experiment has been integrated into the regular healthcare activities, with active involvement from nurses and physicians. The paper reports the feedbacks of the caregivers and discusses wearability and acceptability issues.

A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.

High-Efficiency Multi-Sensor System for Chair Usage Detection

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.

Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study

The most frequent form of dementia is Alzheimer's Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer's disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place.

Alzheimer’s Garden: Understanding Social Behaviors of Patients with Dementia to Improve Their Quality of Life

This paper aims at understanding the social behavior of people with dementia through the use of technology, specifically by analyzing localization data of patients of an Alzheimer’s assisted care home in Italy. The analysis will allow to promote social relations by enhancing the facility’s spaces and activities, with the ultimate objective of improving residents’ quality of life. To assess social wellness and evaluate the effectiveness of the village areas and activities, this work introduces measures of sociability for both residents and places. Our data analysis is based on classical statistical methods and innovative machine learning techniques. First, we analyze the correlation between relational indicators and factors such as the outdoor temperature and the patients’ movements inside the facility. Then, we use statistical and accessibility analyses to determine the spaces residents appreciate the most and those in need of enhancements. We observe that patients’ sociability is strongly related to the considered factors. From our analysis, outdoor areas result less frequented and need spatial redesign to promote accessibility and attendance among patients. The data awareness obtained from our analysis will also be of great help to caregivers, doctors, and psychologists to enhance assisted care home social activities, adjust patient-specific treatments, and deepen the comprehension of the disease.

Wellness Assessment of Alzheimer's Patients in an Instrumented Health-Care Facility

Wellness assessment refers to the evaluation of physical, mental, and social well-being. This work explores the possibility of applying technological tools to assist clinicians and professionals to improve the quality of life of people through continuous monitoring of their wellness. The contribution of this paper is manifold: a coarse-grained localization system is responsible for monitoring and collecting data related to patients, while a novel wellness assessment methodology is proposed to extract quantitative indicators related to the well-being of patients from the collected data. The proposed system has been installed at "Il Paese Ritrovato", an innovative health-care facility for Alzheimer's in Monza, Italy; first satisfactory results have been obtained, and the dataset shows great potential for several applications.

Quantitative Indicators for Behaviour Drift Detection from Home Automation Data

Smart Homes diffusion provides an opportunity to implement elderly monitoring, extending seniors' independence and avoiding unnecessary assistance costs. Information concerning the inhabitant behaviour is contained in home automation data, and can be extracted by means of quantitative indicators. The application of such approach proves it can evidence behaviour changes.

Human Behavior Drift Detection in a Smart Home Environment

The proposed system aims at elderly people independent living by providing an early indicator of habits changes which might be relevant for a diagnosis of diseases. It relies on Hidden Markov Model to describe the behavior observing sensors data, while Likelihood Ratio Test gives the variation within different time periods.

ODINS: On-Demand Indoor Navigation System RFID Based

This paper presents an On-Demand Indoor Navigation System (ODINS) based on RFID technology. ODINS is a distributed infrastructure where a set of information points (Fixed Stations - FS) provides the direction to a user who has to reach the destination point he/she has previously selected. ODINS system is proposed for residencies hosting people with mild cognitive disabilities and elderly but it can be also applied to structures where people could be disoriented. The destination is configured at some reception points or it is a predefined (e.g. the bed room or a selected "safe" point). The destination is associated with a RFID disposable bracelet assigned to her/him. The path is algorithmically computed and spread to all FSs. Every time the user is disoriented, she/he can search for the closest FS that displays the right directition. FSs should be located in strategic positions and provide a user-friendly interface such as bright arrows. The complexity is "system-side" making ODINS usable for everyone.