scholarly journals Towards a Simulation Framework for Smart Indoor Spaces

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7137
Author(s):  
Shadan Golestan ◽  
Ioanis Nikolaidis ◽  
Eleni Stroulia
Keyword(s):  
Assisted Living ◽  
Daily Living ◽  
Time Window ◽  
Ambient Assisted Living ◽  
Sensor Deployment ◽  
Simulation Framework ◽  
Simulation Based ◽  
Different Types ◽  
Indoor Spaces ◽  
Over Time

The effectiveness of sensor-based applications for smart homes and smart buildings is conditioned upon the deployment configuration of their underlying sensors. Real-world evaluation of alternative possible sensor-deployment configurations is labor-intensive, costly, and time-consuming, which implies the need for a simulation-based methodology. In this work, we report on such a methodology that supports the modeling of indoor spaces, the activities of their occupants, and the behaviors of different types of sensors. We argue that, in order for a simulation to be useful for the purpose of evaluating a sensor deployment configuration, it has to generate realistic event streams of individual sensors over time, as well as realistic compositions of sensor events within a time window. We have evaluated our simulator for smart indoor spaces, SIMsis toolkit, in the context of our Smart-Condo ambient-assisted living platform, supporting the observation and analysis of activities of daily living (ADLs). Our findings indicate that SIMsis produces realistic agent traces and sensor readings, and has the potential to support the process of developing and deploying sensor-based applications.

scholarly journals Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report

10.2196/20215 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e20215 ◽  
Author(s):  
Maxime Lussier ◽  
Aline Aboujaoudé ◽  
Mélanie Couture ◽  
Maxim Moreau ◽  
Catherine Laliberté ◽  
...  
Keyword(s):  
Assisted Living ◽  
Concurrent Validity ◽  
Daily Living ◽  
Health Care Services ◽  
Single Case ◽  
Ambient Assisted Living ◽  
Single Case Study ◽  
Care Services ◽  
Over Time

Background Many older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level. Objective The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation. Methods This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity. Results Over time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation. Conclusions Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse’s questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.

scholarly journals Mobile Robots and Autonomic Ambient Assisted Living

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Guanitta Brady ◽  
Roy Sterritt ◽  
George Wilkie
Keyword(s):  
Mobile Robot ◽  
Assisted Living ◽  
Paradigm Shift ◽  
Daily Living ◽  
Ambient Assisted Living ◽  
Research Topic ◽  
Smart Environment ◽  
Sensor Faults ◽  
Ubiquitous Sensing ◽  
Over Time

Abstract The use of Smart Environments in the delivery of pervasive care is a research topic that has witnessed increasing interest in recent years. These environments aim to deliver pervasive care through ubiquitous sensing by monitoring the occupants Activities of Daily Living. In order for these environments to succeed in achieving their goal, it is crucial that sensors deployed in the environment perform faultlessly. In this research we investigate addressing anomalous sensor behavior through the utilization of a mobile robot. The robot’s role is twofold; it must provide substitution in the presence of suspected sensor faults and act as an observer of anomalous sensor behavior in order to understand the changes that occur in the behavior of sensors deployed within the environment over time. The aim of this work is to explore a paradigm shift to the use of Autonomic Ambient Assisted Living.We have discovered that the use of a mobile robot is a viable means of introducing this paradigm to a Smart Environment.

scholarly journals Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report (Preprint)

2020 ◽  
Author(s):  
Maxime Lussier ◽  
Aline Aboujaoudé ◽  
Mélanie Couture ◽  
Maxim Moreau ◽  
Catherine Laliberté ◽  
...  
Keyword(s):  
Assisted Living ◽  
Concurrent Validity ◽  
Daily Living ◽  
Health Care Services ◽  
Single Case ◽  
Ambient Assisted Living ◽  
Single Case Study ◽  
Care Services ◽  
Over Time

BACKGROUND Many older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level. OBJECTIVE The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation. METHODS This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity. RESULTS Over time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation. CONCLUSIONS Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse’s questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.

scholarly journals An Evaluation of Graphical Formats for the Summary of Activities of Daily Living (ADLs)

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 194
Author(s):  
Caroline A. Byrne ◽  
Michael O’Grady ◽  
Rem Collier ◽  
Gregory M. P. O’Hare
Keyword(s):  
Activities Of Daily Living ◽  
Assisted Living ◽  
Daily Living ◽  
Line Graph ◽  
Ambient Assisted Living ◽  
User Interaction ◽  
The Elderly ◽  
Design Principles ◽  
Daily Routine ◽  
Daily Monitoring

Activities of Daily Living systems (ADLs) and the User Interface (UI) design principles used to implement them empowers the elderly to continue living a normal daily routine. The daily monitoring of activities for most Assisted Living (AL) systems demands/necessitates accurate daily user interaction, and the design principles for these systems often focus on the UI usability for the elder, not the caregiver/family member. This paper reviews Ambient Assisted Living (AAL) and ADLs UI designs and evaluates the usability of ADLs visualisation tools for caregivers. Results indicate that the UI presenting information in a bar graph format was the preferred option for respondents, as 60% chose this summarisation method over the alternative line graph UI, which had 38% of respondents selecting this format for information representation. Therefore, when designing Ambient Assisted Living (AAL) UIs, it is recommended that short periods of time are best presented in a pie graph format in combination with a bar graph format for representing extended timeline information to caregivers about their loved ones.

scholarly journals Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

2013 ◽  
Vol 1 (1) ◽  
pp. 62-77 ◽  
Author(s):  
António Pereira ◽  
Filipe Felisberto ◽  
Luis Maduro ◽  
Miguel Felgueiras
Keyword(s):  
Sensor Networks ◽  
Distributed System ◽  
Assisted Living ◽  
Daily Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Wireless Sensor ◽  
Intelligent Algorithm ◽  
Control Center ◽  
New Information

In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%.  

scholarly journals Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7161
Author(s):  
Yiming Tian ◽  
Jie Zhang
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Good Choice ◽  
Learning System ◽  
Sensor Deployment ◽  
Swarm Optimization ◽  
Large Power ◽  
Concurrent Use ◽  
Glowworm Swarm Optimization ◽  
Selective Ensemble

Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.

scholarly journals A Review and Classification of Assisted Living Systems

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 182 ◽  
Author(s):  
Caroline Byrne ◽  
Rem Collier ◽  
Gregory O’Hare
Keyword(s):  
Assisted Living ◽  
Daily Living ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Active Aging ◽  
Reasonable Accuracy ◽  
Aging Research ◽  
Social Agenda ◽  
Flexible Infrastructure

Europe’s social agenda for the “active elderly” is based upon a series of programs that provide a flexible infrastructure for their lives so that they are motivated, engaged in lifelong learning, and contributing to society. Economically speaking, Europe must engage in active aging research in order to avoid unsustainable health costs, and ambient assisted living (AAL) systems provide a platform for the elderly to remain living independently. This paper reviews research conducted within the area of AAL, and offers a taxonomy within which such systems may be classified. This classification distinguishes itself from others in that it categorises AAL systems in a top-down fashion, with the most important categories placed immediately to the left. In this paper, each section is explored further, and AAL systems are the focus. Entire AAL systems still cannot be fully evaluated, but their constituent technical parts can be assessed. The activities of daily living (ADLs) component was given further priority due to its potential for system evaluation, based on its ability to recognise ADLs with reasonable accuracy.

scholarly journals Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 768
Author(s):  
Caetano Mazzoni Ranieri ◽  
Scott MacLeod ◽  
Mauro Dragone ◽  
Patricia Amancio Vargas ◽  
Roseli Aparecida Francelin Romero 
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Daily Living ◽  
Smart Home ◽  
Inertial Sensors ◽  
Ambient Assisted Living ◽  
Sensor Data ◽  
University Of Texas ◽  
Demographic Projections ◽  
The University

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

scholarly journals Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

2018 ◽  
Vol 11 (1) ◽  
pp. 61-88 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Maria Canavarro Teixeira ◽  
Nuno Pombo ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta ◽  
...  
Keyword(s):  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Assisted Living ◽  
Daily Living ◽  
Ambient Assisted Living ◽  
Living Room ◽  
Fusion Data ◽  
Student’S T ◽  
Cleaning Methods

Background:Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors.Objective:This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user.Methods:The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized.Results:The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test.Conclusion:This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.

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