INTEROPERABILITY IN AMBIENT ASSISTED LIVING (AAL) - Standardization of Sensor-data based on ISO/IEEE 11073

2010 ◽  
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Sensor Data ◽  
Ieee 11073

Multi-sensing Monitoring and Knowledge-driven Analysis for Dementia Assessment

2015 ◽  
Vol 6 (4) ◽  
pp. 77-92 ◽  
Author(s):  
Thanos G. Stavropoulos ◽  
Georgios Meditskos ◽  
Efstratios Kontopoulos ◽  
Ioannis Kompatsiaris
Keyword(s):  
Assisted Living ◽  
Common Knowledge ◽  
Wearable Sensors ◽  
Ambient Assisted Living ◽  
Semantic Interpretation ◽  
Sensor Data ◽  
End User ◽  
People With Dementia ◽  
Service Oriented ◽  
Assessment And Monitoring

DemaWare is a Service-Oriented platform that aids in the timely assessment and monitoring of people with dementia in an Ambient Assisted Living context. This work presents in detail the underlying modules integrated in DemaWare, providing both software and hardware services. The system coordinates the retrieval of raw sensor data from a variety of sources, such as ambient and wearable sensors, and their processing into a common knowledge base. The semantic interpretation performed afterwards reasons upon collected knowledge and infers higher level observations. Finally, all knowledge is presented in suitable end-user applications that support various scenarios, e.g. lab assessment trials and monitoring in nursing home environments.

Sensor data fusion for activity monitoring in the PERSONA ambient assisted living project

2011 ◽  
Vol 4 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Michele Amoretti ◽  
Sergio Copelli ◽  
Folker Wientapper ◽  
Francesco Furfari ◽  
Stefano Lenzi ◽  
...  
Keyword(s):  
Data Fusion ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Activity Monitoring ◽  
Sensor Data ◽  
Sensor Data Fusion

Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records

2014 ◽  
Vol 53 (03) ◽  
pp. 149-151 ◽  
Author(s):  
L. Schöpe ◽  
P. Knaup
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Smart Homes ◽  
Health Data ◽  
Sensor Data ◽  
Health Records ◽  
Electronic Health ◽  
Using Data

SummaryIntroduction: This editorial is part of the Focus Theme of Methods of Information in Medicine on “Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records”.Background: To increase efficiency in the health care of the future, data from innovative technology like it is used for ambient assisted living (AAL) or smart homes should be available for individual health decisions. Integrating and aggregating data from different medical devices and health records enables a comprehensive view on health data.Objectives: The objective of this paper is to present examples of the state of the art in research on information management that leads to a sustainable use and long-term storage of health data provided by innovative assistive technologies in daily living.Results: Current research deals with the perceived usefulness of sensor data, the participatory design of visual displays for presenting monitoring data, and communication architectures for integrating sensor data from home health care environments with health care providers either via a regional health record bank or via a telemedical center.Conclusions: Integrating data from AAL systems and smart homes with data from electronic patient or health records is still in an early stage. Several projects are in an advanced conceptual phase, some of them exploring feasibility with the help of prototypes. General comprehensive solutions are hardly available and should become a major issue of medical informatics research in the near future.

scholarly journals An Approach for Representing Sensor Data to Validate Alerts in Ambient Assisted Living

Sensors ◽  
2012 ◽  
Vol 12 (5) ◽  
pp. 6282-6306 ◽  
Author(s):  
Andrés Muñoz ◽  
Emilio Serrano ◽  
Ana Villa ◽  
Mercedes Valdés ◽  
Juan A. Botía
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Sensor Data

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 Real-Time Capable Sensor Data Analysis-Framework for Intelligent Assistance Systems

2021 ◽  
Author(s):  
Ulrich H.P. Fischer ◽  
Sabrina Hoppstock ◽  
Peter Kußmann ◽  
Isabell Steuding
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Sensor Data ◽  
Industrialized Countries ◽  
Cognitive Limitations ◽  
Age Cohort ◽  
Real Time Visualization ◽  
Frame Work

In the industrialized countries, the very old part of the population has been growing rapidly for many years. In the next few years in particular, the age cohort over 65 will increase significantly. This goes hand in hand with illnesses and other physical and cognitive limitations. In order to enable these people to remain in their own homes for as long as possible despite physical and cognitive restrictions, technologies are being used to create ambient assisted living applications. However, most of these systems are neither medically verified nor are latencies short enough, for example, to avoid falls. In order to overcome these problems, a promising approach is to use the new 5G network technology. Combined with a suitable sensor data analysis frame work, the fast care project showed that a real-time situation picture of the patient in the form of an Avatar could be generated. The sensor structure records the heart rate, the breathing rate, analyzes the gait and measures the temperature, the VOC content of the room air, and its humidity. An emergency button has also been integrated. In a laboratory demonstrator, it was shown that the infrastructure realizes a real-time visualization of the sensor data over a heterogeneous network.

scholarly journals Fast care – real-time sensor data analysis framework for intelligent assistance systems

2017 ◽  
Vol 3 (2) ◽  
pp. 743-747
Author(s):  
Albert Hein ◽  
Florian Grützmacher ◽  
Christian Haubelt ◽  
Thomas Kirste
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Assisted Living ◽  
Ad Hoc ◽  
Ambient Assisted Living ◽  
Situation Model ◽  
Sensor Data ◽  
Analysis Framework ◽  
Assistance Systems ◽  
Assistive Systems

AbstractMain target of fast care is the development of a real-time capable sensor data analysis framework for intelligent assistive systems in the field of Ambient Assisted Living, eHealth, Tele Rehabilitation, and Tele Care. The aim is to provide a medically valid integrated situation model based on a distributed, ad-hoc connected, energy-efficient sensor infrastructure suitable for daily use. The integrated situation model combining physiological, cognitive, and kinematic information about the patient is grounded on the intelligent fusion of heterogeneous sensor data on different levels. The model can serve as a tool for quickly identifying risk and hazards as well as enable medical assistance systems to autonomously intervene in real-time and actively give telemedical feedback.

scholarly journals Assessing Older Adults’ Perceptions of Sensor Data and Designing Visual Displays for Ambient Environments

2014 ◽  
Vol 53 (03) ◽  
pp. 152-159 ◽  
Author(s):  
J. Chung ◽  
T. Le ◽  
H. Thompson ◽  
G. Demiris ◽  
B. Reeder
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Assisted Living ◽  
Family Members ◽  
Ambient Assisted Living ◽  
Perceived Usefulness ◽  
Sensor Data ◽  
Care Providers ◽  
Visual Displays ◽  
Adult Participants

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records“.Objectives: Our objectives were to: 1) characterize older adult participants’ perceived usefulness of in-home sensor data and 2) develop novel visual displays for sensor data from Ambient Assisted Living environments that can become part of electronic health records.Methods: Semi-structured interviews were conducted with community-dwelling older adult participants during three and six-month visits. We engaged participants in two design iterations by soliciting feedback about display types and visual displays of simulated data related to a fall scenario. Interview transcripts were analyzed to identify themes related to perceived usefulness of sensor data.Results: Thematic analysis identified three themes: perceived usefulness of sensor data for managing health; factors that affect perceived usefulness of sensor data and; perceived usefulness of visual displays. Visual displays were cited as potentially useful for family members and health care providers. Three novel visual displays were created based on interview results, design guidelines derived from prior AAL research, and principles of graphic design theory.Conclusions: Participants identified potential uses of personal activity data for monitoring health status and capturing early signs of illness. One area for future research is to determine how visual displays of AAL data might be utilized to connect family members and health care providers through shared understanding of activity levels versus a more simplified view of self-management. Connecting informal and formal caregiving networks may facilitate better communication between older adults, family members and health care providers for shared decision-making.

scholarly journals Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Emilio Sansano-Sansano ◽  
Óscar Belmonte-Fernández ◽  
Raúl Montoliu ◽  
Arturo Gascó-Compte ◽  
Antonio Caballer-Miedes
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Indoor Positioning ◽  
Training Data ◽  
Sensor Data ◽  
Smart Devices ◽  
Positioning System ◽  
Positioning Systems ◽  
Future Performance ◽  
Data Scaling

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

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