Low-cost indoor localization using cameras – Evaluating AmbiTrack and its applications in Ambient Assisted Living

2016 ◽  
Vol 8 (3) ◽  
pp. 243-258 ◽  
Author(s):  
Andreas Braun ◽  
Tim Dutz
Keyword(s):  
Assisted Living ◽  
Indoor Localization ◽  
Low Cost ◽  
Ambient Assisted Living

Instrumenting the eHome and Preparing Elderly Pilots

2015 ◽  
pp. 335-361 ◽  
Author(s):  
Panagiotis E. Antoniou ◽  
Evdokimos Konstantinidis ◽  
Antonis S. Billis ◽  
Giorgos Bamparopoulos ◽  
Marianna S. Tsatali ◽  
...  
Keyword(s):  
Assisted Living ◽  
Healthcare Costs ◽  
Healthy Lifestyle ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Daily Activities ◽  
World Population ◽  
Methodological Framework ◽  
Current Trends ◽  
First World

In this chapter the lessons learnt from the build-up and integration of the USEFIL are demonstrated. First an introduction to Ambient Assisted Living (AAL) platforms, the infrastructure for eHomes of any purpose eHome is presented, in the context of their emergence as a viable way for managing healthcare costs in an aging first world population. Then technical and sustainability issues that are present after several years of maturation are touched upon. The USEFIL project's aim at an AAL platform that utilizes low cost “off-the-shelf” technologies in order to develop immediately applicable services, to assist elderly people in maintaining an independent, healthy lifestyle and program of daily activities is then briefly discussed. Afterwards, the methodological framework as well as principal results of the preparation and running of the pre-piloting phase of that platform are presented. Closing, current trends are explored in conjunction with future directions as triggered by this project in the context of cognitive impaired elderly support.

scholarly journals A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems

2017 ◽  
Vol 7 (9) ◽  
pp. 877 ◽  
Author(s):  
Miguel Quintana-Suárez ◽  
David Sánchez-Rodríguez ◽  
Itziar Alonso-González ◽  
Jesús Alonso-Hernández
Keyword(s):  
Assisted Living ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Living Systems ◽  
Acoustic Sensor

scholarly journals User Identity Protection in Automatic Emotion Recognition through Disguised Speech

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 636-649
Author(s):  
Fasih Haider ◽  
Pierre Albert ◽  
Saturnino Luz
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Assisted Living ◽  
Speech Signal ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Recognition Task ◽  
Affect Recognition ◽  
Acoustic Features ◽  
Machine Learning Classification

Ambient Assisted Living (AAL) technologies are being developed which could assist elderly people to live healthy and active lives. These technologies have been used to monitor people’s daily exercises, consumption of calories and sleep patterns, and to provide coaching interventions to foster positive behaviour. Speech and audio processing can be used to complement such AAL technologies to inform interventions for healthy ageing by analyzing speech data captured in the user’s home. However, collection of data in home settings presents challenges. One of the most pressing challenges concerns how to manage privacy and data protection. To address this issue, we proposed a low cost system for recording disguised speech signals which can protect user identity by using pitch shifting. The disguised speech so recorded can then be used for training machine learning models for affective behaviour monitoring. Affective behaviour could provide an indicator of the onset of mental health issues such as depression and cognitive impairment, and help develop clinical tools for automatically detecting and monitoring disease progression. In this article, acoustic features extracted from the non-disguised and disguised speech are evaluated in an affect recognition task using six different machine learning classification methods. The results of transfer learning from non-disguised to disguised speech are also demonstrated. We have identified sets of acoustic features which are not affected by the pitch shifting algorithm and also evaluated them in affect recognition. We found that, while the non-disguised speech signal gives the best Unweighted Average Recall (UAR) of 80.01%, the disguised speech signal only causes a slight degradation of performance, reaching 76.29%. The transfer learning from non-disguised to disguised speech results in a reduction of UAR (65.13%). However, feature selection improves the UAR (68.32%). This approach forms part of a large project which includes health and wellbeing monitoring and coaching.

scholarly journals IndoorCare: Low-Cost Elderly Activity Monitoring System through Image Processing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6051
Author(s):  
Daniel Fuentes ◽  
Luís Correia ◽  
Nuno Costa ◽  
Arsénio Reis ◽  
José Ribeiro ◽  
...  
Keyword(s):  
Low Income ◽  
Assisted Living ◽  
Rural Areas ◽  
Low Cost ◽  
Elderly Care ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Case Scenario ◽  
Care Services ◽  
Socially Excluded

The Portuguese population is aging at an increasing rate, which introduces new problems, particularly in rural areas, where the population is small and widely spread throughout the territory. These people, mostly elderly, have low income and are often isolated and socially excluded. This work researches and proposes an affordable Ambient Assisted Living (AAL)-based solution to monitor the activities of elderly individuals, inside their homes, in a pervasive and non-intrusive way, while preserving their privacy. The solution uses a set of low-cost IoT sensor devices, computer vision algorithms and reasoning rules, to acquire data and recognize the activities performed by a subject inside a home. A conceptual architecture and a functional prototype were developed, the prototype being successfully tested in an environment similar to a real case scenario. The system and the underlying concept can be used as a building block for remote and distributed elderly care services, in which the elderly live autonomously in their homes, but have the attention of a caregiver when needed.

scholarly journals Using IoT technologies to develop a low-cost smart medicine box

2019 ◽  
Author(s):  
Danyllo V. da Silva ◽  
Taisa G. Gonçalves ◽  
Paulo F. Pires
Keyword(s):  
Assisted Living ◽  
Health Professionals ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Treatment Compliance ◽  
Treatment Monitoring ◽  
Quality Life ◽  
Urban Monitoring ◽  
Energy Transportation ◽  
System Prototype

Internet of Things (IoT) is a paradigm that has provided improvements for the day-to-day of society. This paradigm has been applied in several domains such as ambient assisted living (AAL), energy, transportation, environmental, urban monitoring, and healthcare. In the healthcare domain, IoT offers many advantages, such as enable continuous health monitoring, improve quality life and comfort, among others. A kind of IoT application in this domain is smart medicine box, a device that provides medicines treatment monitoring of users. It allows health professionals to verify users’ treatment compliance and supports decision-making. Most of the smart medicine box projects found in the literature are still expensive and do not address some characteristics of IoT systems such as scalability, latency, time to response, among others. Taking into account this scenario, this work proposes a low-cost IoT system prototype to support users during their medicines manipulation. The proposal employs edge-computing concept to add intermediate layer improving the communication among devices and services.

Employing Opportunistic Networks in Dementia Patient Monitoring

2015 ◽  
pp. 106-136
Author(s):  
Radu-Ioan Ciobanu ◽  
Ciprian Dobre
Keyword(s):  
Assisted Living ◽  
Patient Monitoring ◽  
Data Transfer ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Opportunistic Networks ◽  
Dementia Patient ◽  
Area Network ◽  
Dementia Patients ◽  
Life Threatening

By 2050, 135.5 million people will suffer from dementia worldwide. Ambient Assisted Living (AAL) technologies can help dementia patients enjoy an independent life. In particular, communication is vital to any AAL system. Opportunistic networking uses low-cost wearable devices to exchange packets at a close range in cases where there is limited or no infrastructure. In this chapter, the authors propose and describe an autonomous patient monitoring support system based on opportunistic communication. The monitored patient wears non-intrusive sensors, computing devices and actuators, forming a Body Area Network (BAN). The BAN can provide memory impairment support services for the patient and is used to construct personalized condition-monitoring patient models to evaluate against a set of potential life-threatening events. The authors present two data transfer algorithms and show that they are able to offer good hit rates while decreasing congestion and overhead when compared to other existing solutions.

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