scholarly journals Low-Cost Automatic Ambient Assisted Living System

2018 ◽  
Author(s):  
Hossein Malekmohamadi ◽  
Armaghan Moemeni ◽  
Ahmet Orun ◽  
Jayendra Kumar Purohit
Keyword(s):  
Assisted Living ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Living System

scholarly journals Context Awareness Technology Using Parallel Mining for Ambient Assisted Living System

2018 ◽  
Vol 7 (2.19) ◽  
pp. 52
Author(s):  
J Vivek ◽  
Gandla Maharnisha ◽  
Gandla Roopesh Kumar ◽  
Ch Karun Sagar ◽  
R Arunraj
Keyword(s):  
Health Care ◽  
Big Data ◽  
Knowledge Discovery ◽  
Assisted Living ◽  
Context Awareness ◽  
Health Care Services ◽  
Ambient Assisted Living ◽  
Living System ◽  
Health Condition ◽  
Parallel Mining

In  this  paper,  context  awareness  is  a  promising  technology  that  provides  health care services and a niche  area of big data paradigm. The   drift  in  Knowledge  Discovery  from  Data  refers  to  a  set  of  activities  designed  to refine and  extract  new knowledge from complex  datasets.  The   proposed  model  facilitates  a  parallel  mining  of  frequent item sets for Ambient Assisted Living (AAL) System [a.k.a. Health  Care [System]  of  big  data that  reside   inside  a  cloud  environment.  We  extend  a  knowledge  discovery framework for  processing  and  classifying  the  abnormal  conditions of patients having fluctuations in Blood Pressure (BP) and Heart Rate(HR) and storing  this data  sets  called  Big data  into Cloud to access from  anywhere   when  needed.   This   accessed data is used to compare the new data with it, which helps to know the patients health condition.  

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.

Reliability of IoT-Aware BPMN Healthcare Processes

2020 ◽  
pp. 793-821 ◽  
Author(s):  
Dulce Domingos ◽  
Ana Respício ◽  
Ricardo Martinho
Keyword(s):  
Business Process ◽  
Assisted Living ◽  
Process Model ◽  
Ambient Assisted Living ◽  
Living System ◽  
Biomedical Sensors ◽  
Language Extension ◽  
Healthcare Process ◽  
Healthcare Processes ◽  
Electronic Tags

BPMN (Business Process Model and Notation) has become the de-facto business process modelling language standard. Healthcare processes have been increasingly incorporating participants other than humans, including Internet of Things (IoT) physical devices such as biomedical sensors or patient electronic tags. Due to its critical requirements, IoT-aware healthcare processes justify the relevance of Quality of Services aspects, such as reliability, availability, and cost, among others. This chapter focuses on reliability and proposes to use the Stochastic Workflow Reduction (SWR) method to calculate the reliability of IoT-aware BPMN healthcare processes. In addition, the chapter proposes a BPMN language extension to provide processes with reliability information. This way, at design time, modellers can analyse alternatives and, at run time, reliability information can be used to select participants, execute services, or monitor process executions. The proposal is applied to an Ambient Assisted Living system use case, a rich example of an IoT-aware healthcare process.

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.

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