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.

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 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 Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 778
Author(s):  
Nitsa J. Herzog ◽  
George D. Magoulas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Progressive Dementia ◽  
Machine Learning Classification

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

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