Depth Cameras in AAL Environments

2015 ◽  
pp. 22-41 ◽  
Author(s):  
Samuele Gasparrini ◽  
Enea Cippitelli ◽  
Susanna Spinsante ◽  
Ennio Gambi
Keyword(s):  
Assisted Living ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Automatic Monitoring ◽  
Detection Algorithm ◽  
Research Area ◽  
Microsoft Kinect ◽  
Depth Cameras ◽  
Visual Sensing

Automatic and privacy-preserving systems to monitor elderly people in their home environment are one of the basic targets addressed by the wide research area of Ambient Assisted Living. Thanks to the low-cost Microsoft Kinect® device, high-resolution depth and visual sensing is now not limited to experimental and prototype implementations and is ready to address marketable solutions. This chapter emphasizes the advantages provided by Kinect in the field of automatic monitoring, discussing its performance in human subject detection and tracking. Two sample use cases are discussed in detail: the former deals with generating a numerical representation of the Get Up and Go Test outcome, the latter implements an automatic fall detection algorithm based on depth frames analysis, with the sensor in a top configuration. The chapter ends suggesting issues that need to be addressed to further extend the range of applications for the Kinect device and enhance the obtainable performance.

Depth Cameras in AAL Environments

Gamification ◽  
2015 ◽  
pp. 1056-1075 ◽  
Author(s):  
Samuele Gasparrini ◽  
Enea Cippitelli ◽  
Susanna Spinsante ◽  
Ennio Gambi
Keyword(s):  
Assisted Living ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Automatic Monitoring ◽  
Detection Algorithm ◽  
Research Area ◽  
Microsoft Kinect ◽  
Depth Cameras ◽  
Visual Sensing

Automatic and privacy-preserving systems to monitor elderly people in their home environment are one of the basic targets addressed by the wide research area of Ambient Assisted Living. Thanks to the low-cost Microsoft Kinect® device, high-resolution depth and visual sensing is now not limited to experimental and prototype implementations and is ready to address marketable solutions. This chapter emphasizes the advantages provided by Kinect in the field of automatic monitoring, discussing its performance in human subject detection and tracking. Two sample use cases are discussed in detail: the former deals with generating a numerical representation of the Get Up and Go Test outcome, the latter implements an automatic fall detection algorithm based on depth frames analysis, with the sensor in a top configuration. The chapter ends suggesting issues that need to be addressed to further extend the range of applications for the Kinect device and enhance the obtainable performance.

scholarly journals Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

2020 ◽  
Vol 6 (3) ◽  
pp. 388-391
Author(s):  
Roman Siedel ◽  
Tobias Scheck ◽  
Ana C. Perez Grassi ◽  
Julian B. Seuffert ◽  
André Apitzsch ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Quality Estimation ◽  
Emergency Situations ◽  
People With Dementia ◽  
Affected Person ◽  
Development State ◽  
Current Behaviour

AbstractIn recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.

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.

A robust multimodal fall detection method for ambient assisted living applications

2010 ◽  
Author(s):  
Hande Ozgur Alemdar ◽  
Yunus Emre Kara ◽  
Mustafa Ozan Ozen ◽  
Gokhan Remzi Yavuz ◽  
Ozlem Durmaz Incel ◽  
...  
Keyword(s):  
Assisted Living ◽  
Detection Method ◽  
Ambient Assisted Living ◽  
Fall Detection

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.

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