scholarly journals Social-aware Event Handling within the FallRisk Project

2017 ◽  
Vol 56 (01) ◽  
pp. 63-73 ◽  
Author(s):  
Jan Van den Bergh ◽  
Sven Coppers ◽  
Shirley Elprama ◽  
Jelle Nelis ◽  
Stijn Verstichel ◽  
...  
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Smart Devices ◽  
Accurate Analysis ◽  
Fall Incident ◽  
Detection Systems ◽  
Event Handling ◽  
Current Guidelines ◽  
The Internet Of Things

SummaryObjectives: With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a social-aware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way.Methods: The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account.Results: The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process.Conclusions: The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.

Ambient Assisted Living Systems: The Scope of Research and Development

2017 ◽  
Vol 3 (2) ◽  
pp. 21
Author(s):  
Ashish D Patel ◽  
Jigarkumar H. Shah
Keyword(s):  
Assisted Living ◽  
Independent Living ◽  
Intelligent Systems ◽  
New Technologies ◽  
Ambient Assisted Living ◽  
Smart Devices ◽  
Economic Changes ◽  
Aged Population ◽  
The People ◽  
Living Person

The aged population of the world is increasing by a large factor due to the availability of medical and other facilities. As the number grows rapidly, requirements of this segment of age (65+) are increasing rapidly as well as the percentage of aged persons living alone is also increasing with the same rate due to the inevitable socio-economic changes. This situation demands the solution of many problems like loneliness, chronic conditions, social interaction, transportation, day-to-day life and many more for independent living person. A large part of aged population may not be able to interact directly with new technologies. This sought some serious development towards the use of intelligent systems i.e. smart devices which helps the people with their inability to use the available as well future solutions. Ambient Assisted Living (AAL) is the answer to these problems. In this paper, issues related to AAL systems are studied. Study of challenges and limitations of this comparatively new field will help the designers to remove the barriers of AAL systems.

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.

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 Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4227 ◽  
Author(s):  
Andres Sanchez-Comas ◽  
Kåre Synnes ◽  
Josef Hallberg
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Research Field ◽  
Health Security ◽  
Assistive Robots ◽  
Road Map ◽  
The People

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.

Adaptive window based fall detection using anomaly identification in fog computing scenario

2021 ◽  
Vol 17 (1) ◽  
pp. 15-37
Author(s):  
Rashmi Shrivastava ◽  
Manju Pandey
Keyword(s):  
Feature Extraction ◽  
Assisted Living ◽  
Detection System ◽  
Fog Computing ◽  
Ambient Assisted Living ◽  
Window Size ◽  
Fall Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Adaptive Window

Human fall detection is a subcategory of ambient assisted living. Falls are dangerous for old aged people especially those who are unaccompanied. Detection of falls as early as possible along with high accuracy is indispensable to save the person otherwise it may lead to physical disability even death also. The proposed fall detection system is implemented in the edge computing scenario. An adaptive window-based approach is proposed here for feature extraction because window size affects the performance of the classifier. For training and testing purposes two public datasets and our collected dataset have been used. Anomaly identification based on a support vector machine with an enhanced chi-square kernel is used here for the classification of Activities of Daily Living (ADL) and fall activities. Using the proposed approach 100% sensitivity and 98.08% specificity have been achieved which are better when compared with three recent research based on unsupervised learning. One of the important aspects of this study is that it is also validated on actual real fall data and got 100% accuracy. This complete fall detection model is implemented in the fog computing scenario. The proposed approach of adaptive window based feature extraction is better than static window based approaches and three recent fall detection methods.

scholarly journals A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method

2021 ◽  
Vol 10 (3) ◽  
pp. 39
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Machine Learning ◽  
Assisted Living ◽  
Fall Detection ◽  
Machine Learning Method ◽  
Learning Method ◽  
Learning Methods ◽  
Detection Systems ◽  
Machine Learning Methods ◽  
Performance Accuracy ◽  
Optimal Machine

This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of internet of things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method for the development of such systems. This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in terms of performance accuracy. Second, it presents a framework that overcomes the limitations of binary classifier-based fall detection systems by being able to detect falls and fall-like motions. Third, to increase the trust and reliance on fall detection systems, it introduces a novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field. This approach achieved performance accuracies of 99.87% and 99.66%, respectively, when evaluated on the two datasets. Finally, the proposed approach is also highly accurate in detecting the activity of standing up from a lying position to infer whether a fall was followed by a long lie, which can cause minor to major health-related concerns. The above contributions address multiple research challenges in the field of fall detection, that we identified after conducting a comprehensive review of related works, which is also presented in this paper.

scholarly journals Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

2013 ◽  
Vol 1 (1) ◽  
pp. 62-77 ◽  
Author(s):  
António Pereira ◽  
Filipe Felisberto ◽  
Luis Maduro ◽  
Miguel Felgueiras
Keyword(s):  
Sensor Networks ◽  
Distributed System ◽  
Assisted Living ◽  
Daily Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Wireless Sensor ◽  
Intelligent Algorithm ◽  
Control Center ◽  
New Information

In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%.  

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