Ambient Assisted Living [Guest editors' introduction] (original) (raw)
Related papers
A multi-sensor system for fall detection in ambient assisted living contexts
The aging population represents an emerging challenge for healthcare since elderly people frequently suffer from chronic diseases requiring continuous medical care and monitoring. Sensor networks are possible enabling technologies for ambient assisted living solutions helping elderly people to be independent and to feel more secure. This paper presents a multi-sensor system for the detection of people falls in home environment. Two kinds of sensors are used: a wearable wireless accelerometer with onboard fall detection algorithms and a time-of-flight camera. A coordinator node receives data from the two sub-sensory systems with their associated level of confidence and, on the basis of a data fusion logic, it operates the validation and correlation among the two sub-systems delivered data in order to rise overall system performance with respect to each single sensor sub-system. Achieved results show the effectiveness of the suggested multi-sensor approach for improving fall detection service in ambient assisted living contexts.
Automatic fall detection and activity monitoring for elderly
Our modern societies are suffering the increase of elderly population while at the same time social security and health costs must be cut down. In order to avoid the need of special care centers, the actual trend is to encourage elderly to stay living autonomously in their own homes as long as possible. The product presented in this paper contributes to this objective, since it provides user localization, automatic fall detection and activity monitoring both for indoors and outdoors activities, associated to a complete call centre for medical monitoring of the patient as well as to provide support and manage emergency situations.
Expert Systems with Applications, 2013
The Homecare project, which is part of a research project funded by the French National Research Agency (ANR), aims to define a new multi-sensor monitoring system for the elderly with cognitive disabilities in a care unit. Two subjects were recruited to participate to experimental trials. The main objective of this project is to design and test a complete monitoring system at a real site. It is a new clinical and technical approach which is complex to implement: Homecare is intended to propose a possible technical solution, demonstrate its feasibility and illustrate its use working at a protected site. The system consists of a motion sensor network deployed on the ceiling to monitor motion and an electronic patch worn by the subjects to identify them and detect falls. In order to locate tagged subjects inside the care unit, a network of anchor points is used. From these positions and movement data, an analysis algorithm detects an abnormal situation and alerts the nursing staff in real time. A Web application allows the medical staff to access movements and alarms. The complete monitoring system has been functioning for several months and continuously monitors two patients around the clock. In this paper, we present the implementation of the system, the method of localization inside the care unit, and the characterization of the fall detector, and we show certain results relating to activity data.
Background. Few studies of sensor-based falls detection devices have monitored older people in their care settings, particularly in Australia. The present investigation addressed this gap by trialling the feasibility and acceptability of a non-contact smart sensor system (NCSSS) to monitor behaviour and detect falls in an Australian residential aged care facility (RACF). Methods. This study used a mixed methods approach: a) Pilot study implementation at a RACF, b) Post-pilot interviews, c) Analysis and review of results. Results and discussion. Data was collected for four RACF participants over four weeks of the NCSSS pilot. No falls were recorded during the uptime of the system. Numerous feasibility challenges were encountered, for example in the installation, configuration, and location of sensors for optimal detection, network and connectivity issues, and maintenance requirements. These factors may affect NCSSS implementation and adherence.
Fall Detection on Ambient Assisted Living using a Wireless Sensor Network
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal
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%.
Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers October 2018 Pages 770–777, 2019
Falling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of the elderly people so that in case of fall incident they may get rescued timely. For this purpose, many fall monitoring systems have been proposed for the ubiquitous personal assistance of the elderly people but most of those systems focus on the detection of fall incident only. However, if a fall monitoring system is made capable of recognizing the way in which the fall occurs, it can better assist people in preventing or reducing future falls. Therefore, in this study, we proposed a fall monitoring system that not only detects a fall but also recognizes the pattern of the fall for elderly assistance using supervised machine learning. The proposed system effectively distinguishes between falling and non-falling activities to recognize the fall pattern with a high accuracy.
Testing non-wearable fall detection methods in the homes of older adults
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a twowebcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.