For improving e-health services, we propose a context-aware framework to monitor the activities of daily living of dependent persons. We define a strategy for generating long-term realistic scenarios and a framework containing an adaptive monitoring algorithm based on three approaches for optimizing resource usage. The used approaches provide a deep knowledge about the person's context by considering: the person's profile, the activities and the relationships between activities. We evaluate the performances of our framework and show its adaptability and significant reduction in network, energy and processing usage over a traditional monitoring implementation.
Many systems and applications have been designed for dealing with different ideas including sensing and network communications @cite_3 @cite_2 , computing analysis algorithms @cite_1 , energy consumption @cite_7 , time-cost effectiveness, assisting and providing services @cite_6 , etc. In existing HSH and HIS, there are three main categories: physiological monitoring, movement or fall detection and activity monitoring. Physiological systems record the vital signs of elderly and chronically ill @cite_9 . Movement or fall detection systems are ambulatory activities detection including dynamic activities (like @math ) and static postures (like @math ), location tracking and accidental falls @cite_15 . Activity of Daily Living (ADL) monitoring is covering a list of basic daily activities (such as @math and @math ) and allows the elderly to live independently and to provide accurate services for caregivers @cite_6 @cite_8 . Existing approaches lack a true understanding about the person's activities that should be considered in monitoring. Their lack of adaptability lead to a limited and narrow range of activity selection.
@cite_7 @cite_8 @cite_9 @cite_1 @cite_6 @cite_3 @cite_2 @cite_15
2021500530 2765171540 2728787983 2539105851
Recently, how to support and care elderly people is serious problem. Modern approach is trying to support to keep health (body and mental) seniors living alone, but it is hard to keep a health by oneself. Our aim is monitoring a user's life for providing suitable services to keep his her health. To providing suitable service, life log is very useful information. However, traditional monitoring system is only focusing elementary motion, walking, sleeping, and so on. We deal with recognizing advanced motions by using transportation data from an area to other areas in a house. These advanced motion logs support not only elderly but also care stuff or family who plans supports. In daily life, our motion is very variety. Therefore we focused on Basic Activities of Daily Living (ADL) which categories normally doing in daily life such as feeding, bathing, homemaking, and so on. In this research, we pick up 11 motion from ADL for recognition. In active and assisted living environments, a major service that can be provided is the automated assessment of elderly people’s well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm, which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of this paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS data set and artificially generated laboratory data using our HBMS simulator. The results show high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches. New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12 improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73 improvement. As we grow old, our desire for independence does not diminish; yet our health increasingly needs to be monitored. Injuries such as falling can be a serious problem for the elderly. If a person falls and is not able to get assistance within an hour, casualties arising from that fall can result in fatalities as early as 6 months later [1]. It would seem then that a choice between safety and independence must be made. Fortunately, as health care technology advances, simple devices can be made to detect or even predict falls in the elderly, which could easily save lives without too much intrusion on their independence. Much research has been done on the topic of fall detection and fall prediction. Some have attempted to detect falls using a variety of sensors such as: cameras, accelerometers, gyroscopes, microphones, or a combination of the like. This paper is aimed at reporting which existing methods have been found effective by others, as well as documenting the findings of our own experiments. The combination of which will assist in the progression towards a safe, unobtrusive monitoring system for independent seniors.