Hande Özgür Alemdar

Wireless sensor networks for healthcare: A survey

Becoming mature enough to be used for improving the quality of life, wireless sensor network technologies are considered as one of the key research areas in computer science and healthcare application industries. The pervasive healthcare systems provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. This minimizes the need for caregivers and helps the chronically ill and elderly to survive an independent life, besides provides quality care for the babies and little children whose both parents have to work. Although having significant benefits, the area has still major challenges which are investigated in this paper. We provide several state of the art examples together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provide a comprehensive analysis of the benefits and challenges of these systems.

Multimodal Wireless Sensor Network-Based Ambient Assisted Living in Real Homes with Multiple Residents

Human activity recognition and behavior monitoring in a home setting using wireless sensor networks (WSNs) provide a great potential for ambient assisted living (AAL) applications, ranging from health and wellbeing monitoring to resource consumption monitoring. However, due to the limitations of the sensor devices, challenges in wireless communication and the challenges in processing large amounts of sensor data in order to recognize complex human activities, WSN-based AAL systems are not effectively integrated in the home environment. Additionally, given the variety of sensor types and activities, selecting the most suitable set of sensors in the deployment is an important task. In order to investigate and propose solutions to such challenges, we introduce a WSN-based multimodal AAL system compatible for homes with multiple residents. Particularly, we focus on the details of the system architecture, including the challenges of sensor selection, deployment, networking and data collection and provide guidelines for the design and deployment of an effective AAL system. We also present the details of the field study we conducted, using the systems deployed in two different real home environments with multiple residents. With these systems, we are able to collect ambient sensor data from multiple homes. This data can be used to assess the wellbeing of the residents and identify deviations from everyday routines, which may be indicators of health problems. Finally, in order to elaborate on the possible applications of the proposed AAL system and to exemplify directions for processing the collected data, we provide the results of several human activity inference experiments, along with examples on how such results could be interpreted. We believe that the experiences shared in this work will contribute towards accelerating the acceptance of WSN-based AAL systems in the home setting.

Daily life behaviour monitoring for health assessment using machine learning: bridging the gap between domains

Analysis of human behaviour for deducing health and well-being information is one of the contemporary challenges given the ageing in place. To this end, existing and newly developed machine learning methods are needed to be evaluated using annotated real-world data sets. However, the metrics used in performance evaluation are directly taken from the machine learning domain, and they do not necessarily consider the specific needs of human behaviour analysis such as recognizing the duration, start time and frequency of the activities. Moreover, the commonly used metrics such as accuracy or F-measure can be misleading in the presence of skewed class distributions as in the case of human behaviour recognition. In this study, we evaluate the performance of two machine learning methods, hidden Markov model and time windowed neural network on five different real-world data sets through human behaviour understanding for health assessment perspective. According to the experimental results, standard metrics fail to reveal the actual performance of the two compared machine learning methods in terms of behaviour recognition. On the other hand, the proposed evaluation mechanism which considers three different activity categories leads to a more realistic evaluation of the overall performance.

Wireless Healthcare Monitoring with RFID-Enhanced Video Sensor Networks

In pervasive healthcare systems, WSNs provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. Furthermore, they minimize the need for caregivers and help the chronically ill and elderly to survive an independent life. In this paper, we propose an outdoor monitoring environment and evaluate the capabilities of video sensor networks for healthcare monitoring in an outdoor setting. The results exhibit that their capabilities are limited. For this reason, we proposed several enhancements for reducing the traffic load on the network for better performance. RFID is a very mature technology that has already been used in many areas. The RFID-enhanced video sensor networks reduce the network traffic load. Moreover, the proximity of the healthcare professionals who are also moving in the surveillance area is also used for better balancing the network load. Finally, for assuring the reporting of the emergency events with low latencies, we propose an emergency frame based queuing mechanism and evaluated its performance through simulations.

Using active learning to allow activity recognition on a large scale

Automated activity recognition systems that use probabilistic models require labeled data sets in training phase for learning the model parameters. The parameters are different for every person and every environment. Therefore, for every person or environment, training is needed to be performed from scratch. Obtaining labeled data requires much effort therefore poses challenges on the large scale deployment of activity recognition systems. Active learning can be a solution to this problem. It is a machine learning technique that allows the algorithm to choose the most informative data points to be annotated. Because the algorithm selects the most informative data points, the amount of the labeled data needed for training the model is reduced. In this study, we propose using active learning methods for activity recognition. We use three different informativeness measures for selecting the most informative data points and evaluate their performances using three real world data sets recorded in a home setting. We show through experiments that the required number of data points is reduced by 80% in House A, 73% in House B, and 66% in House C with active learning.

A robust multimodal fall detection method for ambient assisted living applications

Accidental falls threaten the lives of people over 65 years of age and can be overcome with quick action for saving lives. Old people who live alone and those who have chronic diseases constitute the main risk groups. Fast and effective detection of falls will increase the quality of life of these people. In this study, using accelerometers together with a video sensor, a multi-modal fall detection mechanism is proposed and its performance has been evaluated. The results indicate that an accelerometer triggered video processing method will minimize the processing costs together with privacy related issues.

Multi-modal fall detection within the WeCare framework

Falls are identified as a major health risk for the elderly and a major obstacle to independent living. Considering the remarkable increase in the elderly population of developed countries, methods for fall detection have been a recent active area of research. However, existing methods often use only wearable sensors, such as acceloremeters, or cameras to detect falls. In this demonstration, in contrast to the state of the art solutions, we focus on the use of multi-modal wireless sensor networks within the WeCare framework. WeCare system is developed as a solution for independent living applications by remotely monitoring the health and well-being of its users. We describe the general structure of WeCare and demonstrate its fall detection method. Our set-up not only includes scalar sensors to detect falls and motion but also consists of embedded cameras and RFID tags and uses sensor fusion techniques to improve the success of fall detection and minimize the false positives.

A Unified Model for Human Behavior Modeling Using a Hierarchy with a Variable Number of States

Human behavior modeling enables many applications for smart cities, smart homes, mobile phones and other domains. We present a hierarchical hidden Markov model for human activity recognition that uses semi-supervised learning to automatically learn the model parameters using only labeled data of the top-layer of the hierarchy. This significantly reduces the annotation requirements for such a model and simplifies the design of such a model, since the inherent structure of the activity is automatically learned from data. The design consideration that remains is the number of states used for representing the actions that an activity consists of. Using multiple real world datasets we show that the same model works both for the recognition of activities of daily living in a smart home and for recognizing office activities from audio data. We show how a variable number of action states per activity can result in a significant increase in performance over using a fixed number per activity. Finally, we show how the use of Bayesian and Akaike information criterion results in models using a sub-optimal set of action states, since a model using intuitively chosen set states is able to outperform them.

Multi-resident activity tracking and recognition in smart environments

During last decade, smart homes in which the activities of the residents are monitored automatically have been developed and demonstrated. However, smart homes with multiple residents still remains an open challenge. In order to tackle the multiple resident concurrent activity recognition problem in smart homes equipped with interaction-based sensors and with multiple residents, we propose two different approaches. In the first approach, we use a factorial hidden Markov model for modeling two separate chains corresponding to two residents. Secondly, we use nonlinear Bayesian tracking for decomposing the observation space into the number of residents. As opposed to the previous studies, we handle multiple residents at the same time without assuming any explicit identification mechanisms. We perform two experiments on real-world multi-resident Activity Recognition with Ambient Sensing data sets. In each experiment, we compare the proposed approach with a counterpart method. We also compare each approach with the manually separated observation performances. We show that both of the proposed methods consistently outperform their counterparts in both houses of the data sets and for both residents. We also discuss the advantages and disadvantages of each approach in terms of run time complexity, flexibility and generalizability.

Active learning with uncertainty sampling for large scale activity recognition in smart homes

One of the major problems faced by automated human activity recognition systems is the scalability. Since the probabilistic models employed in activity recognition require labeled data sets for adapting themselves to different users and environments, redeploying these systems in different settings becomes a bottleneck. In order to handle this problem in a cost effective and user friendly way, uncertainty sampling based active learning method is proposed. With active learning, it is possible to reduce the annotation effort by selecting only the most informative data points for annotation. In this paper, three different measures of uncertainty have been used for selecting the most informative data points and their performance have been evaluated by using real world data sets. It has been shown that the annotation effort can be reduced by a factor of two to four, depending on the house and resident settings in an active learning setup.