Parisa Rashidi

A Survey on Ambient-Assisted Living Tools for Older Adults

In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

Discovering Activities to Recognize and Track in a Smart Environment

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individuals routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individuals patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.

Keeping the Resident in the Loop: Adapting the Smart Home to the User

Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the users explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in residents daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home residents patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.

Activity Discovery and Activity Recognition: A New Partnership

Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS data sets.

COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems

The increasing aging population in the coming decades will result in many complications for society and in particular for the healthcare system due to the shortage of healthcare professionals and healthcare facilities. To remedy this problem, researchers have pursued developing remote monitoring systems and assisted living technologies by utilizing recent advances in sensor and networking technology, as well as in the data mining and machine learning fields. In this article, we report on our fully automated approach for discovering and monitoring patterns of daily activities. Discovering and tracking patterns of daily activities can provide unprecedented opportunities for health monitoring and assisted living applications, especially for older adults and individuals with mental disabilities. Previous approaches usually rely on preselected activities or labeled data to track and monitor daily activities. In this article, we present a fully automated approach by discovering natural activity patterns and their variations in real-life data. We will show how our activity discovery component can be integrated with an activity recognition component to track and monitor various daily activity patterns. We also provide an activity visualization component to allow caregivers to visually observe and examine the activity patterns using a user-friendly interface. We validate our algorithms using real-life data obtained from two apartments during a three-month period.

Mining Sensor Streams for Discovering Human Activity Patterns over Time

In recent years, new emerging application domains have introduced new constraints and methods in data mining field. One of such application domains is activity discovery from sensor data. Activity discovery and recognition plays an important role in a wide range of applications from assisted living to security and surveillance. Most of the current approaches for activity discovery assume a static model of the activities and ignore the problem of mining and discovering activities from a data stream over time. Inspired by the unique requirements of activity discovery application domain, in this paper we propose a new stream mining method for finding sequential patterns over time from streaming non-transaction data using multiple time granularities. Our algorithm is able to find sequential patterns, even if the patterns exhibit discontinuities (interruptions) or variations in the sequence order. Our algorithm also addresses the problem of dealing with rare events across space and over time. We validate the results of our algorithms using data collected from two different smart apartments.

Activity knowledge transfer in smart environments

Current activity recognition approaches usually ignore knowledge learned in previous smart environments when training the recognition algorithms for a new smart environment. In this paper, we propose a method of transferring the knowledge of learned activities in multiple physical spaces, e.g. homes A and B, to a new target space, e.g. home C. Transferring the knowledge of learned activities to a target space results in reducing the data collection and annotation period, achieving an accelerated learning pace and exploiting the insights from previous settings. We validate our algorithms using data collected from several smart apartments.

Discovering Temporal Features and Relations of Activity Patterns

An important problem that arises during the data mining process in many new emerging application domains is mining data with temporal dependencies. One such application domain is activity discovery and recognition. Activity discovery and recognition is used in many real world systems, such as assisted living and security systems, and it has been vastly studied in recent years. However, the temporal features and relations which provide useful insights for activity models have not been exploited to their full potential by mining algorithms. In this paper, we propose a temporal model for discovering temporal features and relations of activity patterns from sensor data. Our algorithm is able to discover features and relations, such as the order of the activities, their usual start times and durations by using rule mining and clustering techniques. The algorithm has been validated using 4 months of data collected in a smart home.

Using association rule mining to discover temporal relations of daily activities

The increasing aging population has inspired many machine learning researchers to find innovative solutions for assisted living. A problem often encountered in assisted living settings is activity recognition. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity usually have been ignored by researchers. Temporal features can provide useful insights for building predictive activity models and for recognizing activities. In this paper, we explore the use of temporal features for activity recognition in assisted living settings. We discover temporal relations such as order of activities, as well as their corresponding start time and duration features. To validate our method, we used four months of real data collected from a smart home.

Mining and monitoring patterns of daily routines for assisted living in real world settings

In this paper we demonstrate a fully automated approach for discovering and monitoring patterns of daily activities. Discovering patterns of daily activities and tracking them can provide unprecedented opportunities for health monitoring and assisted living applications, especially for elderly and people with memory deficits. In contrast to most previous systems that rely on either pre-selected activities or labeled data for tracking and monitoring, we use an automated approach for activity discovery and recognition. We present a mining method that is able to find natural activity patterns in real life data, as well as variations of such patterns. We will also show how the discovered patterns can be recognized and monitored by our recognition component. In addition, we provide a visualization component to help the care-givers to better understand the activity patterns and their variations. To validate our algorithms, we use the data collected in two smart apartments.