Brian L. Thomas

CASAS: A Smart Home in a Box

The CASAS architecture facilitates the development and implementation of future smart home technologies by offering an easy-to-install lightweight design that provides smart home capabilities out of the box with no customization or training.

Simple and Complex Activity Recognition through Smart Phones

Due to an increased popularity of assistive healthcare technologies activity recognition has become one of the most widely studied problems in technology-driven assistive healthcare domain. Current approaches for smart-phone based activity recognition focus only on simple activities such as locomotion. In this paper, in addition to recognizing simple activities, we investigate the ability to recognize complex activities, such as cooking, cleaning, etc. through a smart phone. Features extracted from the raw inertial sensor data of the smart phone corresponding to the users activities, are used to train and test supervised machine learning algorithms. The results from the experiments conducted on ten participants indicate that, in isolation, while simple activities can be easily recognized, the performance of the prediction models on complex activities is poor. However, the prediction model is robust enough to recognize simple activities even in the presence of complex activities.

DETECTION OF SOCIAL INTERACTION IN SMART SPACES

The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. An aspect of daily life that is important for ones emotional and physical health is social interaction. In this paper we investigate the use of smart environment technologies to detect and analyze interactions in smart spaces. We introduce techniques for collecting and analyzing sensor information in smart environments to help in interpreting resident behavior patterns and determining when multiple residents are interacting. The effectiveness of our techniques is evaluated using two physical smart environment test beds.

Using smart phones for context-aware prompting in smart environments

Individuals with cognitive impairment have difficulty successfully performing activities of daily living, which can lead to decreased independence. In order to help these individuals age in place and decrease caregiver burden, technologies for assistive living have gained popularity over the last decade. In this work, a context-aware prompting system is implemented, augmented by a smart phone to determine prompt situations in a smart home environment. While context-aware systems use temporal and environmental information to determine context, we additionally use ambulatory information from accelerometer data of a phone which also acts as a mobile prompting device. A pilot study with healthy young adults is conducted to examine the feasibility of using a smart phone interface for prompt delivery during activity completion in a smart home environment.

A demonstration of PyViz, a flexible smart home visualization tool

As smart home technologies are deployed in research and real world environments, there is continuing need for quality visualization. These data come from a variety of sensor sources, artificial intelligence algorithms and human feedback. Building tools that are easily configured, rapidly developed and capable of communicating with both real time and historical data is ever challenging. This work introduces PyViz, a smart home-focused, middle-ware enabled, interactive interface. PyViz is the result of several years of testing and development in a large smart home research project. During its development, issues of configuration, ease of use and adaptability have been explored and overcome. The demonstration of this tool hopes to enlighten the audience about the facets of user interfaces, network agents and smart home complexity.

Context-aware prompting from your smart phone

Individuals with cognitive impairment have difficulty successfully performing activities of daily living, which can lead to decreased independence. In order to help these individuals age in place and decrease caregiver burden, technologies for assistive living have gained popularity over the last decade. This demo illustrates the implementation of a context-aware prompting system augmented by a smart phone to determine prompt situations in a smart home environment. While context-aware systems use temporal and environmental information to determine context, we additionally use ambulatory information from accelerometer data of a phone which also acts as a mobile prompting device.

CARL: activity-aware automation for energy efficiency

Society is becoming increasingly aware of the impact that our lifestyle choices have on energy usage and the environment. This paper explores the hypothesis that ubiquitous computing technologies can be used to understand this impact and to provide activity-aware interventions to reduce energy consumption. Specifically, we introduce a method to provide energy-efficient home automation based on the recognition of activities and their associated devices. We describe CARL (CASAS Activity-based Resource Limitation), a prototype energy-efficient smart home, and evaluate the performance of our activity-aware automation when using both historic and real-time sensor data to drive intelligent home automation.

Activity Learning as a Foundation for Security Monitoring in Smart Homes

Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed.

A Genetic Algorithm approach to motion sensor placement in smart environments

Smart environments and ubiquitous computing technologies hold great promise for a wide range of real-world applications. The medical community is particularly interested in high-quality measurement of activities of daily living. With accurate computer modeling of older adults, decision support tools may be built to assist care providers. One aspect of effectively deploying these technologies is determining where the sensors should be placed in the home to effectively support these end goals. This work introduces and evaluates a set of approaches for generating sensor layouts in the home. These approaches range from the gold standard of human intuition-based placement to more advanced search algorithms, including Hill Climbing and Genetic Algorithms. The generated layouts are evaluated based on their ability to detect activities while minimizing the number of needed sensors. Sensor-rich environments can provide valuable insights about adults as they go about their lives. These sensors, once in place, provide information on daily behavior that can facilitate an aging-in-place approach to health care.