QoLT.org > Research > Research Areas

Research Areas

In keeping with its vision and families of engineered systems, the QoLT Center has organized its fundamental work into four interrelated research areas grounded in more traditional disciplinary research domains.

[Human-System Interaction] [Mobility and Manipulation] [Perception and Awareness] [Person and Society]

Human-System Interaction (HSIT)
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HSIT directly supports the QOLT Center focus on transformative research and products by addressing a major research challenge: dynamically adjustable human-system interfaces. These types of interfaces use data to determine human state and dynamically adjust an interface to account for any changes. HSIT research, when integrated into transformative product families, reduces the labor needed to deploy technology, address issues of complexity, and increase the availability of care while addressing the remoteness of the immediate family. HSIT has identified what it calls high-stress transitions, or cusps, in life where technology can be particularly helpful in retaining independence. Examples of cusps include cognitive or physical changes due to accident or aging and moving into a managed care environment. To make useful products for these times in life, HSIT is exploring

Logging and inferring for activity sensing

Authoring of individualized plans

Presentation of information

Diagnosis of user capabilities

Personal Agency for information technology access

Automatic QoLT synthesis

Autonomic self-management

Brain control interface

Mobility and Manipulation (MMT)
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A unifying theme for MMT is the provision of safe mobility and manipulation for users, caregivers, their environments, and modes of failure of assistive devices. This is a big challenge for QoLT.

MMT research is focused on

Planning Algorithms: New approaches to planning and control are a key component to improving autonomy of assistive devices. Algorithms must be real-world-ready so they reliably work in natural environments. They also need to function properly based on real-time sensor data input rather than models of a simulated world.

Personal Safety Systems: We have an opportunity to monitor for risky behavior that leads to accidents and falls in home manipulation as well as unassisted and assisted locomotion, and act to change behavior to reduce risk. MMT projects bring together expertise in human behavior as well as a robotics perspective to analyze accidents. We are developing simulation and robotic models of how accidents occur, and what can be done to reduce their occurrence and their effect.

Soft Manipulation: Soft physical interaction with humans is a relatively undeveloped area. No current humanoid robots are back-drivable, for example, and those that implement soft physical interaction do so only at selected sites using contact force sensing. Soft manipulation of fragile humans is a new challenging area for robotics, and we intend to pioneer this area.

Perception and Awareness (PAT)
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PAT is organized around three guiding principles for perception technology

User Centric: Perception technology must concentrate on modeling the user, her intent, and interaction with the environment. This leads to new ways of designing sensory systems by emphasizing acquiring data from the user's perspective, for which we have coined the term "inside-out" perception.

Data-driven learning: Models used for extracting intent prediction from perceptual data must be learned from data acquired from the user and from external data. This leads to new research in designing learning algorithms that can support and utilize large data sets.

Multiple data sources: A QoLT perception system must be able to combine data from different sensors, including cameras, motion sensors, microphones, other wearable sensors, tactile sensors and direct interfaces to the brain and other parts of the nervous system. In addition, it must be able to combine user-specific data acquired on-line from the user's perspective with prior data acquired off-line from a large number of users, such as data acquired in instrumented environments. Specifically, it must be able to combine information that is specific to the user's cognitive behavior (e.g., as indicated by the typical order of actions he takes to execute a task) with prior knowledge (e.g., motion models from observations of a large population of people performing the same task).

Research in the PAT is organized around three main themes: 1) Sensing technology that is specifically designed to acquire the perceptual data that are most directly relevant to the ultimate goal of understanding user intent; 2) Data-driven perception technology for identifying objects, places and events in perceptual data; 3) Learning techniques for extracting models from very large data sets that combine data acquired from the user's perspective combined with prior data from external sources, and for generating predictions from observations. These three themes are mapped onto projects that correspond to a natural progression from data, to intermediate interpretation from sensor data (objects, places, and events), to high-level interpretation (activity association and intent prediction).

Person and Society (PST)
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PST has three primary goals, to:

1) provide core services critical to the success of the technical thrusts and engineering applications of the Center

2) promote a complimentary scientific agenda aimed at developing and implementing evaluation methodologies for assessing the needs, acceptability and impact of QoLT

3) implement models of participatory action design and infusion in the education of engineers and other individuals from other disciplines.

Achieving these goals requires that we develop, populate and disseminate multiple data bases to support and shape Quality of Life Technology development, and that we work closely with the engineering thrusts to ensure their timely and appropriate application.