Measuring Gait and Balance

Measurements of gait and balance are used both for detection of medical conditions and for tracking progress of treatments. For the older population, elevated risk of fall due to balance issues present major risk while for patients with neurological diseases, gait is a marker for the progress of the disease and the success of the treatment.

 

A workshop about this topic was held on May 23rd 2014 hosted by Microsoft Research.  If you are interested in information about the event, please contact rang@microsoft.com.

Most of the talks were recorded and the video recording can be found here.

Meeting agenda:

9:30-9:45

Ran Gilad-Bachrach

Microsoft Research

Opening

9:45-10:30

Kat Steele

University of Washington

New tools and techniques for clinical gait analysis

10:30-11:00

Eric Horvitz

Microsoft Research

Welcome to Microsoft Research

11:00-11:45

Shomir Chaudhuri

University of Washington

The Perceived Usability of Fall Detection Devices for Older Adults

11:45-13:00

Lunch Break

13:00-13:45

Erienne Olesh

West Virginia University

Quantifying post-stroke impairment with Kinect

13:45-14:30

Chris White

Microsoft

Kinect V2

14:30-14:45

Break

14:45-15:30

Kushang Patel

University of Washington

Chronic Pain, Mobility Function, and Physical Activity in Older Adults

15:30-16:15

Ken Kubota

Michael J. Fox Foundation

The Objective Measures of Parkinson’s disease challenges and directions

 

16:15-17:00

Erik Edward Stone

University of Missouri

Eldercare and Rehabilitation Technologies Using the Microsoft Kinect

17:00-17:15 

Ran Gilad-Bachrach

Microsoft Research 

Closing 

Talks outline:

New tools and techniques for clinical gait analysis

Kat Steele / University of Washington

Abstract: Instrumented clinical gait analysis has become a standard part of clinical care for the diagnosis and treatment of neurological disorders. Gait analysis provides a wealth of quantitative data; however, interpreting the deluge of data and making patient-specific treatment decisions remains challenging. Additionally, traditional gait analysis systems are prohibitively expensive and limited to ‘in lab’ conditions that may not reflect mobility and participation in daily life. We will discuss new tools and techniques, including dynamic musculoskeletal simulation, imaging, and ubiquitous computing that have the potential to improve patient-specific treatment planning and extend analysis of human movement outside of the lab.

Bio: Kat Steele is an assistant professor in mechanical engineering at the University of Washington. Her research focuses on integrating dynamic simulation, motion analysis, medical imaging, and device design to improve mobility for individuals with neurological disorders. She earned her BS in Engineering from the Colorado School of Mines and MS and PhD in Mechanical Engineering from Stanford University. To integrate engineering and medicine, she has worked extensively in hospitals including the Cleveland Clinic, Denver Children’s Hospital, Lucile Packard Children’s Hospital, and the Rehabilitation Institute of Chicago. She is also an expert user and developer of the free, open-source software platform for dynamic simulation of movement, OpenSim. More information about Dr. Steele’s research and the Ability Lab is available at: http://faculty.washington.edu/kmsteele/

The Perceived Usability of Fall Detection Devices for Older Adults

Shomir Chaudhuri / University of Washington

Abstract: A third of adults over the age of 65 are estimated to fall at least once a year. Perhaps more dangerous than the fall itself is the time spent after a fall, especially if the fallen person is unable to stand or move. While there are many devices available to detect when a person has fallen, little is known about the opinions of older adults regarding these devices. To explore this issue, we conducted 5 focus groups at 3 different older adult communities with 27 participants. Transcripts were coded to generate themes that captured participants’ overall perceptions. In this presentation, I will discuss issues and concerns with current fall detection systems and suggest improvements based on participants' feedback.

Bio: I am currently an NLM Predoctoral Fellow in the 4th year of my PhD training in Biomedical Informatics and Medical Education. I am working with Dr. George Demiris and the Health-E Team on developing informatic solutions for older adults. My dissertation topic concerns the usability of fall detection devices specifically for adults over the age of 65.

Quantifying post-stroke impairment with Kinect

Erienne Olesh / West Virginia University

Abstract: Current methods for assessing post-stroke motor impairment rely on subjective measurements by physical therapists. In order to improve rehabilitation practices and provide more detailed information regarding post stroke motor impairment, objective measurement tools are needed. To address this need, we have developed an algorithm that automatically quantifies post-stroke motor impairment from kinematics recorded by the Kinect sensor. We will show the findings that compare the performance of this algorithm relative to the trained physical therapists.

Bio: Erienne Olesh is a third year graduate student in the Center for Neuroscience at West Virginia University. Her research focus is to identify the underlying changes that occur in motor control after a stroke that lead to movement impairment. Furthermore, she is working to improve rehabilitation methods by developing a quantitative and automated method of movement assessment. Her undergraduate work was completed at the University of Puget Sound where she majored in Exercise Physiology. Her research focus during her undergraduate work was to characterize foot morphological changes that occurred after long term walking.

Kinect V2

Chris White / Microsoft

Abstract: This talk will go over the technical capabilities of the Kinect v2 sensor, and introduce a number of relevant examples where Kinect is being used by businesses in the healthcare field.

Bio: Chris White is a Senior Program Manager with Microsoft, and owns the Kinect for Windows SDK and the program to bring the Kinect V2 sensor to Windows. He has been at Microsoft since 1999, starting as a developer in Office. In 2006, Chris moved to the Program Management discipline, and joined a startup inside Microsoft focused on Health Care. He launched the first versions of the HealthVault personal health platform, and of HealthVault Community Connect, before diving down to drive the application platform architecture for Microsoft’s Amalga platform, an enterprise level application platform for hospitals and other Health Systems. In 2012, Chris joined Kinect and is currently plugging away at teaching computers to see and hear and understand everything in the world around them. In his “spare” time, Chris generally stays ridiculously busy. He has studied animation (2 of his student videos have over 1 MM hits on youtube), written digital synthesizers, done research in audio analysis with a focus on automatic DJ software. He is also an elite competitive ultimate player, a DJ, a Burner, and an installation artist. One of his pieces (The Groovik’s Cube) is currently on display at the Liberty Science Center, at the beginning of its 7 year, 30 museum world tour.

Chronic Pain, Mobility Function, and Physical Activity in Older Adults

Kushang V. Patel / University of Washington

Abstract: Chronic pain is a common condition in the older adult population and is the most widely cited cause of disability. We are currently investigating the role of pain in performance-based assessments of mobility function and objective measures of physical activity among older adults. Results from these population-based studies will be discussed.

Bio: Kushang Patel is a Research Assistant Professor of Anesthesiology and Pain Medicine at the University of Washington. His research investigates (1) the epidemiology of pain and aging using community-based and national data sources; (2) outcomes assessment in clinical trials and observational studies; and (3) interventions to improve pain management and function, particularly in older adults. Prior to moving to Seattle in 2012, he worked at the NIH, carrying out epidemiologic research on aging.

The Objective Measures of Parkinson’s disease challenges and directions

Ken Kubota / Michael J. Fox Foundation

Abstract: The talk will cover the cardinal motor symptoms of Parkinson’s disease including gait and the challenges measuring them.

Bio: Ken Kubota is the new Director of Data Science at the Michael J Fox Foundation, hired September of last year. He comes to the foundation after having managed the Kinetics Foundation, a privately endowed PD philanthropy founded by the former CEO of Intel, Andy Grove, for 10 years. Cofounder of a software company, SyntheticLink and a former PM of MSTV, director of TechNet Events and Systems Engineer for the Fareast in Advanced Technology Sales at the Microsoft Corporation. Ken is an alumni of the Stanford Graduate School of Business and has a BS in Electrical Engineering and Computer Science at the University of California at Berkeley.

Eldercare and Rehabilitation Technologies Using the Microsoft Kinect

Erik Edward Stone/ University of Missouri

Abstract: Release of the Microsoft Kinect sensor in 2010 opened the door to development of new tools and technologies requiring an inexpensive sensor that can generate accurate three-dimensional representations of people and the environment. This talk will discuss three such tools developed at the Center for Eldercare and Rehabilitation Technology which are now in use in real-world settings such as older adult’s homes and hospital rooms. These include a system for in-home gait analysis and fall risk assessment, a system for in-home fall detection, and a system for screening high school female athletes for increased risk of ACL injury. Results from laboratory validation experiments and real world deployments will be presented and issues encountered will be discussed.

Bio: Erik Stone is a postdoctoral fellow in the Center for Eldercare and Rehabilitation Technology at the University of Missouri - Columbia. He received the B.S. degree in Electrical and Computer Engineering, the M.S. degree in Computer Engineering, and the Ph.D. degree in Electrical and Computer Engineering from the University of Missouri – Columbia in 2006, 2009, and 2013, respectively. His research interests include computer vision, machine learning, and pattern recognition.