Automated Assessment of Mobility in Bedridden Patients Advisor: Dr. Chun-Ju Hou Presenter: Si-Ping Chen Date:2014/12/10 35th Annual International Conference of the IEEE EMBS Osaka, Japan, July, 2013 Stephanie Bennett, Member, IEEE, Rafik Goubran, Fellow, IEEE, Kenneth Rockwood, and Frank Knoefel.

Outline Introduction Methods Results Conclusions 2

Introduction Prevalence of population aging 3

Introduction The impact of population aging on society 。 National financial load increase 。 Declining economic growth 。 Political attention to the elderly-related policies 。 Business and consumer behavior change 。 Adjustment of the real estate industry 。 Shift the focus of education 4

Introduction Geriatric giants –The major categories of impairment that appear in elderly people, especially as they begin to fail. These include Immobility Instability Incontinence Impaired intellect/memory 5

Introduction The Hierarchical Assessment of Balance and Mobility (HABAM) –Balance –Transfers –Mobility HABAM tools –Bedridden patients –Mildly functional impaired patients 6

Introduction Problems –Spend time on manually assessing HABAM   The quality of healthcare –Not currently used enough by hospital staff to be informative –  Population aging  Overloading health resources and staff Automatic and computerized measurement –Accelerometers and pressure sensors Sleep pattern, gait pattern, pressure ulcer, smart home environment 7

Purpose The goal of this work is often not only to automatic, but improve current health measurements particularly to impaired mobility and immobility. 8

Methods Equipment and Set-up –A laptop –A video camera –Three pressure sensitive mats manufactured by S4 sensors –Software 9

Methods Equipment and Set-up –Sensor captures this data at sampling rate of 10Hz. –The data generated by the mats is sent via Bluetooth to a laptop. 10

Methods Equipment and Set-up –Constructed of four panels: 。 One large panel supporting the back. 。 Two small panels at the sacrum. 。 An intermediate sized panel supporting the legs. 11 Mat 80cm 25cm

Methods Equipment and Set-up 12

Methods Experimental Procedure –Data were collected from the mattress alone and five volunteers performing entirely in-bed enactments of HABAM scores. –HABAM scores: 。 Score 0: Needs positioning in bed. 。 Score 4: Positions self in bed. 。 Score 7: Lying-sitting independently. 13

Methods Data Analysis –Sums of sensors scores over time –Algorithms: HABAM score enactments Subsystem 1: eliminated the weight of the mattress from all volunteer performed enactment. Subsystem 2: determined if the volunteer was enacting a score of 0, or a score of 4. Subsystem 3: determined if the volunteer was enacting a score of 7. 14

Methods Subsystem 1: ‘zero-ed’ data 15 Average mattress pressure

Methods 16 Left Right

Methods Subsystem 2 4.Points in time at which a sensor recorded a percentage decrease of or less were recorded, least two sensors had simultaneously dropped below a percentage change of Left Right Score 4( Positions self in bed ) Left Right -0.95X Score 0( Needs positioning in bed )

Methods 18

Results Subsystem 2 determined if the enactment was either of score 0: needs positioning in bed, or score 4: can position self in bed. –Relief of three sensors under the left hip during enactment 19

Results Location of relieved sensors in the mat. 20

Results Subsystem 3 determined if the enactment was of score 7:lying-sitting. This was done by calculating the sums, then ratios of the top mat and the bottom mat at every point in time. 21

Results Top over bottom mat ratio for scenario 3. 22

Results Overall results of the system can be observed in Table I. 23

Conclusions This paper aimed to automate a volunteer-based, partial HABAM assessment. Five volunteers performed three enactments each, on a standard hospital bed while pressure data was gathered from pressure mats underneath a hospital mattress. 24

Conclusions Subsystem 1 : ‒ To identify and distinguish between a subject in a sitting or lying position. Subsystem 2: –For expansion to include examination of pressure points and associated patterns underneath a subject during HABAM enactments. 25

Conclusions Data revealed that the system had not assessed incorrectly. This system, with relative engineering simplicity, was able to better assess HABAM scores than an observing researcher. HABAM –Emphasize the importance of pervasive computing in the assessment and tracking of immobility. 26

27 Thanks for your attention