Evaluation of Generic Shape Contoured cushion for Wheelchair Users
Based on the
Development of Body-Seat Interface Shape Measurement Instrument
Yue Li
Planification et analyse statistique d'exp é riences
MTH6301 April 21, 2001
Presentation Structure
• Problem & Objective
• Methodology
— Description of input variables
— Description of response variables
— Choice of design
• Statistical analysis
Problem & Objective (1)
Pressure &
deformation of the body-seat interface
Internal characteristics of the wheelchair user
External characteristics of the wheelchair user
Type of cushion
Posture
Moisture
Tissue stiffness
Gender Nutrition
Weight Height
Skin temperature
Age
Type of disability
Time spending on the wheelchair
Leg-rest angle Seat angle Backrest angle
Foot-rest angle Material
Shape Active or static
ã ã General objective: Minimum the pressure and deformation
Problem & Objective (2)
1. Average pressure 2. Maximum pressure 3. Peak pressure gradient 4. Maximum deformation
Slump posture Forward trunk flexion posture
Mid-line posture Flat foam cushion
ISCUS cushion Generic shape contoured cushion
ã ã Specific objective: Minimum the 4 response variables &
confirm that Generic shape contoured cushion (GSCC) is better
Posture
Wheelchair configuration
Type of cushion
Problem & Objective (3)
Example of Generic shapes
Methodology (1)
Description of input variables
• Controllable input variables
— Type of cushion: 3 levels
1. Flat foam cushion
2. ISCUS cushion (Orthofab Inc, Montreal)
3. Generic shape contoured cushion (developing in the project)
— Posture: 3 levels
1. Mid-line posture
2. Forward trunk flexion posture 3. Slump posture
• Uncontrollable variable
— Body type of the subject (thin, average, obese)
Methodology (2)
Description of response variables
1. Average pressure 2. Maximum pressure
3. Peak pressure gradient
4. Maximum deformation
ð Pressure measurement:
Force Sensing Array (FSA) pad (Vista Medical, Manitoba, Canada)
ð Deformation measurement:
Shape Sensing Array (SSA) pad (developing from Shape Tape - Measurand Inc, Fredericton, NB, Canada)
IB 1 IB 2 IB 3 IB 4 IB 5
IB 7 IB 8 IB 6
Computer
Methodology (3)
Choice of design
Constraints:
Number of subjects ( ≤ 20)
Number of maximum treatments per subject: 4
• 3
2full factorial design è è 9 treatments per subject x
• Central composite design: quantitative variable only x
• Balanced incomplete block design [[
t = Number of treatments = 9
b = Number of blocks = 18 (number of subject)
k = Block size = 4
r = Number of replicates of each treatment = 8
N = tr = bk = total number of observations = 72
Methodology (4)
Balanced incomplete block design
• Each subject represents one block
• Four treatments per subject
• Treatments are based on a full factorial design (3
2= 9 treatments)
• Three repeat measurements
•