TY - JOUR
T1 - Validity of using tri-axial accelerometers to measure human movement - Part II
T2 - Step counts at a wide range of gait velocities
AU - Fortune, Emma
AU - Lugade, Vipul
AU - Morrow, Melissa
AU - Kaufman, Kenton
N1 - Funding Information:
Funding was provided by DOD DM090896 , NIH T32 HD07447 , and NIH K12 HD065987 . The body-worn motion detection and recording units were provided by Dr. Barry Gilbert, James Bublitz, Kevin Buchs, Charles Burfield, Christopher Felton, Dr. Clifton Haider, Michael Lorsung, Shaun Schreiber, Steven Schuster, and Daniel Schwab from the Mayo Clinic Special Purpose Processor Development Group. The information or content and conclusions do not necessarily represent the official position of, nor should any official endorsement be inferred by the National Institutes of Health, the United States Navy, the Department of Defense, or the U.S. Government.
PY - 2014/6
Y1 - 2014/6
N2 - A subject-specific step counting method with a high accuracy level at all walking speeds is needed to assess the functional level of impaired patients. The study aim was to validate step counts and cadence calculations from acceleration data by comparison to video data during dynamic activity. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, thigh, and waist of 11 healthy adults. ICC values were greater than 0.98 for video inter-rater reliability of all step counts. The activity monitoring system (AMS) algorithm demonstrated a median (interquartile range; IQR) agreement of 92% (8%) with visual observations during walking/jogging trials at gait velocities ranging from 0.1 to 4.8 m/s, while FitBits (ankle and waist), and a Nike Fuelband (wrist) demonstrated agreements of 92% (36%), 93% (22%), and 33% (35%), respectively. The algorithm results demonstrated high median (IQR) step detection sensitivity (95% (2%)), positive predictive value (PPV) (99% (1%)), and agreement (97% (3%)) during a laboratory-based simulated free-living protocol. The algorithm also showed high median (IQR) sensitivity, PPV, and agreement identifying walking steps (91% (5%), 98% (4%), and 96% (5%)), jogging steps (97% (6%), 100% (1%), and 95% (6%)), and less than 3% mean error in cadence calculations.
AB - A subject-specific step counting method with a high accuracy level at all walking speeds is needed to assess the functional level of impaired patients. The study aim was to validate step counts and cadence calculations from acceleration data by comparison to video data during dynamic activity. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, thigh, and waist of 11 healthy adults. ICC values were greater than 0.98 for video inter-rater reliability of all step counts. The activity monitoring system (AMS) algorithm demonstrated a median (interquartile range; IQR) agreement of 92% (8%) with visual observations during walking/jogging trials at gait velocities ranging from 0.1 to 4.8 m/s, while FitBits (ankle and waist), and a Nike Fuelband (wrist) demonstrated agreements of 92% (36%), 93% (22%), and 33% (35%), respectively. The algorithm results demonstrated high median (IQR) step detection sensitivity (95% (2%)), positive predictive value (PPV) (99% (1%)), and agreement (97% (3%)) during a laboratory-based simulated free-living protocol. The algorithm also showed high median (IQR) sensitivity, PPV, and agreement identifying walking steps (91% (5%), 98% (4%), and 96% (5%)), jogging steps (97% (6%), 100% (1%), and 95% (6%)), and less than 3% mean error in cadence calculations.
KW - Accelerometer
KW - Body-worn sensors
KW - Gait velocity
KW - Step detection
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U2 - 10.1016/j.medengphy.2014.02.006
DO - 10.1016/j.medengphy.2014.02.006
M3 - Article
C2 - 24656871
AN - SCOPUS:84900791913
SN - 1350-4533
VL - 36
SP - 659
EP - 669
JO - Medical Engineering and Physics
JF - Medical Engineering and Physics
IS - 6
ER -