title: Walking-age analyzer for healthcare applications.
abstract: This paper describes a walking-age pattern analysis and identification system using a 3-D accelerometer and a gyroscope. First, a walking pattern database from 79 volunteers of ages ranging from 10 to 83 years is constructed. Second, using feature extraction and clustering, three distinct walking-age groups, children of ages 10 and below, adults in 20-60s, and elders in 70s and 80s, were identified. For this study, low-pass filtering, empirical mode decomposition, and K-means were used to process and analyze the experimental results. Analysis shows that volunteers' walking-ages can be categorized into distinct groups based on simple walking pattern signals. This grouping can then be used to detect persons with walking patterns outside their age groups. If the walking pattern puts an individual in a higher "walking age" grouping, then this could be an indicator of potential health/walking problems, such as weak joints, poor musculoskeletal support system or a tendency to fall.
authors.forename: Bo
authors.initials: B
authors.lastname: Jin
authors.forename: Tran Hoai
authors.initials: TH
authors.lastname: Thu
authors.forename: Eunhye
authors.initials: E
authors.lastname: Baek
authors.forename: SungHwan
authors.initials: S
authors.lastname: Sakong
authors.forename: Jin
authors.initials: J
authors.lastname: Xiao
authors.forename: Tapas
authors.initials: T
authors.lastname: Mondal
authors.forename: M Jamal
authors.initials: MJ
authors.lastname: Deen
mesh_terms: D061725:Accelerometry; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000842:Ankle / Q000502:physiology; D002648:Child; D016000:Cluster Analysis; D005684:Gait / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000502:physiology*; D055815:Young Adult