title: A novel method for the automatic segmentation of activity data from a wrist worn device: Preliminary results.
abstract: Activity monitoring is used in a number of fields in order to assess the physical activity of the user. Applications include health and well-being, rehabilitation and enhancing independent living. Data are often gathered from multiple accelerometers and analysis focuses on multi-parametric classification. For longer term monitoring this is unsuitable and it is desirable to develop a method for the precise analysis of activity data with respect to time. This paper presents the initial results of a novel approach to this problem which is capable of segmenting activity data collected from a single accelerometer recording naturalized activity.
authors.forename: James D
authors.initials: JD
authors.lastname: Amor
authors.forename: Vijayalakshmi
authors.initials: V
authors.lastname: Ahanathapillai
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: James
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000203:Activities of Daily Living; D003627:Data Interpretation, Statistical; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D009043:Motor Activity; D014953:Wrist