title: Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors.
abstract: BACKGROUND
  Recently, much attention has been given to the use of inertial sensors for remote monitoring of individuals with limited mobility. However, the focus has been mostly on the detection of symptoms, not specific activities. The objective of the present study was to develop an automated recognition and segmentation algorithm based on inertial sensor data to identify common gross motor patterns during activity of daily living.
  
  
  METHODS
  A modified Time-Up-And-Go (TUG) task was used since it is comprised of four common daily living activities; Standing, Walking, Turning, and Sitting, all performed in a continuous fashion resulting in six different segments during the task. Sixteen healthy older adults performed two trials of a 5 and 10 meter TUG task. They were outfitted with 17 inertial motion sensors covering each body segment. Data from the 10 meter TUG were used to identify pertinent sensors on the trunk, head, hip, knee, and thigh that provided suitable data for detecting and segmenting activities associated with the TUG. Raw data from sensors were detrended to remove sensor drift, normalized, and band pass filtered with optimal frequencies to reveal kinematic peaks that corresponded to different activities. Segmentation was accomplished by identifying the time stamps of the first minimum or maximum to the right and the left of these peaks. Segmentation time stamps were compared to results from two examiners visually segmenting the activities of the TUG.
  
  
  RESULTS
  We were able to detect these activities in a TUG with 100% sensitivity and specificity (n = 192) during the 10 meter TUG. The rate of success was subsequently confirmed in the 5 meter TUG (n = 192) without altering the parameters of the algorithm. When applying the segmentation algorithms to the 10 meter TUG, we were able to parse 100% of the transition points (n = 224) between different segments that were as reliable and less variable than visual segmentation performed by two independent examiners.
  
  
  CONCLUSIONS
  The present study lays the foundation for the development of a comprehensive algorithm to detect and segment naturalistic activities using inertial sensors, in hope of evaluating automatically motor performance within the detected tasks.
authors.affiliation: Département de Kinanthropologie, Université du Québec à Montréal, C.P. 8888, succursale Centre-Ville, Montréal, H3C 3P8, Québec, Canada. hpnguyen@utexas.edu.
authors.forename: Hung P
authors.initials: HP
authors.lastname: Nguyen
authors.affiliation: Département de Kinanthropologie, Université du Québec à Montréal, C.P. 8888, succursale Centre-Ville, Montréal, H3C 3P8, Québec, Canada. fouazayachi@gmail.com.
authors.forename: Fouaz
authors.initials: F
authors.lastname: Ayachi
authors.affiliation: Département de Kinanthropologie, Université du Québec à Montréal, C.P. 8888, succursale Centre-Ville, Montréal, H3C 3P8, Québec, Canada. lavigne-pelletier.catherine@courrier.uqam.ca.
authors.forename: Catherine
authors.initials: C
authors.lastname: Lavigne-Pelletier
authors.affiliation: Faculté des Sciences, Université du Québec à Montréal, Montreal, Canada. margaux.blamoutier@gmail.com.
authors.forename: Margaux
authors.initials: M
authors.lastname: Blamoutier
authors.affiliation: Electrical Engineering Department, University of Bonab, Bonab, East Azerbaijan, Iran. fariborz.rahimi@gmail.com.
authors.forename: Fariborz
authors.initials: F
authors.lastname: Rahimi
authors.affiliation: Department of Surgery, Orthopaedic Division, Faculty of Medicine and Health Sciences, Université of Sherbrooke, Quebec, Canada. Patrick.Boissy@USherbrooke.ca.
authors.forename: Patrick
authors.initials: P
authors.lastname: Boissy
authors.affiliation: London Health Sciences Center University Hospital, Ontario, Canada. Mandar.Jog@lhsc.on.ca.
authors.forename: Mandar
authors.initials: M
authors.lastname: Jog
authors.affiliation: Département de Kinanthropologie, Université du Québec à Montréal, C.P. 8888, succursale Centre-Ville, Montréal, H3C 3P8, Québec, Canada. duval.christian@uqam.ca.
authors.forename: Christian
authors.initials: C
authors.lastname: Duval
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000465:Algorithms; D001696:Biomechanical Phenomena; D005260:Female; D006801:Humans; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D012680:Sensitivity and Specificity