title: Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson's disease using multiple inertial sensors.
abstract: BACKGROUND
  Wearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson's disease (PD).
  
  
  METHODS
  A modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms.
  
  
  RESULTS
  Using the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms.
  
  
  CONCLUSIONS
  This study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.
authors.affiliation: Département des Sciences de l'activité Physique, Université du Québec àMontréal, 141 Avenue du Président -Kennedy, Montréal, Québec, Canada.|Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.
authors.forename: Hung
authors.initials: H
authors.lastname: Nguyen
authors.affiliation: Faculty of Medicine and Health Sciences, Department of Surgery, Université de Sherbrooke, Sherbrooke, Québec, Canada.|Research Center on Aging, Sherbrooke, Québec, Canada.|Interdisciplinary Institute for Technological Innovation (3IT), Université de Sherbrooke, Sherbrooke, Québec, Canada.
authors.forename: Karina
authors.initials: K
authors.lastname: Lebel
authors.affiliation: Faculty of Medicine and Health Sciences, Department of Surgery, Université de Sherbrooke, Sherbrooke, Québec, Canada.|Research Center on Aging, Sherbrooke, Québec, Canada.|Interdisciplinary Institute for Technological Innovation (3IT), Université de Sherbrooke, Sherbrooke, Québec, Canada.
authors.forename: Patrick
authors.initials: P
authors.lastname: Boissy
authors.affiliation: Département des Sciences de l'activité Physique, Université du Québec àMontréal, 141 Avenue du Président -Kennedy, Montréal, Québec, Canada.|Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.
authors.forename: Sarah
authors.initials: S
authors.lastname: Bogard
authors.affiliation: Département des Sciences de l'activité Physique, Université du Québec àMontréal, 141 Avenue du Président -Kennedy, Montréal, Québec, Canada.|Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.
authors.forename: Etienne
authors.initials: E
authors.lastname: Goubault
authors.affiliation: Département des Sciences de l'activité Physique, Université du Québec àMontréal, 141 Avenue du Président -Kennedy, Montréal, Québec, Canada. duval.christian@uqam.ca.|Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada. duval.christian@uqam.ca.
authors.forename: Christian
authors.identifier: http://orcid.org/0000-0001-5906-1041
authors.initials: C
authors.lastname: Duval
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods; D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000465:Algorithms; D001696:Biomechanical Phenomena; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D016138:Walking
keywords: Accelerometer; Activity detection; Inertial measurement unit; Movement disorders; Parkinson’s disease; Sitting; Turning; Walking