title: Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.
abstract: An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.
authors.affiliation: The Ottawa Hospital Research Institute, University of Ottawa; elemaire@toh.on.ca.
authors.forename: Edward D
authors.initials: ED
authors.lastname: Lemaire
authors.affiliation: Department of Mechanical Engineering, University of Ottawa.
authors.forename: Marco D
authors.initials: MD
authors.lastname: Tundo
authors.affiliation: Department of Mechanical Engineering, University of Ottawa.
authors.forename: Natalie
authors.initials: N
authors.lastname: Baddour
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D004777:Environment; D005260:Female; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009043:Motor Activity; D011187:Posture; D000068997:Smartphone / Q000295:instrumentation*