دورية أكاديمية
أعرض في EDS

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

التفاصيل البيبلوغرافية
العنوان: Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.
المؤلفون: Zimbelman EG; Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID, United States of America., Keefe RF; Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID, United States of America.
المصدر: PloS one [PLoS One] 2021 May 12; Vol. 16 (5), pp. e0250624. Date of Electronic Publication: 2021 May 12 (Print Publication: 2021).
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't; Validation Study
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Accelerometry/*methods , Forestry/*methods , Human Activities/*statistics & numerical data , Machine Learning/*standards , Wearable Electronic Devices/*standards, Humans ; Idaho
مستخلص: Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.
Competing Interests: The authors have declared that no competing interests exist.
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معلومات مُعتمدة: U54 OH007544 United States OH NIOSH CDC HHS
تواريخ الأحداث: Date Created: 20210512 Date Completed: 20211102 Latest Revision: 20230301
رمز التحديث: 20230301
مُعرف محوري في PubMed: PMC8115790
DOI: 10.1371/journal.pone.0250624
PMID: 33979355
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0250624