Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors
المؤلفون المشاركون
Zemp, Roland
Taylor, William R.
Tanadini, Matteo
Plüss, Stefan
Schnüriger, Karin
Singh, Navrag B.
Lorenzetti, Silvio
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-10-27
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions.
Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user’s sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest).
Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples).
Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification.
The different classification methods were compared by means of a Leave-One-Out cross-validation approach.
The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar.
The classification accuracy varied between 81% and 98% for the seven different sitting positions.
The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses.
The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zemp, Roland& Tanadini, Matteo& Plüss, Stefan& Schnüriger, Karin& Singh, Navrag B.& Taylor, William R.…[et al.]. 2016. Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors. BioMed Research International،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1098307
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zemp, Roland…[et al.]. Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors. BioMed Research International No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1098307
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zemp, Roland& Tanadini, Matteo& Plüss, Stefan& Schnüriger, Karin& Singh, Navrag B.& Taylor, William R.…[et al.]. Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1098307
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1098307
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر