Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors
المؤلفون المشاركون
Khan, Muhammad Amir
Jadoon, Waqas
Khan, Iftikhar Ahmed
Khan, Usman Ali
Din, Ahmad
Jadoon, Rab Nawaz
Khan, Fiaz Gul
Khan, Abdul Nasir
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-22
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Smartphones with gym exercises predictors can act as trainers for the gym-goers.
However, various available solutions do not have the complete set of most practiced exercises.
Therefore, in this research, a complete set of most practiced 26 exercises was identified from the literature.
Among the exercises, 14 were unique and 12 were common to the existing literature.
Furthermore, finding suitable smartphone attachment position(s) and the number of sensors to predict exercises with the highest possible accuracy were also the objectives of the research.
Besides, this study considered the most number of participants (20) as compared to the existing literature (maximum 10).
The results indicate three key lessons: (a) the most suitable classifier to predict a class (exercise) from the sensor-based data was found to be KNN (K-nearest neighbors); (b) the sensors placed at the three positions (arm, belly, and leg) could be more accurate than other positions for the gym exercises; and (c) accelerometer and gyroscope when combined can provide accurate classification up to 99.72% (using KNN as classifier at all 3 positions).
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Khan, Usman Ali& Khan, Iftikhar Ahmed& Din, Ahmad& Jadoon, Waqas& Jadoon, Rab Nawaz& Khan, Muhammad Amir…[et al.]. 2020. Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors. Scientific Programming،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1209067
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Khan, Usman Ali…[et al.]. Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors. Scientific Programming No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1209067
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Khan, Usman Ali& Khan, Iftikhar Ahmed& Din, Ahmad& Jadoon, Waqas& Jadoon, Rab Nawaz& Khan, Muhammad Amir…[et al.]. Towards a Complete Set of Gym Exercises Detection Using Smartphone Sensors. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1209067
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209067
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