Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients
المؤلفون المشاركون
Kim, Hee-Cheol
Chakraborty, Sabyasachi
Aich, Satyabrata
Joo, Moon-il
Pradhan, Pyari Mohan
Kim, Hee-Tae
Lee, Hae-Gu
Kim, Il Hwan
Jong Seong, Sim
Park, Jinse
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-18
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases.
As patients suffering from Parkinson’s disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment.
The wearable sensors make the measurement process more convenient as well as feasible in a practical environment.
However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario.
This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system.
It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics.
The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system.
It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%.
The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Aich, Satyabrata& Pradhan, Pyari Mohan& Chakraborty, Sabyasachi& Kim, Hee-Cheol& Kim, Hee-Tae& Lee, Hae-Gu…[et al.]. 2020. Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186211
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Aich, Satyabrata…[et al.]. Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients. Journal of Healthcare Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1186211
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Aich, Satyabrata& Pradhan, Pyari Mohan& Chakraborty, Sabyasachi& Kim, Hee-Cheol& Kim, Hee-Tae& Lee, Hae-Gu…[et al.]. Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186211
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186211
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