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

Joint Authors

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

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-02-18

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Public Health
Medicine

Abstract 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.

American Psychological Association (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

Modern Language Association (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

American Medical Association (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

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186211