Version 1
: Received: 11 May 2023 / Approved: 12 May 2023 / Online: 12 May 2023 (10:00:16 CEST)
Version 2
: Received: 30 September 2023 / Approved: 1 October 2023 / Online: 1 October 2023 (09:38:25 CEST)
How to cite:
Nandi, P.; Anupama, K.R.; Agarwal, H.; Paliwal, S.; Patel, K.; Bang, V.; Bharat, M.; Guru, M.V. Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device. Preprints2023, 2023050917. https://doi.org/10.20944/preprints202305.0917.v2
Nandi, P.; Anupama, K.R.; Agarwal, H.; Paliwal, S.; Patel, K.; Bang, V.; Bharat, M.; Guru, M.V. Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device. Preprints 2023, 2023050917. https://doi.org/10.20944/preprints202305.0917.v2
Nandi, P.; Anupama, K.R.; Agarwal, H.; Paliwal, S.; Patel, K.; Bang, V.; Bharat, M.; Guru, M.V. Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device. Preprints2023, 2023050917. https://doi.org/10.20944/preprints202305.0917.v2
APA Style
Nandi, P., Anupama, K.R., Agarwal, H., Paliwal, S., Patel, K., Bang, V., Bharat, M., & Guru, M.V. (2023). Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device. Preprints. https://doi.org/10.20944/preprints202305.0917.v2
Chicago/Turabian Style
Nandi, P., Manan Bharat and Madhen Vyas Guru. 2023 "Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device" Preprints. https://doi.org/10.20944/preprints202305.0917.v2
Abstract
Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.
Keywords
Machine learning; Geriartic fall detection; Dataset; Dew Computing; End Device; Feature Extraction; Supervised Machine Learning; Sensor Data Analysis
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
1 October 2023
Commenter:
Purab Nandi
Commenter's Conflict of Interests:
Author
Comment:
Multiple sections of the paper have been updated to give more clarity on the custom END device design to collect the data , extract features and make predictions. An author has been added. The title of the paper has been updated to "Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device."
Commenter: Purab Nandi
Commenter's Conflict of Interests: Author
An author has been added.
The title of the paper has been updated to "Impact of Varying User Characteristics on The Accuracy of Fall Prediction Algorithms Using Custom Wrist-Worn End Device."