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.; Patel, K.; Bang, V.; Bharat, M.; Guru, M.V. Analysis of ML Algorithm for Geriatric Fall Detection Due to the Effects of Various User Characteristics. Preprints2023, 2023050917. https://doi.org/10.20944/preprints202305.0917.v1
Nandi, P.; Anupama, K.R.; Agarwal, H.; Patel, K.; Bang, V.; Bharat, M.; Guru, M.V. Analysis of ML Algorithm for Geriatric Fall Detection Due to the Effects of Various User Characteristics. Preprints 2023, 2023050917. https://doi.org/10.20944/preprints202305.0917.v1
Nandi, P.; Anupama, K.R.; Agarwal, H.; Patel, K.; Bang, V.; Bharat, M.; Guru, M.V. Analysis of ML Algorithm for Geriatric Fall Detection Due to the Effects of Various User Characteristics. Preprints2023, 2023050917. https://doi.org/10.20944/preprints202305.0917.v1
APA Style
Nandi, P., Anupama, K.R., Agarwal, H., Patel, K., Bang, V., Bharat, M., & Guru, M.V. (2023). Analysis of ML Algorithm for Geriatric Fall Detection Due to the Effects of Various User Characteristics. Preprints. https://doi.org/10.20944/preprints202305.0917.v1
Chicago/Turabian Style
Nandi, P., Manan Bharat and Madhen Vyas Guru. 2023 "Analysis of ML Algorithm for Geriatric Fall Detection Due to the Effects of Various User Characteristics" Preprints. https://doi.org/10.20944/preprints202305.0917.v1
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; K Nearest Neighbours; Naive Bayes; Logistic Regression; Random Forest; Support Vector Machine
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.
