ARTICLE | doi:10.20944/preprints202305.0917.v2
Subject: Engineering, Electrical And Electronic Engineering Keywords: Machine learning; Geriartic fall detection; Dataset; Dew Computing; End Device; Feature Extraction; Supervised Machine Learning; Sensor Data Analysis
Online: 1 October 2023 (09:38:25 CEST)
ARTICLE | doi:10.20944/preprints202306.0804.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Fall Detection; Data Pruning; Bagging; Boosting; Voting; Stacking
Online: 12 June 2023 (09:00:42 CEST)
Falls, especially those left unattended, are fatal for the elderly. Several efforts have been made to use the Internet of Things and Machine Learning algorithms to detect falls. All such systems have issues such as (a) sensor placement on the torso and thigh which will be uncomfortable for the elderly. (b) Predictions made on the cloud- the result of the prediction is then sent back to the end device to raise an alert. This is prone to network connectivity and latency issues. We have built an end/device that is wrist-worn and has multiple Inertial Measurement Unit sensors and a heart sensor. We have developed three novel ensemble algorithms (a)Stack(A) (b) Variable weighted Ensemble Voting algorithm-B(VWE(B)), and (c) Variable weighted Ensemble Voting algorithm-C (VWE(C)). Since the ensemble algorithm is run on the end-device built around Qualcomm Snapdragons 820c, we do both feature extraction and selection to reduce data dimensionality. We have used multiple methods such as (a) Identifying features that have maximum impact (b) Principal Component Analysis (PCA) (c) Shapley’s values (d) Cross-Correlation combined with relliefF. We got an accuracy of 97% and specificity of 99%. In this paper, we present the analysis of the system with and without pruning.