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Performance enhancement of vision based fall detection using ensemble of machine learning model

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Abstract

An automatic fall detection system (FDS) provides timely medical assistance to the elderly by predicting falls and non-falls, thereby preventing serious injuries and death. This article proposes an ensemble-based FDS focuses on improving the fall detection accuracy by selecting optimal classifiers with a greedy algorithm-based majority voting approach. The proposed ensemble learning is better than any of the individual models because it involves multiple machine learning models along with deep learning model, namely Support Vector Machine, K-Nearest Neighbour, Decision Tree, and Deep LSTM that are combined in majority voting fashion. The greedy algorithm-based majority voting uses three searching criteria, namely forward search, backward search, and recovery search to select the optimal classifiers. The forward search fuses the classifiers based on majority voting error (MVE) and goodness of classifiers, while the backward search removes the classifier based on the majority voting improvement selection criteria. Thus, the classifiers with the lowest MVE and processing time are fused, and the others are removed. Also, the recovery search is introduced to prevent any loss of optimal classifiers by backward search. Finally, classifiers with the best performance are combined to produce accurate fall detection. Then simulation is carried out for the individual ML model, and the proposed ensemble model. According to the evaluation outcome, the proposed ensemble-based FDS achieves high accuracy (99.98%), sensitivity (99.8%), specificity (99.9%), and precision (96.23%) than the conventional approaches. Hence, the proposed ensemble-based FDS proved to enhance fall detection accuracy.

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Authors and Affiliations

  1. Department Of Computer Science, Gd Goenka University, Sohna, Haryana, 122103, India

    Shikha Rastogi & Jaspreet Singh

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  1. Shikha Rastogi
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  2. Jaspreet Singh
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All the authors SR and JS have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

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Correspondence to Shikha Rastogi.

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This article does not contain any studies with human participants and/or animals performed by any of the authors.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shikha Rastogi, Jaspreet Singh. The first draft of the manuscript was written by Shikha Rastogi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: Shikha Rastogi; Methodology: Shikha Rastogi; Formal analysis and investigation: Shikha Rastogi, Jaspreet Singh; Writing—original draft preparation: Shikha Rastogi; Writing—review and editing: Jaspreet Singh; Supervision: Jaspreet Singh.

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Rastogi, S., Singh, J. Performance enhancement of vision based fall detection using ensemble of machine learning model. Cluster Comput 26, 4119–4132 (2023). https://doi.org/10.1007/s10586-022-03818-6

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