Predicting Falls using Time Series Data from the Equilivest Device

"Falls pose a significant health risk to older adults, often resulting in serious injuries and reduced independence. This study explored two machine learning approaches for detecting falls using data from wearable sensors: a supervised binary classification approach trained on labeled fall and...

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Autor principal: Rejzi, Ledion
Formato: Tesis de maestría
Lenguaje:Inglés
Publicado: 2025
Materias:
FALL DETECTION, BINARY CLASSIFICATION, ANOMALY DETECTION, SUPERVISED LEARNING, UNSUPERVISED LEARNING
Acceso en línea:https://hdl.handle.net/20.500.14769/5138
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-5138
record_format dspace
spelling I32-R138-20.500.14769-51382026-01-07T14:21:47Z Predicting Falls using Time Series Data from the Equilivest Device Rejzi, Ledion FALL DETECTION, BINARY CLASSIFICATION, ANOMALY DETECTION, SUPERVISED LEARNING, UNSUPERVISED LEARNING "Falls pose a significant health risk to older adults, often resulting in serious injuries and reduced independence. This study explored two machine learning approaches for detecting falls using data from wearable sensors: a supervised binary classification approach trained on labeled fall and non fall data and an unsupervised anomaly detection approach trained exclusively on normal gait patterns. The results show that both approaches can accurately detect fall events within the scope of the study. The supervised models—Random Forest, Support Vector Machine, and Logistic Regression—demonstrated consistent performance, whereas the unsupervised One-Class Support Vector Machine (OCSVM) effectively identified anomalies without relying on fall data. This study offers a practical foundation for building fall detection systems and highlights the potential for future developments in predictive and real-time monitoring solutions. The motivation behind this dual approach lies in its long-term significance: if robust models can be developed to reliably detect fall events, they will provide a foundation for future work on more complex systems capable of predicting falls before they occur. Therefore, establishing dependable detection is a critical step toward enabling proactive and preventive safety solutions." 2025-10-24T17:37:30Z 2025-10-24T17:37:30Z 2025-06-30 Tesis de maestría https://hdl.handle.net/20.500.14769/5138 en application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic FALL DETECTION, BINARY CLASSIFICATION, ANOMALY DETECTION, SUPERVISED LEARNING, UNSUPERVISED LEARNING
spellingShingle FALL DETECTION, BINARY CLASSIFICATION, ANOMALY DETECTION, SUPERVISED LEARNING, UNSUPERVISED LEARNING
Rejzi, Ledion
Predicting Falls using Time Series Data from the Equilivest Device
topic_facet FALL DETECTION, BINARY CLASSIFICATION, ANOMALY DETECTION, SUPERVISED LEARNING, UNSUPERVISED LEARNING
description "Falls pose a significant health risk to older adults, often resulting in serious injuries and reduced independence. This study explored two machine learning approaches for detecting falls using data from wearable sensors: a supervised binary classification approach trained on labeled fall and non fall data and an unsupervised anomaly detection approach trained exclusively on normal gait patterns. The results show that both approaches can accurately detect fall events within the scope of the study. The supervised models—Random Forest, Support Vector Machine, and Logistic Regression—demonstrated consistent performance, whereas the unsupervised One-Class Support Vector Machine (OCSVM) effectively identified anomalies without relying on fall data. This study offers a practical foundation for building fall detection systems and highlights the potential for future developments in predictive and real-time monitoring solutions. The motivation behind this dual approach lies in its long-term significance: if robust models can be developed to reliably detect fall events, they will provide a foundation for future work on more complex systems capable of predicting falls before they occur. Therefore, establishing dependable detection is a critical step toward enabling proactive and preventive safety solutions."
format Tesis de maestría
author Rejzi, Ledion
author_facet Rejzi, Ledion
author_sort Rejzi, Ledion
title Predicting Falls using Time Series Data from the Equilivest Device
title_short Predicting Falls using Time Series Data from the Equilivest Device
title_full Predicting Falls using Time Series Data from the Equilivest Device
title_fullStr Predicting Falls using Time Series Data from the Equilivest Device
title_full_unstemmed Predicting Falls using Time Series Data from the Equilivest Device
title_sort predicting falls using time series data from the equilivest device
publishDate 2025
url https://hdl.handle.net/20.500.14769/5138
work_keys_str_mv AT rejziledion predictingfallsusingtimeseriesdatafromtheequilivestdevice
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