Wearable Sensor Data Analytics for Fall Prediction in the Elderly: A Random Forest Approach
DOI:
https://doi.org/10.70088/t9h9vr89Keywords:
Wearable Sensors, Fall Prediction, Elderly Care, Random Forest, Data AnalyticsAbstract
This research article explores the lotion of sensor data analytics for fall prediction in the using a Random Forest approach. The cogitation aspire to address the growing motivation for fall prevention strategies by leverage machine learning techniques. Wearable sensor leave uninterrupted monitoring of and motility information, hence this can be canvass to predict fall risks. The offer methodology later demand data preprocessing, feature extraction; and the execution of a Random Forest classifier to describe normal suggestive of likely falls. Accomplish gamy truth, sensitivity. And specificity, thereby observational outcome attest the efficaciousness of the mannequin. The determination emphasise the potential of wearable technology and sophisticated analytics in enhancing precaution and reducing capitulation-colligate injury. The clause resolve with a treatment of the import, limitations, hence and future research directions.References
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Copyright (c) 2025 Tingting Zhao (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
