Differentiating Human Falls from Daily Activities Using Machine Learning Methods Based on Accelerometer and Altimeter Sensor Fusion Feature Engineering
This paper presents a detailed analysis of signal data acquired from wearable sensors such as accelerometers and barometric altimeters for human activity recognition, with an emphasis on fall detection. This research addressed two types of activity recognition tasks: a binary classification problem between activities of daily living (ADLs) and simulated fall activities and a multiclass classification problem involving five different activities (running, walking, sitting down, jumping, and falling). By combining features derived from both sensors, traditional machine models such as random forest, support vector machine, XGBoost, logistic regression, and majority voter models were used for both classification problems. All of the aforementioned methods generally produced better results using combined features of both sensors compared to single-sensor models, highlighting the potential of sensor fusion approaches for fall detection.
With the rapid development of underground space, structural crack monitoring has become increasingly critical. This study proposes a unified framework integrating image preprocessing, feature extraction, model training, and safety assessment for crack analysis. An improved OTSU t…
This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime rem…
Human activity recognition (HAR) based on smartphone and wearable sensor data is commonly addressed using statistical learning methods and deep neural networks that often provide strong predictive performance, but at the expense of limited interpretability and substantial computa…
Wireless power transfer (WPT) systems for electric vehicles require reliable foreign object detection (FOD) mechanisms both during and prior to power transfer to ensure operational safety and efficiency. The primary purpose of this study was to develop a foreign object detection…
Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the…
This study has explored the effectiveness of machine learning methods for high-precision spatial interpolation of meteorological variables, aiming to provide accurate atmospheric delay corrections for high-precision edge and corner nets observation in complex-terrain environments…