Falls are a widespread issue affecting people worldwide, regardless of their social status. Falls lead to physical, psychological, and economic consequences. Experts are developing solutions for this problem, given the high frequency of falls among the elderly. This study presents various ML models, which can predict human falls using signals of a wearable sensor located on the wrist or the waist. By extracting the mean, standard deviation, and range, we were able to train and evaluate various machine learning models considering accelerometers and gyroscopes as sensors. The combination of these characteristics and sensors resulted in the RF waist model achieving the most favorable metrics, achieving an accuracy rate of 97.22%.

Electroencephalography (EEG)‐based seizure detection is increasingly being considered for wearable neurological monitoring, but conventional approaches often rely on dense electrode configurations and high‐dimensional feature representations. This letter presents a machine learning‐guided co‐design framework in which feature relevance informs EEG acquisition design. From 18 bipolar derivations of the CHB‐MIT database, 270 wavelet descriptors were extracted. The available reduced inputs comprise 10 features from nine derivations and a separate four‐feature configuration comprising four bipolar derivations and eight scalp electrodes. In 75/25 sample‐level evaluations, random forest achieved 93.75%, 91.67% and 93.75% accuracy for the full, 10‐feature, and four‐feature inputs, respectively. These exploratory results demonstrate a traceable link between feature relevance and acquisition‐design decisions, supporting the proposed feature‐to‐derivation mapping as a candidate strategy for compact EEG acquisition while motivating subsequent patient‐independent and wearable validation.