This paper discusses new approaches to parameter estimation of gamma distribution based on representative points. In the first part, the existence and uniqueness of gamma mean squared error representative points (MSE-RPs) are discussed theoretically. In the second part, by comparing three types of representative points, we show that gamma MSE-RPs perform well in parameter estimation and simulation. The last part proposes a new Harrel–Davis sample standardization technique. Simulation studies reveal that the standardized samples can be used to improve estimation performance or generate MSE-RPs. In addition, a real data analysis illustrates that the proposed technique yields efficient estimates for gamma parameters.

Falls among older adults pose a major healthcare and social burden, making early identification of high-risk individuals essential for prevention. This study presents a portable, non-invasive AI-based wearable system that predicts fall risk using surface electromyography (sEMG) and plantar-pressure measurements collected during overground walking. sEMG electrodes were placed bilaterally over eight key lower-limb muscles—tibialis anterior, peroneus longus, medial and lateral gastrocnemius, rectus femoris, vastus medialis, vastus lateralis, and biceps femoris—while pressure insoles captured loading at eight anatomical foot regions. Ninety-four older adults (mean age 69.6 ± 10.0 years; 57 females), including 57 non-fallers and 37 individuals who met ICD-10 diagnostic criteria for “propensity to fall,” participated in the modeling study. The signals from both devices were streamed wirelessly to a central acquisition unit for synchronized processing. Extracted features included muscle activation contribution, mean frequency, mean power frequency, and cumulative plantar-pressure impulses. These features served as model input. To reduce data dimensionality, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were applied. PCA retained a variance structure, whereas LDA maximized class separability. Three machine-learning classifiers—Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained using Leave-One-Out Cross-Validation. LDA substantially improved performance across all models, with LDA + SVM achieving the highest accuracy (0.88), precision (0.92), recall (0.85), and F1-score (0.87). An independent clinical validation study involving ten additional older adults demonstrated that LDA-based models generalized well beyond the original dataset. Compared with existing fall-detection or multimodal EMG-based systems that focus on simulated falls, young participants, or non-portable laboratory equipment, the proposed framework enables physiologically interpretable, clinically deployable fall-risk prediction during natural gait. These findings highlight the promise of dual-modality wearable sensing for proactive fall prevention in geriatric populations.