Fall Detection System Based on Smartphone Sensors: Machine Learning Application in Medical Care Scenarios

This article introduces a machine learning project for fall detection using smartphone accelerometer and gyroscope data. By independently collecting data and building classification models, it addresses the limitations of traditional fall detection solutions and provides a low-cost, easy-to-deploy technical solution for scenarios such as elderly care and medical monitoring, covering the entire process from data collection, feature engineering, model training to practical application.

Falls are a major health threat to the elderly. About one-third of people over 65 fall each year, some leading to serious consequences. Traditional solutions have limitations: wearable devices rely on active wear by users, and environmental sensors involve privacy issues and high costs. Smartphones are widely used and equipped with high-precision IMUs (accelerometers + gyroscopes), providing new possibilities for fall detection, but technical challenges such as distinguishing falls from similar movements, controlling false alarm rates, and real-time processing need to be addressed.

The project uses an end-to-end process and independently collects data with Sensor Logger (to avoid privacy issues): volunteers simulate fall scenarios (forward/backward/sideways falls) and daily activities (walking, sitting, etc.), recording six-dimensional data (3-axis acceleration + 3-axis angular velocity) at 50-100Hz. Preprocessing involves splitting time series with sliding windows (2-4 second windows), extracting features including time domain (mean, variance, etc.), frequency domain (FFT), and physically derived features (synthetic acceleration, attitude angles), and may also use threshold heuristic rules for assistance.

Fall detection is a binary classification problem, and multiple models are tested: traditional ML (Random Forest, SVM), deep learning (CNN, RNN, CNN-LSTM). Evaluation needs to consider class imbalance, using metrics such as sensitivity, specificity, F1 score, and AUC-ROC, while also focusing on latency (rescue time is critical).

Application scenarios include homes (safety net for elderly living alone), elderly care institutions (reducing care burden), hospitals (postoperative patient monitoring), and rehabilitation centers (assessing balance ability). The smartphone solution is low-cost and easy to popularize. In terms of privacy, it follows data minimization, processes data locally, and can use federated learning; a user feedback mechanism is needed to handle misjudgments.

Future directions: multi-modal fusion (combining GPS/microphone), edge computing optimization (offline lightweight models), and personalized adaptation (online/transfer learning). Summary: This project is a typical application in the mobile health field, using general-purpose devices and open-source tools to build an AI system with social value, combining technology and humanity, and is worth learning for developers and researchers.