FALL SENSE: A WEARABLE INERTIAL SENSOR -BASED DATASET FOR PRE-IMPACT FALL DETECTION IN ELDERLY INDIVIDUALS
Keywords:
Fall Sense, Machine learning, Low false alarm rates, Health risk mitigation.Abstract
Falls among elderly individuals pose a significant health risk and are a leading cause of injury-related hospitalizations. Early detection of falls can greatly mitigate the severity of injuries and improve the overall well-being of the elderly population. This paper presents "Fall Sense," a novel approach for preimpact fall detection in elderly individuals using a dataset collected from wearable inertial sensors. In the introduction, we highlight the importance of fall detection in elderly care and emphasize the need for accurate and timely detection to reduce the adverse consequences of falls. The conventional fall detection systems are discussed, revealing their limitations, such as low accuracy, high false alarm rates, and the need for extensive infrastructure. These drawbacks hinder their practicality and effectiveness in real-world scenarios. In response to these limitations, our proposed system leverages wearable inertial sensors, which are unobtrusive and can be comfortably worn by elderly individuals. These sensors continuously collect data on motion and acceleration, which are then processed using machine learning algorithms. Our dataset, "Fall Sense," is introduced, containing a diverse set of activities, including falls, activities of daily living, and simulated falls, making it a valuable resource for training and evaluating machine learning models for fall detection. The proposed system employs machine learning techniques to analyze sensor data in real-time, enabling the detection of fall-related patterns and anomalies before the impact occurs. This early detection allows for timely intervention, such as alerting caregivers or activating emergency response systems, ultimately improving the safety and well-being of elderly individuals. Fall Sense represents a promising advancement in fall detection technology, offering the potential to reduce the impact of falls on the elderly population and enhance their quality of life.
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