Authors - Nikhil Kumar, Anurag Barthwal, Shakti Kundu Abstract - Falls from an altitude are among the most common causes of both fatal and non-fatal injuries in the global community and second only to road traffic accidents in accidental mortality. One of the primary problems in alleviating the effects of such incidents is the late detection and reporting of falls, especially in the cases where witnesses are not present, which exposes the victim to a high risk of severe injuries, or even death, because of the lack of medical care. To curb this problem, this paper proposes an effective and affordable smartphone-based solution towards automated detection of human falls off heights. The suggested solution uses built-in smartphone sensors namely accelerators and barometers to record motion dynamics and changes in altitude which are linked to falls. The primary characteristics, such as the absolute linear acceleration, change in altitude, are acquired and applied to train and test a Support Vector Machine (SVM)-based classification model, which shows strong performance, with the F1-score of 0.94, which, in turn, proves the high reliability of the model in differentiating between fall and non-fall events. The results indicate the success of the multi-sensor data fusion with machine learning methods and emphasize the possible relevance of the given system to practical applications in the field of fall detection in real-time, early emergency response, and the overall occupational and population safety schemes.
