Falling Is A Significant Health Problem. Fall Detection, To Alert For Medical Attention, Has Been Gaining Increasing Attention. Still, Most Of The Existing Studies Use Falls Simulated In A Laboratory Environment To Test The Obtained Performance. We Analyzed The Acceleration Signals Recorded By An Inertial Sensor On The Lower Back During 143 Real-world Falls (the Most Extensive Collection To Date) From The FARSEEING Repository. Such Data Were Obtained From Continuous Real-world Monitoring Of Subjects With A Moderate-to-high Risk Of Falling. We Designed And Tested Fall Detection Algorithms Using Features Inspired By A Multiphase Fall Model And A Machine Learning Approach Such As SVM And Decision Tree. The Obtained Results Suggest That Algorithms Can Learn Effectively From Features Extracted From A Multiphase Fall Model, Consistently Overperforming More Conventional Features. The Most Promising Method (support Vector Machines And Features From The Multiphase Fall Model) Obtained A Sensitivity Higher Than 80%, A False Alarm Rate Per Hour Of 0.56, And An F-measure Of 64.6%. The Reported Results And Methodologies Represent An Advancement Of Knowledge On Real-world Fall Detection And Suggest Useful Metrics For Characterizing Fall Detection Systems For Real-world Use. The SVM And Decision Tree Has Implemented For The Classification Of The Fall.
