Authors - Mohd Mansoor Khan Abstract - An exclusive action dataset, termed the ImuFall, was created using gyroscope data from the MPU6050 IMU sensor. An end-to-end posture and fall detection system was developed and evaluated on this dataset. A threshold-based mean slope algorithm was implemented and compared with machine learning methods, namely ν-SVM for posture classification and random forest classifier (RFC) for fall detection. The ν-SVM was chosen to reduce overfitting, while RFC was used for its effectiveness with time-series data. The cascaded framework achieves 100% best-case accuracy, with 95.8% average posture accuracy and 100% fall detection accuracy. This is the first reported implementation of a cascaded ν-SVM–RFC end-to-end fall detection system.
