User talk:AnalysisDATA

A wearable fall detection system was proposed in [59] that could determine fall events by employing acceleration and orientation thresholds. The acceleration thresholds were obtained at the training phase from SVM, and the postural orientation thresholds were determined from the subject’s tilt angle. The system used Madgwick’s orientation filter for reducing magnetic distortion and gyroscope drift, resulting in high estimation accuracy. The IMU was placed on the waist and could communicate over Bluetooth. The system analyzed the RMS data obtained from the accelerometer and the orientation filter and could detect fall events using a threshold based algorithm. This allows implementing the algorithm for real-time applications in a low profile microprocessor. The algorithm was reported to achieve a high degree of accuracy and sensitivity. Table 4 presents a comparison of the key features and performance characteristics among the activity monitoring systems discussed above.