Background/Objectives: Human fall detection is a critical challenge in the healthcare domain since the late medical salvage will even lead to death situations, therefore it requires timely rescue. This research work proposes a system which uses a wearable device that senses human fall and wirelessly raises alerts. Methods/statistical analysis: The detection system consists of the sensor system which contains both accelerometer and gyroscope sensors. The proper orientation of the subject is provided by the Madgwick filter. Six volunteers were engaged to perform the falling and non-falling events. The system is validated and checked by four algorithms: threshold based, support vector machine (SVM), K-nearest neighbor, and dynamic time wrapping, and thus, the accuracy was calculated. Findings: From the results obtained, the SVM has given an accuracy of 93%. Conclusions: When a fall is being detected, an additional feature to check whether the person is in critical state and is lying down for more than a particular time is incorporated and a critical alert is sent to the caretaker’s mobile. © Springer Nature Singapore Pte Ltd. 2019.