Recognizing Human Activities and Earthquake Vibration from Smartphone Accelerometers using LSTM Algorithm
Budiarianto Suryo Kusumo, Ana Heryana, Ekasari Nugraheni, Andri Fachrur Rozie, Bambang Setiadi · 2018
Nowadays, most smartphones are equipped with accelerometer sensor, which can be used to record acceleration caused by movements or vibration. It opens the opportunity to use them as a personal earthquake early warning system where people can use their smartphone to detect the incoming earthquake. However, to avoid false alarm, we must be able to recognize the source of sensor acceleration. One of the most common sources of vibration recorded by smartphone accelerometer is human activities. In this work, we used machine learning to distinguish the source of movement by observing the acceleration value caused by human activities and earthquakes. RNN-LSTM neural network is trained with labeled time series acceleration data and used to recognize the movement source. Our model show potential to differentiate between human activity and earthquake movement with training accuracy value of 97% and test loss value equal to 0.3.