Inertial Sensor Based Human Activity Identification System Using CNN- LSTM Deep Learning Technique

Inertial sensors embedded within smart devices are emerging research propel that gather information about people's actions. This valuable information can be further used to predict and analyses human behaviors using the latest techniques like machine learning and deep learning. The predictions made through these models have great importance in the field of medical medicine, like caring about elderly and mentally retarded patients and, they can also be used in security surveillance applications. In this proposed approach, a daily living Activity Recognition dataset, MHEALTH from Kaggle repository, which is built from the recordings of ten volunteers through the use of four inertial sensors of mobile devices placed at various body positions of volunteers. A hybrid CNN-LSTM deep learning model is implemented using Python libraries such as keras, pandas, tensorflow, numpy, etc. this model is a layered architecture of Convolutional Neural Network (CNN) to extract prime features from input data and to predict human activity in the long short-term memory (LSTM). The experimental results of the proposed model after hypertunning achieved accuracy of 98% over the MHEALTH dataset