Deep learning method for human activity recognition using heaped LSTM and image pattern of activity

Deep learning is the most spelt word and habitually used technology of the researchers around the globe of technical arena. With the tremendous growth of technologies like data analytics, data mining, machine learning methods and IoT applications like health monitoring, safety and security, and smart control, human movement acknowledgement has become a more noteworthy achievement in the field of science. Utilising the most booming technology, we propose a unique approach for monitoring human activities who aspire to live and lead an independent life, mostly the elderly people. In this experiment we discovered a novel method in identifying the human activities and the forte of this approach is the privacy of the monitored person is ensured. This investigation is moved forward by utilising an improved convolution neural network (CNN) with enriched bi-directional LSTM (BLSTM). The activity recognition model is still optimised by using a heaped LSTM (HLSTM) and a fine trained data clustering algorithm. Our proposed approach, when trained and tested with a prominent dataset that contains sensor data, achieved overall accuracy of 99.43% for all the considered nine activities.