  This study presents a novel method to recognize human physical activities
using CNN followed by LSTM. Achieving high accuracy by traditional machine
learning algorithms, (such as SVM, KNN and random forest method) is a
challenging task because the data acquired from the wearable sensors like
accelerometer and gyroscope is a time-series data. So, to achieve high
accuracy, we propose a multi-head CNN model comprising of three CNNs to extract
features for the data acquired from different sensors and all three CNNs are
then merged, which are followed by an LSTM layer and a dense layer. The
configuration of all three CNNs is kept the same so that the same number of
features are obtained for every input to CNN. By using the proposed method, we
achieve state-of-the-art accuracy, which is comparable to traditional machine
learning algorithms and other deep neural network algorithms.
