Human activity recognition using deep convolutional neural network

Human activity recognition problems involve the processes of recording and analyzing a person's daily life to identify people's behavioral patterns. Various methods such as Video-Based activity recognition and Sensor-Based activity recognition have been developed to collect activity data. Studies have been carried out by recording activities such as walking, sitting, running, jumping, climbing stairs, climbing stairs by means of depth sensors, wearable (portable) devices, and RGB cameras. Systems have been developed especially in the fields of health, the internet of things, smart cities, transportation, and security with activity recognition. Activity-sensitive approaches have been developed for security monitoring and threat detection through activity recognition. By monitoring activity-based anomalies, potential threats can be identified, and appropriate precautions can be taken. Data collection has become extremely easy because of accelerometer, gyroscope, magnetometer sensors found in almost any smart device. Deep learning methods such as ANN, CNN, RNN, and LSTM are used to classify the obtained data. In our research, we mainly focused on sensor-based activity recognition. Classification tasks were done using 1-D Convolution Neural Network feeding the raw data from the UCI-HAR dataset using accelerometer and gyroscope data. Raw data filtered using Median Filter. We didn't apply any mathematical or statistical feature extraction methods to the data. For the experimental results, we implemented 6, 7, and 12 classes activity recognition, and achieved accuracies of 96.95%, 95.03%, and 93.08%, respectively.