HUMAN ACTIVITY RECOGNITION FOR THE INTERNET OF HEALTH THINGS (IOHT): A WEARABLE APPROACH

Abstract

Human Activity Recognition (HAR) refers to the detection and classification of different types of human activities on the basis of data collected by sensors. Various sensory devices are used for fetching the data such as accelerometers, microphones, gyroscopes, and wearable devices to interpret and analyze human activities. The main goal of HAR is to develop models and algorithms which can detect basic and complex human activities. This paper presents the WISDM (Wireless Sensor Data Mining) 2019 data set collected from 51 subjects performing 18 activities for a period of 3 minutes. The sensors used to collect this data are accelerometers and gyroscopes and the devices used are smartphones and smartwatches. For our thesis, we have developed a model similar to InceptionResNet which can be used for different data sets to detect human activities accurately and efficiently. The proposed model is an enhancement of an existing Inception ResNet model and is more generic in nature as compared to the previous model. The model has been tested with two input features 1D and 3D and 3 different batch sizes i.e.75, 100 and 200, which are the magnitude of 3D Accelerations, i.e., magw a ’,and ’3D Accelerations, i.e. (a w,x a w,y a w)z’ . The previous model was developed and only tested on Smartphone data of the WISDM 2011 dataset. To make our model more generic we have trained and validated it on 3 versions of the WISDM 2019 dataset i.e. Smartphones data only, Smartwatches data only, and, Smartphones and Smartwatches combined dataset. In our proposed model, we have changed the architecture of the existing model by replacing the GRU layer with the LSTM layer, reducing the number of inception models from 2 to 1, increasing the number of filters from 10 to 32,64 and, adding Max pooling layer to the model. Time periods are set to 200 and step distance is set to 16. After making these modifications to the model the accuracy achieved for all 18 activities in WISDM 2019 is from 97 % to 99.5 % using the sensors (gyroscope and accelerometer).