Dilated Temporal Convolutional Neural Network Architecture with Independent Component Layer for Human Activity Recognition A system using Convolutional Neural Network was developed for Human Activity Recognition (HAR) application. The proposed method uses the algorithm to detect the daily activities of people with cognitive disabilities. As an initial application, daily hygienic activities were monitored to assist people with cognitive disabilities who often need prompts to start and finish activities. The proposed system discussed in the paper uses Dilated Temporal Convolutional Neural Network Architecture with Independent Component Layers (DTCNNIC) to detect daily hygienic activities. The proposed system can be trained to obtain high average classification accuracy of 92.66 percent from only using a single channel accelerometer information. This method can reduce the necessary computational resources to train the proposed neural network allowing the algorithm to be implemented on lowcost platforms.

Smart Phone Based Human Activity Recognition Human Activity Recognition (HAR) is a field that uses collected data to classify different human actions. One simple and general approach to HAR is to use the sensor data from a mobile device to recognize different patterns behind complex motions. Early studies show promising results on simple activities using manually selected features from accelerometer readings. As newer publicly available datasets include more complex data and activities, manual feature selection have become cumbersome, impractical and face limitations in finding the optimal feature sets for HAR. In this paper, we present an empirical approach to defining models of 3D tensor data structures from 2D time series data obtained from multiple sensors on a smart phone, and a new Convolutional Neural Network (CNN) model, which uses the tensor data and performs automatic feature extraction and classification for HAR. We use the public benchmark dataset, MobiAct v2.0, to train and validate our model, which achieved an overall better performance in classifying 11 Activities of Daily Living (ADL) than the stateoftheart approaches. Compared to the approach presented by Chatzaki et al. which has a very high rate of misclassifications for carstep out (CSO), carstep in (CSI), sit to stand (CHU), and stand to sit (SCH) classes, our proposed approach has 15% higher sensitivity for each of these activities with the optimal number of training epochs being only 25.