MobileAid: A Fast and Effective Cognitive Aid System on Mobile Devices

Abstract

Elderly people generally suffer from diseases with cognitive decline, which seriously affect their daily lives. To assist these patients with cognitive decline, numerous cognitive aid devices have been researched and designed. Although these devices can achieve the aid tasks, they have to face two difficulties: low accuracy and long latency. In this paper, we present MobileAid, an aid system implemented on the mobile device, which assists cognitive decline patients to recognize objects. The key idea of this system is a two-step lightweight neural network design for the target recognition: context recognition and object recognition, and this design achieves low time delay. Considering the great success of convolutional neural networks in object recognition, we design a lightweight convolutional neural network by the combination of pooling layers and activation functions selection, which achieves high accuracy. Furthermore, we apply the depthwise separable convolution to reduce the resource consumption for deploying the system on mobile devices. The results of the extensive experiments we have conducted show that MobileAid can achieve 95% high accuracy and 90ms low time delay with few resource consumption.

Keywords

• Deep learning
• mobile computing
• context recognition
• object recognition