۱- Introduction

۲- Related work

۳- Algorithms

۴- Experiments and evaluation

۵- Computational performance

۶- Conclusion

References

Abstract

With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. This task now arises in a range of applications such as healthcare monitoring, fitness tracking or user-adaptive systems, where a general model capable of instantaneous activity recognition of an arbitrary user is needed. In this paper, we present a user-independent deep learning-based approach for online human activity classification. We propose using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series. Furthermore, we investigate the impact of time series length on the recognition accuracy and limit it up to 1 s that makes possible continuous real-time activity classification. The accuracy of the proposed approach is evaluated on two commonly used WISDM and UCI datasets that contain labeled accelerometer data from 36 and 30 users respectively, and in cross-dataset experiment. The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.

Introduction

The current generation of portable mobile devices, such as smartphones, music players, smart watches or fitness trackers incorporates a wide variety of sensors that can be used for human activity and behavior analysis. This opens up new areas of intelligent applications that use this data for making inferences about different aspects of human life. Among the traditional examples here are healthcare monitoring, life logging, fitness tracking and security applications. Another emerged and rapidly evolving field is an unobtrusive user activity recognition in adaptive mobile applications that adjust their behavior and setup to the current mode of use. One common property of these applications is that they usually need to work out of box for an arbitrary user in an arbitrary environment, since in most cases there is no way of asking the user for training data. Another common challenge is a real-time activity recognition that is especially crucial for security and adaptive apps. The task of human activity recognition can be generally divided into two main steps. The first step is time series segmentation, and the basic approach to this problem is to use a sliding window of a fixed length and split each time series into equal segments. The question that can arise here is how the recognition accuracy depends on the window length, however it was not covered in previous works. Particularly, for WISDM dataset a sliding window of size 10 seconds was used in all studies [1, 2, 3, 4, 5] except for [6], where an adaptive time series segmentation technique was proposed.