Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

Abstract

Abstract. Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject's body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

“…However, it has been decided to apply this method to a public human-activity recognition dataset published by the Center for Machine Learning and Intelligent Systems (Anguita, Ghio, Oneto, Parra, & Reyes-Ortiz, 2012). This decision allows this work to be compared with other proposals through the same information repository.…”

Ascending and Descending Walking Recognition using Smartphone

“…For example, the research in [4] needs 43 features for activity classification but the result is quite small for descending stairs (44.3 %) and ascending stairs (61.5 %), which leads to the consideration of combining the two activities as one action. In another research, the classification precisions of walking downstairs and walking upstairs are 87.2% and 72.6% respectively [3]. However, it requires a vector of 17 features to draw that classification result.…”

“…Another research attempted to recognize six human activities using multiclass support vector machine (MC-SVM) [3]. The experiment was carried out by a group of 30 subjects wearing the smartphone on their wrist.…”

“…Another good point of this research is an activity could be recognized in a short interval of time such as 2 or 4 seconds. The research [4] applies an approximate window size of 2.56 sec, while the study [3] divides data in 10 seconds window segments. A short window time allows the flexible in recognition because sometimes the subjects may perform an activity less than the required duration for recognition.…”

Diffuse interface methods for multiclass segmentation of high-dimensional data

“…We compare this to the results of [25], where the methods MC-SVM and MC-HF-SVM have accuracies of 89.3% and 89.0%, respectively. It is important to note that the results of the paper were obtained using supervised learning methods where 70% of the data was used for training and the rest for testing.…”