Abstract
Sensor-based activity recognition is encountered in innumerable applications of the arena of pervasive healthcare and plays a crucial role in biomedical research. Nonetheless, the frequent situation of unobserved measurements impairs the ability of machine learning algorithms to efficiently extract context from raw streams of data. In this paper, we study the problem of accurate estimation of missing multimodal inertial data and we propose a classification framework that considers the reconstruction of subsampled data during the test phase. We introduce the concept of forming the available data streams into low-rank two-dimensional (2-D) and 3-D Hankel structures, and we exploit data redundancies using sophisticated imputation techniques, namely matrix and tensor completion. Moreover, we examine the impact of reconstruction on the classification performance by experimenting with several state-of-the-art classifiers. The system is evaluated with respect to different data structuring scenarios, the volume of data available for reconstruction, and various levels of missing values per device. Finally, the tradeoff between subsampling accuracy and energy conservation in wearable platforms is examined. Our analysis relies on two public datasets containing inertial data, which extend to numerous activities, multiple sensing parameters, and body locations. The results highlight that robust classification accuracy can be achieved through recovery, even for extremely subsampled data streams.
Highlights
H UMAN activity recognition (HAR) is currently faced up with the challenge of enabling the interpretation of Manuscript received January 13, 2017; revised May 14, 2017; accepted June 7, 2017
We extend our recent work [15] on a human activity classification framework that considered the well-established method of Matrix Completion (MC) [16] for efficiently reconstructing missing values
In this work we have addressed the problem of missing values in the HAR domain, considering: (a) the inherent correlations that characterize heterogeneous inertial data, and (b) the use of MC and Tensor Completion (TC) as an inseparable part of a modular human activity classification framework for reconstructing sub-populated 2D and 3D structures, respectively
Summary
H UMAN activity recognition (HAR) is currently faced up with the challenge of enabling the interpretation of Manuscript received January 13, 2017; revised May 14, 2017; accepted June 7, 2017. The aforementioned approaches share as a common assumption that the training set suffers from missing values, which is not viable in the HAR domain and the use of wearable sensors; training of predictive models is typically performed in well-controlled environments, wherein the sensing data acquisition, transmission and storage is performed under ideal conditions. We study the impact of imputation on human activity classification tasks, wherein the unobserved values are introduced during the evaluation phase. Towards this direction, we extend our recent work [15] on a human activity classification framework that considered the well-established method of Matrix Completion (MC) [16] for efficiently reconstructing missing values. The results highlight the efficacy of the proposed methods in achieving high classification accuracy with less than 50% of the available measurements
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