Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Abstract

Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

“…This underlines the increasing significance and research interest in HAR for P+L. The application covers a wide range of sectors, such as manufacturing and assembling [58,86], warehousing [72,83,85], construction [90] and maintenance [11,14]. Warehousing, in particular order picking, is the only sector in logistics.…”

“…However, the other datasets utilised in these contributions do involve locomotion activities. The remaining publications [72,76,83] from P+L apply HAR methods on both stationary working processes and locomotion activities. Apart from Reining et al [85], the observed contributions in the domain of P+L assume that the activity definition is known at design time and is not going to change at run time of a HAR method (see Section 1).…”

“…There are six P+L contributions using individual datasets, while data from a repository are utilised three times. A single P+L contribution uses both data sources [83]. Examining the entire literature corpus, it is striking that a majority of 38 contributions utilises individual datasets, and 34 of them do so without referring to data that are publicly available in repositories.…”

“…Examining the entire literature corpus, it is striking that a majority of 38 contributions utilises individual datasets, and 34 of them do so without referring to data that are publicly available in repositories. Thus, there are four contributions using both individual and repository data [22,54,71,83]. As most contributions lack a recording protocol and a detailed description of the recorded activities, it remains unclear whether the motion patterns and their assigned activity labels as well as the underlying activity definitions are comparable.…”

“…The term noise filter was also used in [84], but there was no explanation on the particular method. In [68,77], a zero-mean and unit-variance normalisation, and in [83] a Max-Min normalisation to the range [0, 1] were carried out, as there are differences among the units and scales of the measurements. The authors of [67] did not use any pre-processing and forwarded the raw data to a convolutional neural network.…”