Fusion of Smartphone Motion Sensors for Physical Activity Recognition

By Muhammad Shoaib, Stephan Bosch, Özlem Durmaz Incel, Hans Scholten, Paul J. M. Havinga. Italian National Conference on Sensors, 2014

557 citations recorded by the source corpus.

For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

Subfields: Computer Vision and Pattern Recognition, General Health Professions, Transportation

View Paper: https://www.semanticscholar.org/paper/473f837c09ce784264b760eabdb542990aeeb106

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Citations in the corpus

Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks (2015, background): “Especially, activity recognition based on wearable sensors is directly related to our daily lives such as healthcare and workout monitoring [3][4].”
Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors (2016, methodology): “We had shown previously that the accelerometer performs poorly at recognizing walking upstairs and downstairs and needs additional gyroscope data for better recognition of these two activities [12].”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, methodology): “Readers should refer to [66] for detailed information concerning how to assess a feature set.”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, methodology): “For the experiments presented in this paper, we resampled the signal in order to have a constant sampling rate of 50 Hz, which is commonly used in literature for activity recognition from data acquired through smartphones [24, 23, 25].”
Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder (2017, methodology): “2 Multi-dimensional Time Series In the second experiment, we evaluate the proposed method on human activity time series from smartphone sensors [17].”
A Survey of Online Activity Recognition Using Mobile Phones (2015, background): “We have shown previously [10] that a gyroscope is unable to differentiate between the sitting and the standing activity.”