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Open AccessArticle

Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data

by Emily J. Huang, Kebin Yan and Jukka-Pekka Onnela

Sensors 2022, 22(7), 2618; https://doi.org/10.3390/s22072618 - 29 Mar 2022

Cited by 9 | Viewed by 4062

Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone’s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities. Full article

(This article belongs to the Section Wearables)

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1 pages, 153 KB

Open AccessCorrection

Correction: Huang, E.J., and Onnela, J.P. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data. Sensors 2020, 20(13), 3706

by Emily J. Huang and Jukka-Pekka Onnela

Sensors 2020, 20(21), 6091; https://doi.org/10.3390/s20216091 - 27 Oct 2020

Cited by 2 | Viewed by 1728

Abstract

The authors wish to make the following corrections to this paper [...] Full article

(This article belongs to the Section Wearables)

18 pages, 438 KB

Open AccessArticle

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data

by Emily J. Huang and Jukka-Pekka Onnela

Sensors 2020, 20(13), 3706; https://doi.org/10.3390/s20133706 - 2 Jul 2020

Cited by 13 | Viewed by 4726 | Correction

Abstract

Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research. Full article

(This article belongs to the Section Wearables)

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20 pages, 508 KB

Open AccessArticle

Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data

by Bryan D. Martin, Vittorio Addona, Julian Wolfson, Gediminas Adomavicius and Yingling Fan

Sensors 2017, 17(9), 2058; https://doi.org/10.3390/s17092058 - 8 Sep 2017

Cited by 52 | Viewed by 5679

Abstract

We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy. Full article

(This article belongs to the Section Physical Sensors)

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