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

Huang, Emily J.; Onnela, Jukka‐Pekka

doi:10.3390/s20133706

Cited by 15 publications

(17 citation statements)

References 30 publications

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“…These classifiers, when applied to free-living conditions, will naturally miss the activities they were not trained on but will also likely overestimate those activities they were trained on. An improved scheme could assume that the observed activities are a sample from a broader spectrum of possible behaviors, including periods when the smartphone is not on a person, or assess the uncertainty associated with the classification of each type of activity 84 . This could also provide for an adaptive approach that would enable observation/interventions suited to a broad range of activities relevant for health, including decreasing sedentary behavior, increasing active transport (i.e., walking, bicycling, or public transit), and improving circadian patterns/sleep.…”

Section: Discussionmentioning

confidence: 99%

“…Activity templates function essentially as blueprints for different types of physical activity. In the HAR systems, we reviewed, these templates were compared to patterns of observed raw measurements using various distance metrics 38,84 , such as the Euclidean or Manhattan distance. Given the heterogeneous nature of human activities, activity templates were often enhanced using techniques similar to dynamic time warping 29,57 , which measures the similarity of two temporal sequences that may vary in speed.…”

Section: Feature Extractionmentioning

confidence: 99%

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A systematic review of smartphone-based human activity recognition methods for health research

2021

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Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

A systematic review of smartphone-based human activity recognition methods for health research

2021

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“…The first component of HAR is data collection, which requires careful thought about various questions, such as choosing the appropriate sensors, sampling frequency, study environment, and smartphone placement. Some studies use a single sensor [ 11 , 12 ], while other studies simultaneously utilize multiple sensors [ 13 , 14 , 15 , 16 , 17 , 18 ]. In our study, we used data collected from two sensors in the smartphone—the accelerometer and gyroscope.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Yan

Onnela

2022

Sensors

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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.

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“…This might be a challenging problem due to: a large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements [8]. Some of the Machine Learning models that have been already used for the recognition of activities are Movelet Method [9], Support Vector Machines (SVMs) [10], Decision Trees [11], Naive Bayes [12] and Markov chains [13]. It should be noticed that although Machine Learning models could be fit to training data, they could not be generalized with sufficient accuracy on data from subjects not included in the training set.…”

Section: Introductionmentioning

confidence: 99%

Predicting the type of physical activity from tri-axial smartphone accelerometer data

Pavlović¹

2021

J Appl Eng Science

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Development of various statistical learning methods and their implementation in mobile device software enables moment-by-moment study of human social interactions, behavioral patterns, sleep, as well as their physical mobility and gross motor activity. Recently, through the use of supervised Machine Learning, human activity recognition (HAR) has been found numerous applications in biomedical engineering especially in the field of digital phenotyping. Having this in mind, in this research in order to be able to quantify the human movement activity in situ, using data from portable digital devices, we have developed code which uses Random Forest Classifier to predict the type of physical activity from tri-axial smartphone accelerometer data. The code has been written using Python programing language and Anaconda distribution of data-science packages. Raw accelerometer data was collected by using the Beiwe research platform, which is developed by the Onnela Lab at the Harvard T.H. Chan School of Public Health. Tuning has been performed by defining a grid of hyperparameter ranges, using Scikit-Learn’s Randomized Search CV method, randomly sampling from the grid and performing K-Fold CV with each combination of tested values. Obtained results will enable development a more robust models for predicting the type of physical activity with more subjects, usage of different hardwares, various test situations, and different environments.

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

Cited by 15 publications

References 30 publications

A systematic review of smartphone-based human activity recognition methods for health research

A systematic review of smartphone-based human activity recognition methods for health research

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

Predicting the type of physical activity from tri-axial smartphone accelerometer data

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