A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory

Bragança, Hendrio L. de Souza; Colonna, Juan Gabriel; Lima, Wesllen Sousa; Souto, Eduardo

doi:10.3390/s20071856

Cited by 23 publications

(25 citation statements)

References 52 publications

(120 reference statements)

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“…Classification using multiclass SVM achieved an accuracy of 96.81 %. Bragança et al [6] used symbolic representation algorithms (Symbolic Aggregate Approximation, Symbolic Fourier Approximation, Bag-of-Patterns) to encode sensor data time series as symbolic sequences. The latter are classified by KNN classifier.…”

Section: Related Workmentioning

confidence: 99%

Fusion of smartphone sensor data for classification of daily user activities

Şengül

Ozcelik

Misra

et al. 2021

Multimed Tools Appl

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New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.

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Section: Related Workmentioning

confidence: 99%

Fusion of smartphone sensor data for classification of daily user activities

Şengül

Ozcelik

Misra

et al. 2021

Multimed Tools Appl

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“…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. As an alternative to raw measurements, some studies used signal symbolic approximation, which translates a segmented time-series signal into sequences of symbols based on a predefined mapping rule (e.g., amplitude between −1 and −0.5 g represents symbol "a", amplitude between −0.5 and 0 g represents symbol "b", and so on) [85][86][87] .…”

Section: Feature Extractionmentioning

confidence: 99%

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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“…The purpose of sliding window-based feature extraction is to retrieve the detailed raw of accelerometer data for each activity and the changes occurred. Sliding window is a technique of extracting data with data sampling where each window consists of a set of detailed raw of accelerometer data [14]. The sliding windows process is shown as Figure 3.…”

Section: A Sliding Window-based Feature Extractionmentioning

confidence: 99%

Mobile-based Activity Monitoring System for the Self-quarantine Patient

Indrawanti

Mandyartha

2021

ATCSJ

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Nowadays, not all the patient can be hospitalized because of the COVID-19 pandemics. So, the self-quarantine for the patient with the various diseases will be the given solution by the hospital. It would make the hospital needs a system that can monitor the activity and the position of the patient from a distance. Nowadays, mobile phone is equipped by the sensor that can detect the user movement. Not only the user’s position, but also the user’s activity. In this paper, it will be developed an activity and position monitoring system for the self-quarantine patient that can be used in their home. The mobile activity monitoring can be achieved by activity recognition using classification method. For the needs of performance testing, we evaluate some classification method for activity recognition to compare the among classification method for the activity recognition. Some tested classification methods are Naïve Bayes, KNN, KStar and TreeJ48. Furthermore, we tested the impact of sliding windows per N samples taken to the accuracy of the activity recognition. We choose the best N sample that could give the best accuracy for activity recognition. The system not only monitor the patient’s activity, but also the patient’s position. The position monitoring can be achieved using Google Maps API. The result is Naive bayes has the accuracy of 81.25%, KNN has the accuracy of 78.125%, KStar has the accuracy of 78.125% and TreeJ48 has the accuracy of 75%. The N sample that could give the best accuracy is 6 with the accuracy of 90.15%.

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A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory

Cited by 23 publications

References 52 publications

Fusion of smartphone sensor data for classification of daily user activities

Fusion of smartphone sensor data for classification of daily user activities

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

Mobile-based Activity Monitoring System for the Self-quarantine Patient

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