Activity Recognition Based on Smartphone and Dual-Tree Complex Wavelet Transform

Wang, Chi; Zhang, Wei

doi:10.1109/iscid.2015.51

Cited by 7 publications

(11 citation statements)

References 9 publications

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“…In fact, there are some differences and difficulties in development for Android or IOS systems, the 2 most used phone operating systems worldwide. Android is currently the most popular system and has the advantage of being convenient from the programming point of view [7]. Scanning rates of sensors are found to be superior with this operating system [3].…”

Section: Resultsmentioning

confidence: 99%

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Trifan

Oliveira

2019

JMIR Mhealth Uhealth

110

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Background Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations. Objective This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being–related outcomes. In addition, it explores users’ engagement and possible challenges in using such self-monitoring systems. Methods A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems? Results Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred. Conclusions One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users’ health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.

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Section: Resultsmentioning

confidence: 99%

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Trifan

Oliveira

2019

JMIR Mhealth Uhealth

110

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“…The J48 decision tree, Random Forest, Instance-based learning (IBk), and rule induction (J-Rip) methods were used with accelerometer data for the recognition of standing, sitting, going up stairs, going down stairs, walking, and jogging, implementing the Dual-tree complex wavelet transform (DT-CWT), DT-CWT statistical information and orientation as features, reporting an accuracy of 86% for the recognition of all activities [27].…”

Section: Related Workmentioning

confidence: 99%

“…The features used are mean, variance, standard deviation, median, minimum, maximum, range, Interquartile range, Kurtosis, skewness and spectrum peak position of the accelerometer data, reporting an accuracy of 93.8% [25]. Another authors implemented the Sliding-Window-based Hidden Markov Model (SW-HMM), and compared this method with SVM and ANN for the recognition of walking, standing, running, going up stairs, and going down stairs activities, using the mean, variance and quartiles of the accelerometer data [26], reporting an accuracy around 80%.The J48 decision tree, Random Forest, Instance-based learning (IBk), and rule induction (J-Rip) methods were used with accelerometer data for the recognition of standing, sitting, going up stairs, going down stairs, walking, and jogging, implementing the Dual-tree complex wavelet transform (DT-CWT), DT-CWT statistical information and orientation as features, reporting an accuracy of 86% for the recognition of all activities [27].The authors of [28] created a system named Actitracker, that performs the recognition of walking, jogging, going up stairs, going down stairs, standing, sitting, and lying down activities, using the Random Forest method and accelerometer data. This systems uses the mean and standard deviation for each axis, the bin distribution and the heuristic measure of wave periodicity, with an accuracy around 90% [28].In [10], the authors implemented a solution using ANN and SVM methods applied to the accelerometer data, in order to identify several activities, such as standing, sitting, standing up from a chair, sitting down on a chair, walking, lying, and falling activities.…”

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confidence: 99%

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

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This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%. Following the previous research studies [4][5][6], the recognition of the ADL is composed by several steps, such as the data acquisition, the data processing, composed by data cleaning, data imputation, and feature extraction, the data fusion, and the artificial intelligence methods for the concrete identification of the ADL. However, this study only uses the accelerometer sensor, removing some steps of the proposed architecture. Based on the assumption that the sensor was always acquiring the data, the final steps used are the data acquisition, the data cleaning, and the application of the artificial intelligence methods.During the last years, the recognition of ADL has been studies by several authors [7][8][9][10][11][12], verifying that the Artificial Neural Networks (ANN) are widely used. This paper proposes the creation of a method for the recognition of ADL using accelerometer, comparing three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning, in order to verify the method that achieves the best accuracy in the recognition of running, walking, going upstairs, going downstairs, and standing. The datasets are composed with raw accelerometer data acquired by individual with ages ranged between 16 and 60 years old and different lifestyles, with a mobile device in the pocket. For the implementation of these types of ANN are used several datasets with different sets of features, identifying the best features to increase the accuracy of the recognition, and three Java libraries are used for the implementation of the different methods, such as Neuroph [13], Encog [14], and DeepLearning4j [15], achieving the best accuracy with Deep Learning methods.The remaining sections of this paper are organized as follows: Section 2 presents a brief literature review re...

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“…walking) from those without a repetition, Ustev et al [27] add frequency domain features and improve recognition accuracy. Wang and Zhang [28] propose a method for extracting wavelet domain features including both time domain and frequency domain features to recognize six activities. From these previous studies, we find that the extracted features are greatly relevant to the analyzed activities.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost

Zhang

Zhao

2019

IEEE Access

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Recently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor changes and the improvement of positioning accuracy according to indoor landmarks. However, we have not found a comprehensive study for recognizing indoor activities related to multi-floor indoor positioning based on a robust machine learning algorithm. In this work, we propose a framework for recognizing five indoor activities, i.e., walking, stillness, stair climbing, escalator, or elevator taking. In this framework, we investigate the relevant sensors and features to improve the recognition accuracy of these activities, especially some specific features in the frequency domain and wavelet domain. We propose to utilize a promising tree-based ensemble learning classifier, XGBoost, to recognize these activities. Based on our dataset created by 40 volunteers, we provide a comprehensive analysis of the proposed framework for indoor activity recognition. Considering both accuracy and computational cost, the XGBoost-based indoor activity recognition algorithm outperforms the other ensemble learning classifiers and single classifiers, and the average recognition F-score of XGBoost reaches 84.41%. In addition, our introduced specific features in the frequency domain and wavelet domain can significantly improve the recognition accuracy. Moreover, we use a publicly available dataset to verify our proposed framework and XGBoost classifier reaches 84.19% that outperforms the other classifiers.

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Activity Recognition Based on Smartphone and Dual-Tree Complex Wavelet Transform

Cited by 7 publications

References 9 publications

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost

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