Actitracker: A Smartphone-Based Activity Recognition System for Improving Health and Well-Being

Weiss, Gary M.; Lockhart, Jeffrey W.; Pulickal, Tony T.; McHugh, Paul T.; Ronan, Isaac H.; Timko, Jessica L.

doi:10.1109/dsaa.2016.89

Cited by 60 publications

(45 citation statements)

References 12 publications

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“…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].…”

Section: Related Workmentioning

confidence: 99%

“…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. The features implemented are sum of all magnitude of the vectors, sum of all magnitude of the vectors excluding the gravity, maximum and minimum value of acceleration in gravity vector direction, mean of absolute deviation of acceleration in gravity vector direction, and gravity vector changing angle, reporting that the results have a sensitivity of 96.67% and specificity of 95% [10].In order to the recognition of going up stairs, going down stairs, walking, jogging, and jumping activities, the authors of [29] used the KNN, Random Forests and SVM methods with accelerometer data to identify accurately the activities.…”

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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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Section: Related Workmentioning

confidence: 99%

mentioning

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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“…Fig 2 shows accelerometer data graph for walking, jogging, sitting and climbing downstairs. These six activities are divided into two groups: motionless activities (standing and sitting) and motion activities (stair climbing, walking and jogging) [6]. There is notable distinction between motionless activities and motion activities.…”

Section: Activity Recognition Goalmentioning

confidence: 99%

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

Wang¹,

Zhang²

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

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Smartphone contains many multiple and powerful sensors, which establishes exciting new opportunities for human-computer interaction and data mining. Those sensors placed inside smartphone are used for phone function enhancement initially. In this work, we show how general machine learning algorithms can use labeled accelerometer data to classify motion activities when users hold a smartphone. First we establish an Android-based data collection application to gain persons' motion data via accelerometer placed inside smartphone. Then we collect 6 different motion activities from 3 users. Lastly we use normal machine learning algorithm to classify those collected activities. Previous works only use time-domain features for classification. This leads to low accuracy since activity data contains frequency-domain and orientation information. In this paper, we propose a new method for extracting both timedomain and frequency-domain features. We use dual-tree complex wavelet transform (DT-CWT) as feature extraction tool. Then we use general machine learning algorithm tool WEKA for classification. Results show that our method performs better than other researcher's method which only extracts time-domain feature from accelerometer data in accuracy aspect. Our algorithm acquires accuracy at 86% by using DT-CWT statistical information and orientation feature.

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“…Several studies have implemented activity recognition or activity level estimation approaches by applying accelerometers [9,10,11,12,13,14,15,16,17,18,19,20]. For example, Kwapisz et al identified activity types through smartphones carried in the users’ pockets [11].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Kwapisz et al identified activity types through smartphones carried in the users’ pockets [11]. Weiss et al established a smartphone-based activity recognition system to monitor personal health [12]. Such studies are useful and have contributed to well-being management.…”

Section: Introductionmentioning

confidence: 99%

Exercise Performance Measurement with Smartphone Embedded Sensor for Well-Being Management

Liu¹,

Chan²

2016

IJERPH

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Regular physical activity reduces the risk of many diseases and improves physical and mental health. However, physical inactivity is widespread globally. Improving physical activity levels is a global concern in well-being management. Exercise performance measurement systems have the potential to improve physical activity by providing feedback and motivation to users. We propose an exercise performance measurement system for well-being management that is based on the accumulated activity effective index (AAEI) and incorporates a smartphone-embedded sensor. The proposed system generates a numeric index that is based on users’ exercise performance: their level of physical activity and number of days spent exercising. The AAEI presents a clear number that can serve as a useful feedback and goal-setting tool. We implemented the exercise performance measurement system by using a smartphone and conducted experiments to assess the feasibility of the system and investigated the user experience. We recruited 17 participants for validating the feasibility of the measurement system and a total of 35 participants for investigating the user experience. The exercise performance measurement system showed an overall precision of 88% in activity level estimation. Users provided positive feedback about their experience with the exercise performance measurement system. The proposed system is feasible and has a positive effective on well-being management.

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Actitracker: A Smartphone-Based Activity Recognition System for Improving Health and Well-Being

Cited by 60 publications

References 12 publications

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

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

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

Exercise Performance Measurement with Smartphone Embedded Sensor for Well-Being Management

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