Physical activity recognition by smartphones, a survey

Morales, Jafet; Akopian, David

doi:10.1016/j.bbe.2017.04.004

Cited by 106 publications

(102 citation statements)

References 27 publications

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“…Smartphones contain various sensors that can provide relevant information about the intensity and type of PA while the subject is carrying the device. Accelerometer, magnetometer, gyroscope, and GPS have a clear association with PA, but additionally light, proximity and WiFi sensors, barometers, microphones, and cameras can provide extra modalities for PA analysis (for overview see [48]). The interactive nature of the smartphone also allows for attempts at influencing the users' PA [49], which itself is an important field of inquiry for promoting PA behavior change among the youth [50].…”

Section: Methods Of Assessing Physical Activity Of Childrenmentioning

confidence: 99%

Computer Vision-Based Unobtrusive Physical Activity Monitoring in School by Room-Level Physical Activity Estimation: A Method Proposition

Hõrak

2019

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As sedentary lifestyles and childhood obesity are becoming more prevalent, research in the field of physical activity (PA) has gained much momentum. Monitoring the PA of children and adolescents is crucial for ascertaining and understanding the phenomena that facilitate and hinder PA in order to develop effective interventions for promoting physically active habits. Popular individual-level measures are sensitive to social desirability bias and subject reactivity. Intrusiveness of these methods, especially when studying children, also limits the possible duration of monitoring and assumes strict submission to human research ethics requirements and vigilance in personal data protection. Meanwhile, growth in computational capacity has enabled computer vision researchers to successfully use deep learning algorithms for real-time behaviour analysis such as action recognition. This work analyzes the weaknesses of existing methods used in PA research; gives an overview of relevant advances in video-based action recognition methods; and proposes the outline of a novel action intensity classifier utilizing sensor-supervised learning for estimating ambient PA. The proposed method, if applied as a distributed privacy-preserving sensor system, is argued to be useful for monitoring the spatio-temporal distribution of PA in schools over long periods and assessing the efficiency of school-based PA interventions.

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Section: Methods Of Assessing Physical Activity Of Childrenmentioning

confidence: 99%

Computer Vision-Based Unobtrusive Physical Activity Monitoring in School by Room-Level Physical Activity Estimation: A Method Proposition

Hõrak

2019

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“…In this sense, data cleansing represents a relevant procedure to improve the quality of datasets and to improve the outcomes of the classifiers. Data cleansing can be defined as the process of removing errors or inconsistencies such as noise and/or outliers from a collection of data [34]. The presence of noise and outliers in data is an important issue to address as these can have a substantial influence on the results produced by data driven techniques, according to Gorunescu [35].…”

Section: Human Activity Recognitionmentioning

confidence: 99%

Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition

Neira-Rodado

Nugent

Cleland

et al. 2020

Sensors

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Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.

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“…The contemporary IMU of smartphone devices has many applications. Among them is analysis and recognition of human activity-a survey on this subject can be found in in [3,4]. Beside IMU sensors, an up to date smartphone device contains efficient GPU and high resolution screen.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Hachaj

Piekarczyk

2019

Sensors

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The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.

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Physical activity recognition by smartphones, a survey

Cited by 106 publications

References 27 publications

Computer Vision-Based Unobtrusive Physical Activity Monitoring in School by Room-Level Physical Activity Estimation: A Method Proposition

Computer Vision-Based Unobtrusive Physical Activity Monitoring in School by Room-Level Physical Activity Estimation: A Method Proposition

Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

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