A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian, Majid; Fekr, Atena Roshan; Radecka, Katarzyna; Žilić, Željko

doi:10.3390/s17030529

Cited by 174 publications

(152 citation statements)

References 58 publications

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“…The results are slightly different for other accelerometer placements, but the trend of mid-size windows performing best holds [26].…”

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 82%

“…According to [26], mid-sized time windows (from 5 to 7 s long) perform best from a range of windows from 1 to 15 s for wrist-placed accelerometers. The results are slightly different for other accelerometer placements, but the trend of mid-size windows performing best holds [26].…”

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 99%

“…Alternatively, methods such as Principal Component Analysis (PCA) are used to map the original features into a lower dimensional subspace with mutually uncorrelated components. Reducing the number of features significantly reduces the computational effort of the classification process [26].…”

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 99%

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A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

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“…The results are slightly different for other accelerometer placements, but the trend of mid-size windows performing best holds [26].…”

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 82%

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 99%

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A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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“…Machine learning techniques have been widely used for activity monitoring [15]. Depending on the nature of the task, supervised or unsupervised algorithms can be employed.…”

Section: Related Workmentioning

confidence: 99%

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

Mehrang

Pietilä

Korhonen

2018

Sensors

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Wrist-worn sensors have better compliance for activity monitoring compared to hip, waist, ankle or chest positions. However, wrist-worn activity monitoring is challenging due to the wide degree of freedom for the hand movements, as well as similarity of hand movements in different activities such as varying intensities of cycling. To strengthen the ability of wrist-worn sensors in detecting human activities more accurately, motion signals can be complemented by physiological signals such as optical heart rate (HR) based on photoplethysmography. In this paper, an activity monitoring framework using an optical HR sensor and a triaxial wrist-worn accelerometer is presented. We investigated a range of daily life activities including sitting, standing, household activities and stationary cycling with two intensities. A random forest (RF) classifier was exploited to detect these activities based on the wrist motions and optical HR. The highest overall accuracy of 89.6 ± 3.9% was achieved with a forest of a size of 64 trees and 13-s signal segments with 90% overlap. Removing the HR-derived features decreased the classification accuracy of high-intensity cycling by almost 7%, but did not affect the classification accuracies of other activities. A feature reduction utilizing the feature importance scores of RF was also carried out and resulted in a shrunken feature set of only 21 features. The overall accuracy of the classification utilizing the shrunken feature set was 89.4 ± 4.2%, which is almost equivalent to the above-mentioned peak overall accuracy.

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“…HumanEva [12] , CMU Mocap [13] , and those reported in the survey by Chaquet et al [14] , are examples of datasets including samples from cameras, while the works by Wang et al [15] and Urtasum et al [16] are some examples of methods for learning dynamical models of human motion from camera data. Only very recently, Janidarmian et al combined 14 publicly available datasets focusing on acceleration patterns in order to conduct an analysis on feature representations and classification techniques for human activity recognition [17]. Unfortunately, they do not make the resulting dataset available for downloading.…”

Section: Introductionmentioning

confidence: 99%

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

2017

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Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics.

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A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Cited by 174 publications

References 58 publications

A Comprehensive Study of Activity Recognition Using Accelerometers

A Comprehensive Study of Activity Recognition Using Accelerometers

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

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