Comparison of Classification Algorithms for Physical Activity Recognition

Peterek, Tomáš; Penhaker, Marek; Gajdoš, Petr; Dohnálek, Pavel

doi:10.1007/978-3-319-01781-5_12

Cited by 29 publications

(14 citation statements)

References 11 publications

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“…The PCA components can be counted by X = YP , where X and Y are centering and input matrix, respectively and P is a matrix of eigenvector of the covariance vector matrix C x = PΛP T . Λ is a diagonal matrix whose diagonal elements are the eigenvalues corresponding to each eigenvector [19]. The new feature vectors are so-called principal components and arranged according to their variance (from largest to lowest).…”

Section: Backgrounds and Methodologiesmentioning

confidence: 99%

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian

Fekr

Radecka

et al. 2017

Sensors

181

137

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Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

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Section: Backgrounds and Methodologiesmentioning

confidence: 99%

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian

Fekr

Radecka

et al. 2017

Sensors

181

137

View full text Add to dashboard Cite

show abstract

“…Traditional methods, which have been developed to facilitate human activity recognition, are inside the range of supervised learning. For instance, the pervious methods include SVM [8] and Random Forest [9], which require to extract handcrafted features as the inputs of classifiers. Later, deep learning, and in particular, convolutional neural networks, has been diffusely used in the field of HAR.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Convolutional Neural Network for Weakly Labeled Human Activities’ Recognition With Wearable Sensors

2019

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Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a timeconsuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attentionbased convolutional neural network for human recognition from weakly labeled data. The proposed attention model can focus on labeled activity among a long sequence of sensor data, and while filter out a large amount of background noise signals. In experiment on the weakly labeled dataset, we show that our attention model outperforms classical deep learning methods in accuracy. Besides, we determine the specific locations of the labeled activity in a long sequence of weakly labeled data by converting the compatibility score which is generated from attention model to compatibility density. Our method greatly facilitates the process of sensor data annotation, and makes data collection more easy.Index Terms-Human activity recognition, attention-based convolutional neural network, compatibility density, weakly supervised learning, wearable sensor data.

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“…A wide range of supervised methods commonly used for activity classification was reviewed in [Preece et al 2009;Peterek et al 2014]. One main characteristic of these methods is the necessity of a significant amount of labelled data to build the classification model.…”

Section: Learning Approachesmentioning

confidence: 99%

Activity Recognition with Evolving Data Streams

et al. 2018

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Activity recognition aims to provide accurate and opportune information on people's activities by leveraging sensory data available in today's sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed and the possible directions of future research are indicated. CCS Concepts: r General and reference → Surveys and overviews; r Theory of computation → Streaming models; Machine learning theory; r Computing methodologies → Transfer learning; r Hardware → Sensor applications and deployments; Sensor devices and platforms;

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Comparison of Classification Algorithms for Physical Activity Recognition

Cited by 29 publications

References 11 publications

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Attention-Based Convolutional Neural Network for Weakly Labeled Human Activities’ Recognition With Wearable Sensors

Activity Recognition with Evolving Data Streams

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