PCA Based Optimal ANN Classifiers for Human Activity Recognition Using Mobile Sensors Data

Walse, K. H.; Dharaskar, Rajiv V.; Thakare, V. M.

doi:10.1007/978-3-319-30933-0_43

Cited by 40 publications

(21 citation statements)

References 5 publications

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“…(Vepakomma et al, 2015) first extracted hand-engineered features from the sensors, then those features are fed into a DNN model. Similarly, (Walse et al, 2016) performed PCA before using DNN. In those work, DNN only served as a classification model after hand-crafted feature extraction, hence they…”

Section: Deep Neural Networkmentioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,555

927

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Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• We survey deep learning based HAR in sensor modality, deep model, and application.• We comprehensively discuss the insights of deep learning models for HAR tasks.• We extensively investigate why deep learning can improve the performance of HAR.• We also summarize the public HAR datasets frequently used for research purpose.• We present some grand challenges and feasible solutions for deep learning based HAR. ABSTRACTSensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

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Section: Deep Neural Networkmentioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,555

927

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“…FCN was used from the early stage right after the introduction of the deep learning. However, humans were still responsible for determining features, while FCN performed classification based on the handcrafted features [ 22 , 23 ]. The FCN models sometimes failed for generalization because the human-determined features were too shallow to be general enough for various tasks.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

Jun

Choi

2020

Sensors

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Human activity recognition (HAR) works have mostly focused on the activities of adults. However, HAR is typically beneficial to the safety and wellness of newborn or infants because they have difficulties in verbal communication. The activities of infants are different from those of adults in terms of its types and intensity. Hence, it is necessary to study the behavior of infants separately. We study newborn and infant activity recognition by analyzing accelerometer data from the sensors attached to body. We aim to classify four types of activities: sleeping, moving in agony, moving in normal condition, and movement by external force. For this work, we collected 11 h videos and corresponding sensor data from 10 infant subjects. For recognition, we propose an end-to-end deep model using autoencoder and k-means clustering, which is trained in an unsupervised way. From a set of performance tests, our model can achieve 0.96 in balanced accuracy and F-1 score of 0.95.

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“…The other approach is to learn features automatically with different network models. Due to the nonlinearity of activation function, raw data are translated into features or even the probability of each category [39,40]. Compared to the aforementioned approach, neither final features nor intermediate outputs of hidden layers in most network models have definite meanings, instead by sketchy concepts.…”

Section: Related Workmentioning

confidence: 99%

A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory

Zhang

Chao

2020

Sensors

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As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training.

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PCA Based Optimal ANN Classifiers for Human Activity Recognition Using Mobile Sensors Data

Cited by 40 publications

References 5 publications

Deep learning for sensor-based activity recognition: A survey

Deep learning for sensor-based activity recognition: A survey

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory

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