Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

Ahmed, Nadeem; Rafiq, Jahir Ibna; Islam, Rashedul

doi:10.3390/s20010317

Cited by 171 publications

(80 citation statements)

References 32 publications

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“…Although SVM showed excellent results with rather short-themed activities, we consider it interesting to test it as an initial model in our dataset. It is one of the most used models in HAR, applied in works such as [9,16] and, more recently, in [23], all with outstanding overall performance in this field, as well as being a simple and straightforward AI model.…”

Section: Classification Algorithmmentioning

confidence: 99%

A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors

García-González

Rivero

Fernández-Blanco

et al. 2020

Sensors

121

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In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes it particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need for other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes asupport vector machine (SVM) model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application.

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Section: Classification Algorithmmentioning

confidence: 99%

A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors

García-González

Rivero

Fernández-Blanco

et al. 2020

Sensors

121

View full text Add to dashboard Cite

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“…Considering the wearable sensor-based technologies for HAR, a wide range of sensors have been integrated to get distinctness in the acquisition of cue understanding. Among them, a few wearable sensors like accelerometers, gyroscopes and magnetometers make it feasible to detect multiple aspects of human life and to measure position changes, angular rotation and body movements in 3-dimensional space [ 3 , 4 , 5 ]. However, despite such substantial amounts of information provided by wearable sensors, there are still some HAR challenges [ 6 , 7 ] that face the unresolved issues and lack the capability of giving perfect HAR results.…”

Section: Introductionmentioning

confidence: 99%

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

Tahir

Jalal

Kim

2020

Entropy

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Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

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“…Furthermore, ambient sensors may violate privacy. However, plenty of the existing HAR techniques are based on the data collected by ambient and smartphone sensors [ 13 , 14 , 15 , 16 ]. PAMAP2 is the dataset studied in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition

Abbaspour

Fotouhi

Sedaghatbaf

et al. 2020

Sensors

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Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.

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Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

Cited by 171 publications

References 32 publications

A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors

A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition

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