A sensor based on recognition activities using smartphone

Beily, Marthen; Badjowawo, Maria D; Bekak, Daud Obed; Dana, Sumartini

doi:10.1109/isitia.2016.7828692

Cited by 14 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [51] extracted only the mean, variance and correlation for all axis of the accelerometer data, applying the Naïve Bayes, MLP, J48 decision tree, and SVM for the recognition of all activities when the user is playing tennis, reporting an accuracy of 100% with SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

View full text Add to dashboard Cite

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%. Following the previous research studies [4][5][6], the recognition of the ADL is composed by several steps, such as the data acquisition, the data processing, composed by data cleaning, data imputation, and feature extraction, the data fusion, and the artificial intelligence methods for the concrete identification of the ADL. However, this study only uses the accelerometer sensor, removing some steps of the proposed architecture. Based on the assumption that the sensor was always acquiring the data, the final steps used are the data acquisition, the data cleaning, and the application of the artificial intelligence methods.During the last years, the recognition of ADL has been studies by several authors [7][8][9][10][11][12], verifying that the Artificial Neural Networks (ANN) are widely used. This paper proposes the creation of a method for the recognition of ADL using accelerometer, comparing three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning, in order to verify the method that achieves the best accuracy in the recognition of running, walking, going upstairs, going downstairs, and standing. The datasets are composed with raw accelerometer data acquired by individual with ages ranged between 16 and 60 years old and different lifestyles, with a mobile device in the pocket. For the implementation of these types of ANN are used several datasets with different sets of features, identifying the best features to increase the accuracy of the recognition, and three Java libraries are used for the implementation of the different methods, such as Neuroph [13], Encog [14], and DeepLearning4j [15], achieving the best accuracy with Deep Learning methods.The remaining sections of this paper are organized as follows: Section 2 presents a brief literature review re...

show abstract

Section: Related Workmentioning

confidence: 99%

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The previous phase of research mainly used traditional machine learning methods for modelling predictions. KNN, HMM, SVM, RF and XGBoost are some of the most commonly used traditional algorithms [15]. Traditional methods extract a large number of features after pre-processing the raw data and selecting some key features that represent the essential differences between different activities.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on CNN and LSTM

Tan¹

2023

Journal of Computers

View full text Add to dashboard Cite

<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p> </p>

show abstract

“…When it comes to applying MEMS sensors in tennis, most studies used tri-axial accelerometers, tri-axial gyroscopes, and tri-axial magnetometers to collect motion data. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Notably, these studies usually employed multiple wearable devices with a sampling frequency greater than 100 Hz. 9,[17][18][19][23][24][25] These wearable devices were placed around major joints of the human body and/or inside the racket's handle connected to a computer or smartphone via Bluetooth technology to transfer the collected data.…”

Section: Introductionmentioning

confidence: 99%

Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part P:

View full text Add to dashboard Cite

This study examined the reliability of a tennis stroke classification and assessment platform consisting of a single low-cost MEMS sensor in a wrist-worn wearable device, smartphone, and computer. The data that was collected was transmitted via Bluetooth and analyzed by machine learning algorithms. Twelve right-handed male elite tennis athletes participated in the study, and each athlete performed 150 strokes. The results from three machine learning algorithms regarding their recognition and classification of the real-time data stream were compared. Stroke recognition and classification went through pre-processing, segmentation, feature extraction, and classification with Support Vector Machine (SVM), including SVM without normalization, SVM with Min–Max, SVM with Z-score normalization, K-nearest neighbor (K-NN), and Naive Bayes (NB) machine learning algorithms. During the data training process, 10-fold cross-validation was used to avoid overfitting and suitable parameters were found within the SVM classifiers. The best classifier was achieved when C = 1 using the RBF kernel function. Different machine learning algorithms’ classification of unique stroke types yielded highly reliable clusters within each stroke type with the highest test accuracy of 99% achieved by SVM with Min–Max normalization and 98.4% achieved using SVM with a Z-score normalization classifier.

show abstract

A sensor based on recognition activities using smartphone

Cited by 14 publications

References 7 publications

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Human Activity Recognition Based on CNN and LSTM

Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data

Contact Info

Product

Resources

About