Human Activity Recognition Using Multinomial Logistic Regression

Madarshahian, Ramin; Caicedo, Juan M.

doi:10.1007/978-3-319-15224-0_38

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…There are different algorithms of characterization that have been utilized for HAR: Naive Bayes, RF, k-NN, SVM, ANNs, and Recurrent Neural Networks (RNN) are among the most well-known approaches investigated. Among all, the RF algorithm is designed to deliver high accuracy and consistent speed for data mining, particularly classification with advanced features [20][21][22][23]. Unfortunately, all of these state-of-the-art algorithms are not compared very effectively in terms of performance, such as precision, accuracy, F1-score, and computation speed.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

et al. 2022

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There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation.

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Section: Introductionmentioning

confidence: 99%

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

et al. 2022

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“…In addition, wearables are unsuitable for target groups such as small animals due to the relative weight of the wearable device, which has a strong negative impact. For these target groups, non-contact activity monitoring methods are employed which use cameras [5] and other sensing methods [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Promising non-contact methods for activity monitoring have been presented previously [6][7][8][17][18][19][20][21], which are based on measurements of structural vibrations. These methods demonstrate how various activities and non-activity are detected in different use cases based on structural vibrations using various computational methods.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Activity Monitoring Using a Multi-Axial Inertial Measurement Unit in Animal Husbandry

Try¹,

Gebhard²

2022

Sensors

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In this work, a novel method is presented for non-contact non-invasive physical activity monitoring, which utilizes a multi-axial inertial measurement unit (IMU) to measure activity-induced structural vibrations in multiple axes. The method is demonstrated in monitoring the activity of a mouse in a husbandry cage, where activity is classified as resting, stationary activity and locomotion. In this setup, the IMU is mounted in the center of the underside of the cage floor where vibrations are measured as accelerations and angular rates in the X-, Y- and Z-axis. The ground truth of activity is provided by a camera mounted in the cage lid. This setup is used to record 27.67 h of IMU data and ground truth activity labels. A classification model is trained with 16.17 h of data which amounts to 3880 data points. Each data point contains eleven features, calculated from the X-, Y- and Z-axis accelerometer data. The method achieves over 90% accuracy in classifying activity versus non-activity. Activity is monitored continuously over more than a day and clearly depicts the nocturnal behavior of the inhabitant. The impact of this work is a powerful method to assess activity which enables automatic health evaluation and optimization of workflows for improved animal wellbeing.

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Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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Human Activity Recognition Using Multinomial Logistic Regression

Cited by 11 publications

References 20 publications

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Non-Contact Activity Monitoring Using a Multi-Axial Inertial Measurement Unit in Animal Husbandry

Computational Intelligence for Simulating a LiDAR Sensor

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