Modelling and Analysis of Various Machine Learning Algorithms for Human Activity Recognition

It becomes essential to monitor the Activity of Daily Living(ADL) of elderly people living alone by keeping track of their day to day activities & helping those having strong health issues. In this paper various machine learning algorithms for human activity recognition is analyzed. Along with this, an extensive study is carried out to learn about the current technologies used in activity recognition. Activity recognition is generally done in the form of signals generated through sensors. The signals are then preprocessed, segmented, features are extracted and activity is recognized. The main objective of Human Activity Recognition System is to explore the limitations of self-dependent old age persons and suggest ways of overcoming it. By using the different wearable and non-wearable sensors, one can easily monitor the human activity and evaluate the data generated through it.

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Deep learning and model personalization in sensor-based human activity recognition

Journal of Reliable Intelligent Environments ◽

10.1007/s40860-021-00167-w ◽

2022 ◽

Author(s):

Anna Ferrari ◽

Daniela Micucci ◽

Marco Mobilio ◽

Paolo Napoletano

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Wearable Sensors ◽

Human Activity Recognition ◽

Population Diversity ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Learning Techniques

AbstractHuman activity recognition (HAR) is a line of research whose goal is to design and develop automatic techniques for recognizing activities of daily living (ADLs) using signals from sensors. HAR is an active research filed in response to the ever-increasing need to collect information remotely related to ADLs for diagnostic and therapeutic purposes. Traditionally, HAR used environmental or wearable sensors to acquire signals and relied on traditional machine-learning techniques to classify ADLs. In recent years, HAR is moving towards the use of both wearable devices (such as smartphones or fitness trackers, since they are daily used by people and they include reliable inertial sensors), and deep learning techniques (given the encouraging results obtained in the area of computer vision). One of the major challenges related to HAR is population diversity, which makes difficult traditional machine-learning algorithms to generalize. Recently, researchers successfully attempted to address the problem by proposing techniques based on personalization combined with traditional machine learning. To date, no effort has been directed at investigating the benefits that personalization can bring in deep learning techniques in the HAR domain. The goal of our research is to verify if personalization applied to both traditional and deep learning techniques can lead to better performance than classical approaches (i.e., without personalization). The experiments were conducted on three datasets that are extensively used in the literature and that contain metadata related to the subjects. AdaBoost is the technique chosen for traditional machine learning, while convolutional neural network is the one chosen for deep learning. These techniques have shown to offer good performance. Personalization considers both the physical characteristics of the subjects and the inertial signals generated by the subjects. Results suggest that personalization is most effective when applied to traditional machine-learning techniques rather than to deep learning ones. Moreover, results show that deep learning without personalization performs better than any other methods experimented in the paper in those cases where the number of training samples is high and samples are heterogeneous (i.e., they represent a wider spectrum of the population). This suggests that traditional deep learning can be more effective, provided you have a large and heterogeneous dataset, intrinsically modeling the population diversity in the training process.

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Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance

Sensors ◽

10.3390/s20133647 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3647

Author(s):

Sebastian Scheurer ◽

Salvatore Tedesco ◽

Brendan O’Flynn ◽

Kenneth N. Brown

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Human Activity ◽

Recognition Performance ◽

Human Activity Recognition ◽

Machine Learning Algorithms ◽

Specific Target ◽

Single Person ◽

Target User ◽

The Subject

The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.

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Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2019040103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 34-47 ◽

Cited By ~ 1

Author(s):

Bagavathi Lakshmi ◽

S.Parthasarathy

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Action Recognition ◽

Human Activity ◽

Nearest Neighbor ◽

Human Action Recognition ◽

Human Action ◽

Human Activity Recognition ◽

Machine Learning Algorithms ◽

Support Vector

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).

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