Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Ariza-Colpas, Paola; Vicario, Enrico; Carrascal, Ana Isabel Oviedo; Butt, Shariq Aziz; Piñeres-Melo, Marlon Alberto; Quintero-Linero, Alejandra Paola; Patara, Fulvio

doi:10.3390/s22093401

Cited by 29 publications

(11 citation statements)

References 175 publications

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“…According to the SC value of the UCI-HAR dataset, the proposed model outperforms the model presented in [26], which achieved an SC of 0.441 by implementing the K-means algorithm.…”

Section: Resultsmentioning

confidence: 88%

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

Al-Khamees¹,

Al-A’araji²,

Al-Shamery³

2022

IJFIS

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Data streams are a modern type of data that differ from traditional data in various characteristics: their indefinite size, high access, and concept drift due to their origin in non-stationary environments. Data stream clustering aims to split these data samples into significant clusters, depending on their similarity. The main drawback of data stream clustering algorithms is the large number of clusters they produce. Therefore, determining an optimal number of clusters is an important challenge for these algorithms. In practice, evolving models can change their general structure by implementing different mechanisms. This paper presents a fuzzy model that mainly consists of an evolving Cauchy clustering algorithm which is updated through a specific membership function and determines the optimal number of clusters by implementing two evolving mechanisms: adding and splitting clusters. The proposed model was tested on six different streaming datasets, namely, power supply, sensor, HuGaDB, UCI-HAR, Luxembourg, and keystrokes. The results demonstrated that the efficiency of the proposed model in producing an optimal number of clusters for each dataset outperforms that of previous models.

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“…According to the SC value of the UCI-HAR dataset, the proposed model outperforms the model presented in [26], which achieved an SC of 0.441 by implementing the K-means algorithm.…”

Section: Resultsmentioning

confidence: 88%

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

Al-Khamees¹,

Al-A’araji²,

Al-Shamery³

2022

IJFIS

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“…Advanced RL approaches for predicting prospective activity include proactive user support systems, assistive technology, healthcare, and many more. The real-time estimation of the dataset is gathered from sensors, sound, and images, among which algorithms can be highlighted and addressed soon based on Transfer Learning and Reinforcement Learning 46 .…”

Section: Related Workmentioning

confidence: 99%

An improved human activity recognition technique based on convolutional neural network

Raj,

Kos

2023

Sci Rep

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A convolutional neural network (CNN) is an important and widely utilized part of the artificial neural network (ANN) for computer vision, mostly used in the pattern recognition system. The most important applications of CNN are medical image analysis, image classification, object recognition from videos, recommender systems, financial time series analysis, natural language processing, and human–computer interfaces. However, after the technological advancement in the power of computing ability and the emergence of huge quantities of labeled data provided through enhanced algorithms, nowadays, CNN is widely used in almost every area of study. One of the main uses of wearable technology and CNN within medical surveillance is human activity recognition (HAR), which must require constant tracking of everyday activities. This paper provides a comprehensive study of the application of CNNs in the classification of HAR tasks. We describe their enhancement, from their antecedents up to the current state-of-the-art systems of deep learning (DL). We have provided a comprehensive working principle of CNN for HAR tasks, and a CNN-based model is presented to perform the classification of human activities. The proposed technique interprets data from sensor sequences of inputs by using a multi-layered CNN that gathers temporal and spatial data related to human activities. The publicly available WISDM dataset for HAR has been used to perform this study. This proposed study uses the two-dimensional CNN approach to make a model for the classification of different human activities. A recent version of Python software has been used to perform the study. The rate of accuracy for HAR through the proposed model in this experiment is 97.20%, which is better than the previously estimated state-of-the-art technique. The findings of the study imply that using DL methods for activity recognition might greatly increase accuracy and increase the range of applications where HAR can be used successfully. We have also described the future research trends in the field of HAR in this article.

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“…The IMU itself may contain several sensors, such as accelerometers and gyroscopes, which possess microelectromechanical properties, allowing their capacitance to vary with movement [ 2 ]. The accelerometer measures acceleration, while the gyroscope measures angular velocity [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…The accelerometer measures acceleration, while the gyroscope measures angular velocity [ 3 ]. Usually, Machine Learning (ML) is applied to enable an association between the signals obtained from these sensors and specific human activities [ 2 ]. The typical HAR system comprises the following steps [ 4 ]: data acquisition, preprocessing, segmentation, feature extraction, and classification.…”

Section: Introductionmentioning

confidence: 99%

Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

Bento

Rebelo

Barandas

et al. 2022

Sensors

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Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.

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Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Cited by 29 publications

References 175 publications

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters

An improved human activity recognition technique based on convolutional neural network

Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

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