A Survey on Human Activity Recognition Using Accelerometer Sensor

Alrazzak, Umran; Alhalabi, Bassem

doi:10.1109/iciev.2019.8858578

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…This study did not perform extensive pre-processing to increase the generalization of the models. Only PCA was used for the dimensionality reduction for optimal processing [34]. PCA will compress the dataset into a lower-dimensional feature space with the idea of maintaining significant information.…”

Section: Data Pre-processingmentioning

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: Data Pre-processingmentioning

confidence: 99%

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

et al. 2022

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“…In the literature, the main problem Sangavi S. ( 2019); Alrazzak and Alhalabi (2019); Fellger et al (2020); Kolkar and Geetha (2021); Das et al (2023) inherent in the development of assistive technologies adapted for home care concerns artificial intelligence models allowing real-time recognition of a person's ongoing Activities of Daily Living (ADLs). Few examples could be: preparing a meal, washing hands, taking medication, etc.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living

Doukaga,

Rakotoarson,

Roberge

et al. 2024

Preprint

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The swift progression of artificial intelligence, along with the rising demand for in-home assistance for individuals facing a loss of autonomy, has driven a significant increase in research within the domain of ambient assisted living (AAL). A key challenge challenge in developing assistive technologies in an AAL context concerns the automatic recognition of the ongoing user’s activity. Most existing approaches of Human Activity Recognition (HAR) use a level of abstraction (low granularity) that is insufficient for developing efficient assistive technologies. The majority of them focus primarily on identifying broad categories of activities, such as eating or sleeping. While this identification is sufficient for monitoring general behavior, it does not enable providing practically meaningful real-time, actionable assistance. In this paper, we propose a comparative study of two novel algorithmic approaches for hand gesture recognition, intended to serve as core components of a fine-grained activity recognition model. To this end, we have defined thirteen atomic hand gestures commonly used in cooking activities. The first model we introduce utilizes inertial data, collected from a standard wristband equipped with a triaxial accelerometer and gyroscope, and applies machine learning techniques for analysis. The second model is based on a less conventional approach, employing photoplethysmography (PPG) sensors, which are rarely used for activity recognition. We detail the design and implementation of both approaches and the conducted experiments. Finally, we present a comparative analysis of the obtained results showing the potential of such approaches for the AAL.

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“…Many studies have examined the identification of human activities from diverse points of view. These include: by specialised approach [13]; by algorithm type [14], by sensor type [1,15,16]; by fuse type [17] or by device type [18], although other analyses have been carried out more generally by the HAR categories [19,20]. HAR accomplished five primary tasks, namely the recognition of the fundamental activities [21], the recognition of everyday activities [22], uncommon events [23], biometric subjects [24], and energy expenditure predictions [25].…”

Section: Related Workmentioning

confidence: 99%

Study on Temperature (τ) Variation for SimCLR based Activity Recognition

Kumar¹

2021

Preprint

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Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

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A Survey on Human Activity Recognition Using Accelerometer Sensor

Cited by 10 publications

References 28 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

Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living

Study on Temperature (τ) Variation for SimCLR based Activity Recognition

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