Prayer Activity Recognition Using Smartphone

Topu, Tofayel Ahamed; Rahman, Md. Mahafuzar; Hossain, Md. Sumon; Marouf, Ahmed Al

doi:10.1109/icccnt51525.2021.9579974

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Next, recognition of Takbeer and differentiation between the postures of standing and short-standing could not be done previously due to using a smartphone. The size of the datasets used in the earlier studies is also a matter of concern as the majority cover only 10 subjects [21], [22], [25] or even less [24].…”

Section: Motivations Behind Our Studymentioning

confidence: 99%

“…Moreover, none of the existing studies reported perfect or near-perfect accuracy, which is important for such sensitive usage. In fact, the adoption of cross-validation alone to measure the performance of the models of almost all the studies [22]- [24], raise serious concern as to whether they truly reflect the models' realistic performances [35]. Additionally, the accuracy of HAR solution for Salat might hamper if a person performs any extra activity in Salat, that does not nullify prayer [36] as shown in Figure 1.…”

Section: Motivations Behind Our Studymentioning

confidence: 99%

“…FIGURE 4: Suggested smartphone placement positions in [22] and [21] They found the upper arm placement of the smartphone yielding better accuracy which is not a convenient position at all. Lastly, the study in [24], recognized six steps in prayer using three smartphone sensors placing the smartphone in the pant's pocket. Therefore, we see that the placement of smartphones is inconvenient or impractical in these studies (Figure 4).…”

Section: Har Research Related To Salatmentioning

confidence: 99%

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Leveraging a Smartwatch for Activity Recognition in Salat

Jahan,

Al-Nabhan,

Noor

et al. 2023

IEEE Access

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Salat, the most important worship of Muslims and the second pillar of Islam, is an integral part of the Muslim community. Being a complex activity, Salat involves a series of steady and transitional activities to be performed in a specific sequence. On top of that, Salat has variations based on time, priority, school of thought, etc., making activity recognition in Salat more challenging. Existing research studies related to recognizing individual activities in Salat either demand capturing images by a camera or carrying a smartphone (sometimes in inconvenient places) while praying. Both of the demands are not convenient or applicable in real cases. Besides, the existing studies lack user-independent accuracy analysis and fine-grained prediction. To address these gaps, in this study, we first assess the requirement and acceptability of technological solutions for activity recognition in Salat by conducting an exploratory study. Upon establishing the requirement, we propose an activity recognition methodology using a smartwatch to recognize different activities in Salat. We prepare a Salat activity dataset using a smartwatch and propose a new methodology using semantic rules and Dynamic Time Warping (DTW) that achieves a near-perfect accuracy (99.3%) in recognizing activities in Salat. Besides, our proposed methodology offers fine-grained recognition of the individual activities in Salat and is robust enough to overlook the extra transitional activities a person performs while praying, which does not nullify Salat. Therefore, this research is expected to lead to a comprehensive solution for monitoring Salat.

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Section: Motivations Behind Our Studymentioning

confidence: 99%

Section: Motivations Behind Our Studymentioning

confidence: 99%

Section: Har Research Related To Salatmentioning

confidence: 99%

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Leveraging a Smartwatch for Activity Recognition in Salat

Jahan,

Al-Nabhan,

Noor

et al. 2023

IEEE Access

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Recognizing salat activity using deep learning models via smartwatch sensors

Vurgun,

Kiran

2024

Neural Comput & Applic

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In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying.

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Stacked LSTM Deep Neural Networks for Accurate Recognition of Prayer Activities with Smartphone Sensors

Syed

2024

Arab J Sci Eng

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Prayer Activity Recognition Using Smartphone

Cited by 6 publications

References 8 publications

Leveraging a Smartwatch for Activity Recognition in Salat

Leveraging a Smartwatch for Activity Recognition in Salat

Recognizing salat activity using deep learning models via smartwatch sensors

Stacked LSTM Deep Neural Networks for Accurate Recognition of Prayer Activities with Smartphone Sensors

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