Context-aware and dynamically adaptable activity recognition with smart watches: A case study on smoking

Ağaç, Sümeyye; Shoaib, Muhammad; İncel, Özlem Durmaz

doi:10.1016/j.compeleceng.2020.106949

Cited by 15 publications

(4 citation statements)

References 6 publications

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“…Sensor-based HAR uses data from a wide range of sensors integrated with wearable devices to capture time series [85], [86]. The authors study a context-conscious HAR system in [87] and note that an accelerometer is mainly suitable to detect simple walking and sitting activities. When gyroscope data are added, system detection performance for complex activities such as drinking, eating, and smoking is increased.…”

Section: Hybrid Sensorsmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Kumar¹

2021

Preprint

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Over the past decade, recognition of human activities (HAR) has become a vibrant field of research, in particular, the spread in our everyday lives of electronics such as mobile phones, smart cell phones, and video cameras. Furthermore, the advancement in the field of deep methodologies and other paradigms have enabled scientists to enable HAR in many areas, consisting of activities in fitness and wellness. For instance, HAR is one of many resorts to support older people through day-to-day activities to support their cognition and physicality. This study is centered on the key aspects deep learning plays in the development of HAR applications. Although numerous HAR examination studies were carried out previously, there have been no overall studies on this subject, in all the earlier studies there were only specific HAR-related subjects. A detailed review covering all the main subjects in this area is therefore essential. This study discusses the latest developments and works in HAR. It separates the methods and the advantages and disadvantages of each method group. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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Section: Hybrid Sensorsmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Kumar¹

2021

Preprint

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“…There are many proposed machine learning (ML) algorithms for HAR prediction, with the five main types of algorithms as follows: algorithms based on fuzzy logic (FL) ( Medjahed et al, 2009 ; Schneider and Banerjee, 2021 ), algorithms based on probabilities ( Maswadi et al, 2021 ; Schneider and Banerjee, 2021 ), algorithms based on rules ( Hartmann et al, 2022 ; Radhika et al, 2022 ), algorithms based on distance ( Agac et al, 2021 ; Fahad and Tahir, 2021 ), and optimization-based approaches ( Muralidharan et al, 2021 ; Nguyen et al, 2021 ). The six actions recognized in HAR, including exercise, lying down, sitting, standing up, walking, and sleeping, are recognized by fuzzy rule-based inference systems using FL ( Medjahed et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Wearable sensors based on artificial intelligence models for human activity recognition

Alarfaj,

Al Madini,

Alsafran

et al. 2024

Front. Artif. Intell.

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Human motion detection technology holds significant potential in medicine, health care, and physical exercise. This study introduces a novel approach to human activity recognition (HAR) using convolutional neural networks (CNNs) designed for individual sensor types to enhance the accuracy and address the challenge of diverse data shapes from accelerometers, gyroscopes, and barometers. Specific CNN models are constructed for each sensor type, enabling them to capture the characteristics of their respective sensors. These adapted CNNs are designed to effectively process varying data shapes and sensor-specific characteristics to accurately classify a wide range of human activities. The late-fusion technique is employed to combine predictions from various models to obtain comprehensive estimates of human activity. The proposed CNN-based approach is compared to a standard support vector machine (SVM) classifier using the one-vs-rest methodology. The late-fusion CNN model showed significantly improved performance, with validation and final test accuracies of 99.35 and 94.83% compared to the conventional SVM classifier at 87.07 and 83.10%, respectively. These findings provide strong evidence that combining multiple sensors and a barometer and utilizing an additional filter algorithm greatly improves the accuracy of identifying different human movement patterns.

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“…Mobile-type sensors are, in general, worn by the subject person and generate data associated with the movements of the subject person, so it is highly likely to contain a significant amount of missing data or noise caused by the movements [2][3][4][5]. Stationary sensors are, in general, installed in places of interest, and the collected data contains relatively fewer missing values and noise compared to those of mobiles [6][7][8][9][10]. It is also important to differentiate whether the activity recognition is performed in online or offline environments.…”

Section: Introductionmentioning

confidence: 99%

Activity Prediction Based on Deep Learning Techniques

et al. 2023

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Studies on real-time PM2.5 concentrations per activity in microenvironments are gaining a lot of attention due to their considerable impact on health. These studies usually assume that information about human activity patterns in certain environments is known beforehand. However, if a person’s activity pattern can be inferred reversely using environmental information, it can be easier to access the levels of PM2.5 concentration that affect human health. This study collected the actual data necessary for this purpose and designed a deep learning algorithm that can infer human activity patterns reversely using the collected dataset. The dataset was collected based on a realistic scenario, which includes activity patterns in both indoor and outdoor environments. The deep learning models used include the well-known multilayer perception (MLP) model and a long short-term memory (LSTM) model. The performance of the designed deep learning algorithm was evaluated using training and test data. Simulation results showed that the LSTM model has a higher average test accuracy of more than 15% compared to the MLP model, and overall, we were able to achieve high accuracy of over 90% on average.

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Context-aware and dynamically adaptable activity recognition with smart watches: A case study on smoking

Cited by 15 publications

References 6 publications

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Wearable sensors based on artificial intelligence models for human activity recognition

Activity Prediction Based on Deep Learning Techniques

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