Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer

Asim, Yusra; Azam, Muhammad Awais; Ehatisham-ul-Haq, Muhammad; Naeem, Usman; Khalid, Asra

doi:10.1109/jsen.2020.2964278

Cited by 47 publications

(16 citation statements)

References 37 publications

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“…Prior activity recognition and health assessment research offers evidence that movement-based sensors including accelerometers and gyroscopes provide insight on behavior patterns “in the wild” [ 26 ]–[ 28 ]. Many of these previous efforts analyze movement-based activities such as walking, lying down, and exercising.…”

Section: Methodsmentioning

confidence: 99%

Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Cook

Schmitter‐Edgecombe

2021

IEEE Access

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Advances in machine learning and low-cost, ubiquitous sensors offer a practical method for understanding the predictive relationship between behavior and health. In this study, we analyze this relationship by building a behaviorome, or set of digital behavior markers, from a fusion of data collected from ambient and wearable sensors. We then use the behaviorome to predict clinical scores for a sample of n = 21 participants based on continuous data collected from smart homes and smartwatches and automatically labeled with corresponding activity and location types. To further investigate the relationship between domains, including participant demographics, self-report and external observation-based health scores, and behavior markers, we propose a joint inference technique that improves predictive performance for these types of high-dimensional spaces. For our participant sample, we observe correlations ranging from small to large for the clinical scores. We also observe an improvement in predictive performance when multiple sensor modalities are used and when joint inference is employed.

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Section: Methodsmentioning

confidence: 99%

Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Cook

Schmitter‐Edgecombe

2021

IEEE Access

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“…The reviewed literature included a broad range of classifiers, from simple decision trees 18 , k-nearest neighbors 65 , support vector machines [91][92][93] , logistic regression 21 , naïve Bayes 94 , and fuzzy logic 64 to ensemble classifiers such as random forest 76 , XGBoost 95 , AdaBoost 45,96 , bagging 24 , and deep neural networks 48,60,82,[97][98][99] . Simple classifiers were frequently compared to find the best solution in the given measurement scenario 43,53,[100][101][102] . A similar type of analysis was implemented for ensemble classifiers 79 .…”

Section: Activity Classificationmentioning

confidence: 99%

A systematic review of smartphone-based human activity recognition methods for health research

2021

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Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

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“…Hence the power of the automated feature extraction approach, i.e. deep learning, has attracted increasing interest from various research areas such as cognitive assistance and context-aware systems [16], [17] and human activity recognition [18]- [20]. Different techniques based on deep learning have been proposed in activity recognition on various benchmark datasets.…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

et al. 2022

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Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domainwise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.

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Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer

Cited by 47 publications

References 37 publications

Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

A systematic review of smartphone-based human activity recognition methods for health research

Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

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