A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.

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“…Three main types of windowing are mainly used in HAR: activity-defined windows, event-defined windows, and sliding windows [53].…”

Section: Window Typementioning

confidence: 99%

Trends in human activity recognition using smartphones

Ferrari

Micucci

Mobilio

et al. 2021

J Reliable Intell Environ

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“…In some situations, it is not suitable to recognize activities several minutes or hours after they occur, for example, in case of emergencies, such as fall detection. Quigley et al [47] have studied and compared different windowing approaches.…”

Section: Data Segmentationmentioning

confidence: 99%

A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning

Bouchabou

Nguyen

Lohr

et al. 2021

Sensors

109

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Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field.

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“…For experiments, the Sensor Event Windows (SEW) [14] was used. The SEW approach divides the data into equal sensor event intervals.…”

Section: Sliding Windowmentioning

confidence: 99%

“…Therefore, the duration of the windows may vary. Authors of [14] compared different windows types and conclude that Time Windows (TW) provides the best accuracy and F-Measure score. They consider SEW as the second-best window method because SEW are able to classify more activities than TW.…”

Section: Sliding Windowmentioning

confidence: 99%

Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

Bouchabou

Nguyen

Lohr

et al. 2021

Communications in Computer and Information Science

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Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it is a challenging problem in terms of environments' variability, sensory-motor systems, user habits, but also sparsity of signals and redundancy of models. Therefore, end-to-end systems fail at automatically extracting key features, and need to access context and domain knowledge. We propose to tackle feature extraction for activity recognition in smart homes by merging methods of Natural Language Processing (NLP) and Time Series Classification (TSC) domains.We evaluate the performance of our method with two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). We analyze the contributions of the use of embedding based on term frequency encoding, to improve automatic feature extraction. Moreover we compare the classification performance of Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain knowledge gained by event encoding and embedding improves significantly the performance of classifiers.

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A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

Cited by 21 publications

References 19 publications

Trends in human activity recognition using smartphones

Trends in human activity recognition using smartphones

A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning

Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

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