Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, Florenc; Pravadelli, Graziano; Bïhorac, Azra; Rashidi, Parisa

doi:10.1109/access.2020.3037715

Cited by 237 publications

(146 citation statements)

References 201 publications

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“…ML algorithms are categorized into two major classes: supervised learning and unsupervised learning. In supervised algorithms, the machine learns the data by looking at the relationship between the inputs and their resultant outputs; however, in unsupervised algorithms, machine learns the patterns found in the input data to build up its model parameters without having any knowledge about the outputs [81].…”

Section: Machine Learningmentioning

confidence: 99%

“…Based on this table, Regression Models, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest were among the most frequently used ML algorithms by the studies taking advantage of the chest-worn inertial sensor. For a comprehensive overview of the existing methods of signal processing on inertial data from preprocessing and feature extraction to classification, we would refer the readers to the following papers: [7,[81][82][83].…”

Section: Machine Learningmentioning

confidence: 99%

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Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Rahmani

Berkvens

Weyn

2021

Sensors

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Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Rahmani

Berkvens

Weyn

2021

Sensors

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“…HAR has numerous applications, relying on one or more of these sensors, including surveillance [5], gesture recognition [6], [7], gait analysis [8], healthcare [9], [10], and indoor navigation [11], [12]. Due to its wide applicability it has been addressed and surveyed extensively in the literature [13]- [20]. In SLR, the user's actions are reflected through changes in the location of the smartphone.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, feed forward networks (FFN) and long short term memory (LSTM) architectures were proposed for this task [24], [25], removing the burden of feature engineering while achieving improved accuracy. Recent detailed and extensive surveys describing traditional and deep learning techniques for HAR are available for the interested reader [13], [18]- [20]. There, various types of deep learning approaches such as convolutional neural networks (CNNs), recurrent neural networks, stacked autoencoders, temporal convolutional networks, and variational autoencoders are reviewed.…”

Section: Introductionmentioning

confidence: 99%

Boosting Inertial-Based Human Activity Recognition With Transformers

Shavit

Klein

2021

IEEE Access

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Activity recognition problems such as human activity recognition and smartphone location recognition can improve the accuracy of different navigation or healthcare tasks, which rely solely on inertial sensors. Current learning-based approaches for activity recognition from inertial data employ convolutional neural networks or long short term memory architectures. Recently, Transformers were shown to outperform these architectures for sequence analysis tasks. This work presents an activity recognition model based on Transformers which offers an improved and general framework for learning activity recognition tasks. For evaluation purposes, several datasets, with more than 27 hours of inertial data recordings collected by 91 users, are employed. Those datasets represent different user activity scenarios with varying difficulty. The proposed approach consistently achieves better accuracy and generalizes better across all examined datasets and scenarios.

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“…HAR is considered as an important topic; therefore many surveys have been introduced and published [9,22]. Some survey papers are categorized as classifying the activities and clustering, while the others consider features extracted with their types; anyway most of them introduce detailed HAR system as a big issue, since HAR system is complex due to including many necessary stages.…”

Section: Introductionmentioning

confidence: 99%

Weighted Classification of Machine Learning to Recognize Human Activities

Liu

Chen

2021

Complexity

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This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision.

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Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Cited by 237 publications

References 201 publications

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Boosting Inertial-Based Human Activity Recognition With Transformers

Weighted Classification of Machine Learning to Recognize Human Activities

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