Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang, Shibo; Li, Yaxuan; Zhang, Shen; Shahabi, Farzad; Xia, Stephen; Deng, Yu; Alshurafa, Nabil

doi:10.3390/s22041476

Cited by 211 publications

(130 citation statements)

References 310 publications

(342 reference statements)

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“…The dataset selected for testing is the largest currently available for smartphone motion sensor data, covering a wide range of activities [33,[36][37][38][39][40]. However, since it was published in 2021, it has not yet been used for HAR prediction by state-of-the-art hybrid convolutional neural networks with either bidirectional long short-term memory or other deep learning models, which are considered among top contenders [54]. The random forest method prediction accuracy on this dataset [36] was slightly lower than deep learning methods achieved on other datasets.…”

Section: Discussionmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition with Transformer Model

Ľuptáková

Kubovčík

Pospı́chal

2022

Sensors

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Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human–robot interaction. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The results suggest the expected future relevance of the transformer model for human activity recognition.

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Section: Discussionmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition with Transformer Model

Ľuptáková

Kubovčík

Pospı́chal

2022

Sensors

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“…Most of the previous studies suggest only the results according to the selected hidden unit and epoch for the deeplearning model, and these reports have limitations in direct discussion with the current results. However, approaches using various sensor combinations and improved models are in progress to improve classification accuracy [11,12,[26][27][28], and it will be necessary to compare them through various sensor conditions, improved models, and data processing in terms of training efficiency in the future.…”

Section: Discussionmentioning

confidence: 99%

“…), pre-processing, etc. [11,12]. However, if we look only at the application of the same model under the same conditions, the main factors influencing the training time and accuracy of deep-learning models are the number of hidden units and epochs.…”

Section: Introductionmentioning

confidence: 99%

A Pilot Study of the Efficiency of LSTM-Based Motion Classification Algorithms Using a Single Accelerometer

et al. 2022

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Inertial sensors are widely used for classifying the motions of daily activities. Although hierarchical classification algorithms were commonly used for defined motions, deep-learning models have been used recently to classify a greater diversity of motions. In addition, ongoing studies are actively investigating algorithm efficiency (e.g., training time and accuracy). Thus, a deep-learning model was constructed in this study for the classification of a given motion based on the raw data of inertial sensors. Furthermore, the number of epochs (150, 300, 500, 750, and 900) and hidden units (100, 150, and 200) were varied in the model to determine its efficiency based on training time and accuracy, and the optimum accuracy and training time was determined. Using a basic long short-term memory (LSTM), which is a neural network known to be suitable for sequential data, the data classification training was conducted on a common desktop PC with typical specifications. The results show that the accuracy was the highest (99.82%) with 150 hidden units and 300 epochs, while the training time was also relatively short (78.15 min). In addition, the model accuracy did not always increase even when the model complexity was increased (by increasing the number of epochs and hidden units) and the training time increased as a consequence. Hence, through suitable combinations of the two factors that constitute deep-learning models according to the data, the potential development and use of efficient models have been verified. From the perspective of training optimization, this study is significant in having determined the importance of the conditions for hidden units and epochs that are suitable for the given data and the adverse effects of overtraining.

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“…Human Activity Recognition (HAR) is a hot research topic today wherein the identification of different human activities is attempted with the help of sensor data [ 1 , 2 , 3 ]. With the wide array of sensors built into modern smartphones as well as wearable devices such as fitness trackers and smartwatches, the collection of activity-specific data has become very convenient.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Information Sharing in Human Activity Recognition

Marchán

2022

Sensors

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The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various sensors, only accelerometer data were used in this study; thus, the developed methodology would preserve smartphone battery and incur low computation costs. A total of 175 different features were extracted from the pre-processed data. Data stratification was conducted in three ways to investigate the effect of information sharing between the training and testing datasets. After data balancing using only the training dataset, ten-fold and LOSO cross-validation were performed using several algorithms, including Support Vector Machine, XGBoost, Random Forest, Naïve Bayes, KNN, and Neural Network. A very simple post-processing algorithm was developed to improve the accuracy. The results reveal that XGBoost takes the least computation time while providing high prediction accuracy. Although Neural Network outperforms XGBoost, XGBoost demonstrates better accuracy with post-processing. The final detection accuracy ranges from 99.8% to 77.6% depending on the level of information sharing. This strongly suggests that when reporting accuracy values, the associated information sharing levels should be provided as well in order to allow the results to be interpreted in the correct context.

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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Cited by 211 publications

References 310 publications

Wearable Sensor-Based Human Activity Recognition with Transformer Model

Wearable Sensor-Based Human Activity Recognition with Transformer Model

A Pilot Study of the Efficiency of LSTM-Based Motion Classification Algorithms Using a Single Accelerometer

Investigating the Impact of Information Sharing in Human Activity Recognition

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