Deep ConvLSTM Network with Dataset Resampling for Upper Body Activity Recognition Using Minimal Number of IMU Sensors
| Autor: | Noor Azah Abd Aziz, Xiang Yang Lim, Kok Beng Gan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
030506 rehabilitation
Computer science hybrid neural network 02 engineering and technology Accelerometer lcsh:Technology Hybrid neural network Activity recognition lcsh:Chemistry 03 medical and health sciences Inertial measurement unit Resampling Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering General Materials Science Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes inertia measurement unit (IMU) business.industry lcsh:T Process Chemistry and Technology Deep learning General Engineering deep learning Pattern recognition lcsh:QC1-999 Computer Science Applications Identification (information) ComputingMethodologies_PATTERNRECOGNITION lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 imbalanced dataset human activity recognition (HAR) 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 3543, p 3543 (2021) Applied Sciences Volume 11 Issue 8 |
| ISSN: | 2076-3417 |
| Popis: | Human activity recognition (HAR) is the study of the identification of specific human movement and action based on images, accelerometer data and inertia measurement unit (IMU) sensors. In the sensor based HAR application, most of the researchers used many IMU sensors to get an accurate HAR classification. The use of many IMU sensors not only limits the deployment phase but also increase the difficulty and discomfort for users. As reported in the literature, the original model used 19 sensor data consisting of accelerometers and IMU sensors. The imbalanced class distribution is another challenge to the recognition of human activity in real-life. This is a real-life scenario, and the classifier may predict some of the imbalanced classes with very high accuracy. When a model is trained using an imbalanced dataset, it can degrade model’s performance. In this paper, two approaches, namely resampling and multiclass focal loss, were used to address the imbalanced dataset. The resampling method was used to reconstruct the imbalanced class distribution of the IMU sensor dataset prior to model development and learning using the cross-entropy loss function. A deep ConvLSTM network with a minimal number of IMU sensor data was used to develop the upper-body HAR model. On the other hand, the multiclass focal loss function was used in the HAR model and classified minority classes without the need to resample the imbalanced dataset. Based on the experiments results, the developed HAR model using a cross-entropy loss function and reconstructed dataset achieved a good performance of 0.91 in the model accuracy and F1-score. The HAR model with a multiclass focal loss function and imbalanced dataset has a slightly lower model accuracy and F1-score in both 1% difference from the resampling method. In conclusion, the upper body HAR model using a minimal number of IMU sensors and proper handling of imbalanced class distribution by the resampling method is useful for the assessment of home-based rehabilitation involving activities of daily living. |
| Databáze: | OpenAIRE |
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