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Abstract
In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.
Highlights
Brain-damaged, and rehabilitation patients often have poor balance
The model names listed in the table are abbreviated as follows: 1D-convolutional neural network (CNN): C; gated recurrent unit (GRU): G; Double head 1D-CNN: DC; Triple-head 1D-CNN: TC; 1D-CNN after GRU: C-G; 1D-CNN and GRU parallel: C+G; and double-head 1D CNN and GRU parallel: DC+G
The model names listed in the table are abbreviated as follows: double-head 1D CNN and GRU stacking ensemble: DC+G
Summary
Brain-damaged, and rehabilitation patients often have poor balance. Human activity recognition (HAR) was introduced to monitor the motion of a subject in daily life using healthcare devices to determine measures to prevent such accidents [3,4,5]. In HAR research, various sensors are used, such as an inertial measurement unit (IMU), vision sensors, electrocardiograms (ECGs), and electromyography (EMG) devices [6]. Microelectromechanical IMU systems have small size, low cost, and low operational power requirements. They can be implemented as wearable devices (e.g., smartwatches, fitness bands, and smart clothing [9,10]). Because human health problems are most often expressed as measurable behaviors [11], IMUs
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