A hybrid approach to product prototype usability testing based on surface EMG images and convolutional neural network classification

PCG-based exercise fatigue detection method using multi-scale feature fusion model

Accurate detection of exercise fatigue based on physiological signals is vital for reasonable physical activity. Existing studies utilize widely Electrocardiogram (ECG) signals to achieve exercise monitoring. Nevertheless, ECG signals may be corrupted because of sweat or loose connection. As a non-invasive technique, Phonocardiogram (PCG) signals have a strong ability to reflect the Cardiovascular information, which is closely related to physical state. Therefore, a novel PCG-based detection method is proposed, where the feature fusion of deep learning features and linear features is the key technology of improving fatigue detection performance. Specifically, Short-Time Fourier Transform (STFT) is employed to convert 1D PCG signals into 2D images, and images are fed into the pre-trained convolutional neural network (VGG-16) for learning. Then, the fusion features are constructed by concatenating the VGG-16 output features and PCG linear features. Finally, the concatenated features are sent to Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) to distinguish six levels of exercise fatigue. The experimental results of two datasets show that the best performance of the proposed method achieves 91.47% and 99.00% accuracy, 91.49% and 99.09% F1-score, 90.99% and 99.07% sensitivity, which has comparable performance to an ECG-based system which is as gold standard (94.32% accuracy, 94.33% F1-score, 94.52% sensitivity).

Feature recognition in multiple CNNs using sEMG images from a prototype comfort test

OBJECTIVE

Deep learning-based CNN networks have recently been investigated to solve the problem of body posture recognition based on surface electromyographic signals (sEMG). Influenced by these studies, to develop a combined approach of sEMG and CNNs in the study of human-product interactions and the impact of body comfort, and to compare the advantages and disadvantages of various CNNs networks.

METHODS

In this study, sEMG measurements were carried out by building a prototype usability experiment, and the data were divided into four categories, with two types of datasets: training and testing. Four CNNs, LeNet-5, VGGNet-11, InceptionNet V4, and DenseNet, were used for the recognition of sEMG images.

RESULTS

DenseNet is another type of convolutional neural network with deep layers, which has a unique advantage over other algorithms. unique advantages over other algorithms. DenseNet has fewer layers and better accuracy than InceptionNet V4, but not only does it bypass enhanced feature reuse, but its network is easier to train and has some regularization effects, while also mitigating the problems of gradient disappearance and model degradation.

CONCLUSION

These findings could lead to a more appropriate CNN model and a useful tool for developing comfort judgments of surface EMG signals, furthering the development of products that come into contact with the human body without the need for routine retraining.

A multidimensional sensory evaluation model to investigate the (dis)comfort of body parts in a supine sitting position during a lunch break

Employees who work long hours frequently complain of muscle fatigue caused by prolonged sitting. As a result, products that assist them when resting in a chair in a reclining position, in order to relieve fatigue and improve comfort are required. To ensure that the new product works as intended, a usability test based on prototyping must be developed. The research process was divided into three stages: firstly, the development of the perception assessment questionnaire; secondly, a validated factor analysis (CFA) was conducted on the perception assessment data of 26 subjects and the measurement model was fitted to verify the reliability and validity of the questionnaire; finally, the sEMG technique was used to verify the comfort level of 21 subjects. Based on usability experiments and an exploration of human factor relationships, this study develops a prototype testing model, which focuses on the comfort perception of body parts, as a means of promoting innovation in the design and manufacturing industry.

Exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network

Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application.