Identification and Classification of Human Body Exercises on Smart Textile Bands by Combining Decision Tree and Convolutional Neural Networks

Abstract

In recent years, human activity recognition (HAR) has gained significant interest from researchers in the sports and fitness industries. In this study, the authors have proposed a cascaded method including two classifying stages to classify fitness exercises, utilizing a decision tree as the first stage and a one-dimension convolutional neural network as the second stage. The data acquisition was carried out by five participants performing exercises while wearing an inertial measurement unit sensor attached to a wristband on their wrists. However, only data acquired along the z-axis of the IMU accelerator was used as input to train and test the proposed model, to simplify the model and optimize the training time while still achieving good performance. To examine the efficiency of the proposed method, the authors compared the performance of the cascaded model and the conventional 1D-CNN model. The obtained results showed an overall improvement in the accuracy of exercise classification by the proposed model, which was approximately 92%, compared to 82.4% for the 1D-CNN model. In addition, the authors suggested and evaluated two methods to optimize the clustering outcome of the first stage in the cascaded model. This research demonstrates that the proposed model, with advantages in terms of training time and computational cost, is able to classify fitness workouts with high performance. Therefore, with further development, it can be applied in various real-time HAR applications.

A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors

“…The scientific literature has given more and more attention to the field of human activity recognition (HAR), which aims to classify human actions by exploiting sensor data [7]. HAR has covered various contexts, from industry [8,9] to sport [10], but a wider application lies in the medical field [7,[10][11][12][13][14]: in this realm, subjects' activities can be remotely registered outside the clinic [15] and clinicians can evaluate their functional abilities after treatment [16,17]. HAR can also enhance a rehabilitative program inside the clinic for the sake of an assist-as-needed approach: in particular, recognizing the motor actions performed by patients (e.g., post-stroke individuals or people with psychomotor dysfunction) can allow for correcting motions or encouraging further exercise when required [18].…”

The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models

“…In deep learning methods, the convolutional neural network (CNN) is first used for handwritten character image recognition. Then, it is extended to solve the problem of target detection, face detection, target tracking, face recognition, video classification, edge detection, image segmentation, and so on [ 8 , 9 ]. Moreover, it combines the ideas of the local field of perception, pooling, and weights sharing.…”