motion classification
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 680
Author(s):  
Sehyeon Kim ◽  
Dae Youp Shin ◽  
Taekyung Kim ◽  
Sangsook Lee ◽  
Jung Keun Hyun ◽  
...  

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group.


2021 ◽  
Vol 2115 (1) ◽  
pp. 012043
Author(s):  
Soumya Shaw ◽  
Susan Elias ◽  
Sudha Velusamy

Abstract With the most advanced classification algorithms in the technological platform, the computational power requirement is on the surge. The paper hereby presents computationally trivial algorithms to simplify the process of computational intensive classifications techniques, especially in the Motion Classification arena. The proposed methods prove crucial in acting as a lightweight and computationally fast stepping stone to a fundamentally more significant application of Motion indexing and classification, Action recognition, and predictive analysis of motion energy. The algorithms classify the motions into linear, circular, or periodic motion types by following an appropriate execution order. They consider the tracked motion path of the object of interest as a sequence and use it as a starting point to perform all operations, resulting in a feature that can be classified into separate classes. Using a single parameter for classifying the motion engenders a faster and relatively more straightforward route to motion identification and elicits the algorithm’s uniqueness. Two algorithms are proposed, namely, Angle Derivative Technique and Determinant Method for classifying the motion into two classes (linear & circular). On the other hand, a different algorithm identifies periodic motion using the principle of correlation on the motion sequences. All the algorithms show an average accuracy of over 95%. It also elicited an average processing time of 15.6 ms and 19.86 ms for Angle Derivative Method and Determinant Method, respectively, and 31.2 ms for periodic motion on Intel(R) Core(TM) i3-5005U CPU @ 2.00 GHz and 8GB RAM. A dataset of camera-captured videos consisting of three motion types is used for testing while the proposed methods are trained on a dataset of motion described by mathematical equations with added 3σ noise levels.


Author(s):  
Zeyu Chen ◽  
Yana Zhang ◽  
Lianyi Zhang ◽  
Cheng Yang

2021 ◽  
Vol 11 (16) ◽  
pp. 7630
Author(s):  
Hai Li ◽  
Hwa Jen Yap ◽  
Selina Khoo

This study recognized the motions and assessed the motion accuracy of a traditional Chinese sport (Baduanjin), using the data from the inertial sensor measurement system (IMU) and sampled-based methods. Fifty-three participants were recruited in two batches to participate in the study. Motion data of participants practicing Baduanjin were captured by IMU. By extracting features from motion data and benchmarking with the teacher’s assessment of motion accuracy, this study verifies the effectiveness of assessment on different classifiers for motion accuracy of Baduanjin. Moreover, based on the extracted features, the effectiveness of Baduanjin motion recognition on different classifiers was verified. The k-Nearest Neighbor (k-NN), as a classifier, has advantages in accuracy (more than 85%) and a short average processing time (0.008 s) during assessment. In terms of recognizing motions, the classifier One-dimensional Convolutional Neural Network (1D-CNN) has the highest accuracy among all verified classifiers (99.74%). The results show, using the extracted features of the motion data captained by IMU, that selecting an appropriate classifier can effectively recognize the motions and, hence, assess the motion accuracy of Baduanjin.


2021 ◽  
Author(s):  
Martin Nowak ◽  
Anthony Beninati ◽  
Nicolas Douard ◽  
Alec Puran ◽  
Charles Barnes ◽  
...  

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