scholarly journals Human Posture Detection Method Based on Wearable Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoou Li ◽  
Zhiyong Zhou ◽  
Jiajia Wu ◽  
Yichao Xiong
Keyword(s):  
Standard Deviation ◽  
Upper Limb ◽  
Plantar Pressure ◽  
Gait Recognition ◽  
Human Motion ◽  
Lower Limbs ◽  
Identification Accuracy ◽  
Support Vector ◽  
Human Posture ◽  
Semg Signals

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

scholarly journals Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5385
Author(s):  
Tianyang Zhong ◽  
Donglin Li ◽  
Jianhui Wang ◽  
Jiacan Xu ◽  
Zida An ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Human Motion ◽  
Identification Accuracy ◽  
Classification Model ◽  
Upper Limb Rehabilitation ◽  
Time Frequency ◽  
Limb Rehabilitation ◽  
Semg Signals

Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on upper-limb motions is lost in the process of feature extraction for sEMG signals, and there exists the fact that only a small variety of rehabilitation movements can be distinguished, and the classification accuracy is easily affected. To solve these dilemmas, first, a multiscale time–frequency information fusion representation method (MTFIFR) is proposed to obtain the time–frequency features of multichannel sEMG signals. Then, this paper designs the multiple feature fusion network (MFFN), which aims at strengthening the ability of feature extraction. Finally, a deep belief network (DBN) was introduced as the classification model of the MFFN to boost the generalization performance for more types of upper-limb movements. In the experiments, 12 kinds of upper-limb rehabilitation actions were recognized utilizing four sEMG sensors. The maximum identification accuracy was 86.10% and the average classification accuracy of the proposed MFFN was 73.49%, indicating that the time–frequency representation approach combined with the MFFN is superior to the traditional machine learning and convolutional neural network.

scholarly journals A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2761 ◽  
Author(s):  
Wenxia Xu ◽  
Jian Huang ◽  
Lei Cheng
Keyword(s):  
Human Locomotion ◽  
Detection Algorithm ◽  
Human Motion ◽  
Lower Limbs ◽  
Support Vector ◽  
Normal Walking ◽  
Coordinated Motion ◽  
Fusion Algorithm ◽  
Robot System ◽  
Walking Aid

Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user’s normal walking state and abnormal walking state for a walking-aid robot system. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user’s upper and lower limbs. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user’s falling mode when he/she is in an abnormal walking state. The experimental results verify the effectiveness of the proposed algorithms.

scholarly journals MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

2021 ◽  
Vol 15 ◽  
Author(s):  
Kecheng Shi ◽  
Rui Huang ◽  
Zhinan Peng ◽  
Fengjun Mu ◽  
Xiao Yang
Keyword(s):  
Upper Limb ◽  
Lower Limb ◽  
Prediction Performance ◽  
Lower Limbs ◽  
Natural Interaction ◽  
Semg Signal ◽  
Good Movement ◽  
Lower Limb Movement ◽  
Feature Visualization ◽  
Semg Signals

The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.

scholarly journals Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 130 ◽  
Author(s):  
Yanxia Deng ◽  
Farong Gao ◽  
Huihui Chen
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Joint Angle ◽  
Pearson Correlation ◽  
Lower Limbs ◽  
Human Gait ◽  
Support Vector ◽  
Joint Movement ◽  
Knee Joint Angle ◽  
Semg Signals

Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.

HUMAN RECOGNITION BASED ON PLANTAR PRESSURE PATTERNS DURING GAIT

2013 ◽  
Vol 13 (02) ◽  
pp. 1350039
Author(s):  
YU-CHIH LIN ◽  
YU-TZU LIN
Keyword(s):  
Image Processing ◽  
Plantar Pressure ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Data Classification ◽  
Support Vector ◽  
Neuron Network ◽  
Recognition Method ◽  
Foot Pressure ◽  
Sensor Cell

Recognizing individuals by their gait is a new biometric methodology, which employs dynamic features derived from tracking gait. Instead of the image processing techniques used in most existing studies, our previous study initialized the work of investigating gait recognition in terms of biomechanics. The experimental results showed that the angles and forces of the lower limb joints were reliable features for recognition of individuals, which can provide us with a considerable amount of information in the field of computer science and thus help in developing a more efficient recognition method, which is also more computationally efficient than current image processing methods. Encouraged by the early results, in this study, we proposed a people recognition method based on plantar pressure patterns, which can be used in a concealed manner. We hoped to prove the feasibility of using foot pressure for individual recognition. Two different plantar pressure parameter measurement schemes are discussed: (1) the characteristic parameters and (2) the pressure values of each sensor cell in each frame. The self-organizing map (SOM) neuron network algorithm was used in both schemes for data classification. In order to improve the recognition rate, a support vector machine (SVM) was used as the data classification algorithm for the all-sensor-values method. High recognition rates were achieved with the second method, i.e., using all the sensor cell values of the foot pressure pattern during walking, regardless of the algorithm used. It is suggested that the foot pressure distribution of gait is a suitable feature for gait recognition. Both SOM and SVM can be feasible classifiers for foot pressure-based features.

Computed tomography angiography and microsurgical flaps for traumatic wounds: What is the added value?

2021 ◽  
pp. 1-9
Author(s):  
Lucas Sousa Macedo ◽  
Renato Polese Rusig ◽  
Gustavo Bersani Silva ◽  
Alvaro Baik Cho ◽  
Teng Hsiang Wei ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Upper Limb ◽  
Computed Tomography Angiography ◽  
Lower Limb ◽  
High Energy ◽  
Lower Limbs ◽  
Added Value ◽  
Vascular Lesions ◽  
Limb Injuries ◽  
Significant Difference

BACKGROUND: Microsurgical flaps are widely used to treat complex traumatic wounds of upper and lower limbs. Few studies have evaluated whether the vascular changes in preoperative computed tomography angiography (CTA) influence the selection of recipient vessel and type of anastomosis and the microsurgical flaps outcomes including complications. OBJECTIVE: The aim of this study was to evaluate if preoperative CTA reduces the occurrence of major complications (revision of the anastomosis, partial or total flap failure, and amputation) of the flaps in upper and lower limb trauma, and to describe and analyze the vascular lesions of the group with CTA and its relationship with complications. METHODS: A retrospective cohort study was undertaken with all 121 consecutive patients submitted to microsurgical flaps for traumatic lower and upper limb, from 2014 to 2020. Patients were divided into two groups: patients with preoperative CTA (CTA+) and patients not submitted to CTA (CTA–). The presence of postoperative complications was assessed and, within CTA+, we also analyzed the number of patent arteries on CTA and described the arterial lesions. RESULTS: Of the 121 flaps evaluated (84 in the lower limb and 37 in the upper limb), 64 patients underwent preoperative CTA. In the CTA+ group, 56% of patients with free flaps for lower limb had complete occlusion of one artery. CTA+ patients had a higher rate of complications (p = 0.031), which may represent a selection bias as the most complex limb injuries and may have CTA indicated more frequently. The highest rate of complications was observed in chronic cases (p = 0.034). There was no statistically significant difference in complications in patients with preoperative vascular injury or the number of patent arteries. CONCLUSIONS: CTA should not be performed routinely, however, CTA may help in surgical planning, especially in complex cases of high-energy and chronic cases, since it provides information on the best recipient artery and the adequate level to perform the microanastomosis, outside the lesion area.

scholarly journals The Design and Simulation of a 16-Sensors Plantar Pressure Insole Layout for Different Applications: From Sports to Clinics, a Pilot Study

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1450
Author(s):  
Alfredo Ciniglio ◽  
Annamaria Guiotto ◽  
Fabiola Spolaor ◽  
Zimi Sawacha
Keyword(s):  
Pressure Distribution ◽  
Plantar Pressure ◽  
Center Of Pressure ◽  
Reaction Force ◽  
Weight Lifting ◽  
Lower Limbs ◽  
Plantar Pressure Distribution ◽  
Drop Landing ◽  
Mean Pressure ◽  
Footwear Design

The quantification of plantar pressure distribution is widely done in the diagnosis of lower limbs deformities, gait analysis, footwear design, and sport applications. To date, a number of pressure insole layouts have been proposed, with different configurations according to their applications. The goal of this study is to assess the validity of a 16-sensors (1.5 × 1.5 cm) pressure insole to detect plantar pressure distribution during different tasks in the clinic and sport domains. The data of 39 healthy adults, acquired with a Pedar-X® system (Novel GmbH, Munich, Germany) during walking, weight lifting, and drop landing, were used to simulate the insole. The sensors were distributed by considering the location of the peak pressure on all trials: 4 on the hindfoot, 3 on the midfoot, and 9 on the forefoot. The following variables were computed with both systems and compared by estimating the Root Mean Square Error (RMSE): Peak/Mean Pressure, Ground Reaction Force (GRF), Center of Pressure (COP), the distance between COP and the origin, the Contact Area. The lowest (0.61%) and highest (82.4%) RMSE values were detected during gait on the medial-lateral COP and the GRF, respectively. This approach could be used for testing different layouts on various applications prior to production.

Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Alexander Knyshov ◽  
Samantha Hoang ◽  
Christiane Weirauch
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Training Image ◽  
Species Level ◽  
Identification Accuracy ◽  
Support Vector ◽  
Test Case ◽  
Learning Approaches ◽  
Lower Accuracy ◽  
Neural Network Classifiers

Abstract Automated insect identification systems have been explored for more than two decades but have only recently started to take advantage of powerful and versatile convolutional neural networks (CNNs). While typical CNN applications still require large training image datasets with hundreds of images per taxon, pretrained CNNs recently have been shown to be highly accurate, while being trained on much smaller datasets. We here evaluate the performance of CNN-based machine learning approaches in identifying three curated species-level dorsal habitus datasets for Miridae, the plant bugs. Miridae are of economic importance, but species-level identifications are challenging and typically rely on information other than dorsal habitus (e.g., host plants, locality, genitalic structures). Each dataset contained 2–6 species and 126–246 images in total, with a mean of only 32 images per species for the most difficult dataset. We find that closely related species of plant bugs can be identified with 80–90% accuracy based on their dorsal habitus alone. The pretrained CNN performed 10–20% better than a taxon expert who had access to the same dorsal habitus images. We find that feature extraction protocols (selection and combination of blocks of CNN layers) impact identification accuracy much more than the classifying mechanism (support vector machine and deep neural network classifiers). While our network has much lower accuracy on photographs of live insects (62%), overall results confirm that a pretrained CNN can be straightforwardly adapted to collection-based images for a new taxonomic group and successfully extract relevant features to classify insect species.

Sign in / Sign up

Export Citation Format

Share Document