HDL-PSR: Modelling Spatio-Temporal Features Using Hybrid Deep Learning Approach for Post-Stroke Rehabilitation

Bijalwan, Vishwanath; Semwal, Vijay Bhaskar; Singh, Ghanapriya; Mandal, Tapan Kumar

doi:10.1007/s11063-022-10744-6

Cited by 50 publications

(25 citation statements)

References 48 publications

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“…On the other hand, while this study focuses on kinematic data captured by an inertial sensor, these instruments can be used as a complement to other devices, such as RGB-D cameras to achieve higher accuracy [ 37 ], optimizing their advantages [ 38 ]. In addition, data from inertial sensors can be used to develop a hybrid deep learning (HDL) system to facilitate the rehabilitation process [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Differences in Tridimensional Shoulder Kinematics between Asymptomatic Subjects and Subjects Suffering from Rotator Cuff Tears by Means of Inertial Sensors: A Cross-Sectional Study

Roldán-Jiménez

Cuadros-Romero

Bennett

et al. 2023

Sensors

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Background: The aim of this study was to analyze differences in three-dimensional shoulder kinematics between asymptomatic subjects and patients who were diagnosed with rotator cuff tears. Methods: This cross-sectional study recruited 13 symptomatic subjects and 14 asymptomatic subjects. Data were obtained from three inertial sensors placed on the humerus, scapula and sternum. Kinematic data from the glenohumeral, scapulothoracic and thoracohumeral joints were also calculated. The participants performed shoulder abductions and flexions. The principal angles of movements and resultant vectors in each axis were studied. Results: The glenohumeral joint showed differences in abduction (p = 0.001) and flexion (p = 0.000), while differences in the scapulothoracic joint were only significant during flexion (p = 0.001). The asymptomatic group showed higher velocity values in all sensors for both movements, with the differences being significant (p < 0.007). Acceleration differences were found in the scapula during abduction (p = 0.001) and flexion (p = 0.014), as well as in the sternum only during shoulder abduction (p = 0.022). Conclusion: The results showed kinematic differences between the patients and asymptomatic subjects in terms of the mobility, velocity and acceleration variables, with lower values for the patients.

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Section: Discussionmentioning

confidence: 99%

Differences in Tridimensional Shoulder Kinematics between Asymptomatic Subjects and Subjects Suffering from Rotator Cuff Tears by Means of Inertial Sensors: A Cross-Sectional Study

Roldán-Jiménez

Cuadros-Romero

Bennett

et al. 2023

Sensors

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“…In addition to depth and RGB monitoring systems, using a wearable sensor is another alternative to detect and analyze the human posture; among them, methods such as [62][63][64][65][66][67][68][69] can be mentioned. Although the accuracy of these methods is very promising, they need the voluntary cooperation of the subject, and they are prone to be forgotten.…”

Section: Discussionmentioning

confidence: 99%

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

Rasoulidanesh

Payandeh

2022

Journal of Sensors

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Inspired by the notion of swarm robotics, sensing, and minimalism, in this paper, we study and analyze how a collection of only 1D depth scans can be used as a part of the minimum feature for human body detection and its segmentation in a point cloud. In relation to the traditional approaches which require a complete point cloud model representation for skeleton model reconstruction, our proposed approach offers a lower computation and power consumption, especially in sensor and robotic networks. Our main objective is to investigate if the reduced number of training data through a collection of 1D scans of a subject is related to the rate of recognition and if it can be used to accurately detect the human body and its posture. The method takes advantage of the frequency components of the depth images (here, we refer to it as a 1D scan). To coordinate a collection of these 1D scans obtained through a sensor network, we also proposed a sensor scheduling framework. The framework is evaluated using two stationary depth sensors and a mobile depth sensor. The performance of our method was analyzed through movements and posture details of a subject having two relative orientations with respect to the sensors with two classes of postures, namely, walking and standing. The novelty of the paper can be summarized in 3 main points. Firstly, unlike deep learning methods, our approach would require a smaller dataset for training. Secondly, our case studies show that the method uses very limited training dataset and still can detect the unseen situation and reasonably estimate the orientation and detail of the posture. Finally, we propose an online scheduler to improve the energy efficiency of the network sensor and minimize the number of sensors required for surveillance monitoring by employing a mobile sensor to recover the occluded views of the stationary sensors. We showed that with the training data captured on 1 m from the camera, the algorithm can detect the detailed posture of the subject from 1, 2, 3, and 4 meters away from the sensor during the walking and standing with average accuracy of 93% and for different orientation with respect to the sensor by 71% accuracy.

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“…For example, when the learning rate is too large, the parameters fluctuate near the minimum and cannot approach the optimal solution. By contrast, the model iteration number will increase when the learning rate is too low, and calculation surges and over-fitting might occur [29]. As in Fig.…”

Section: B Comparison Of Different Video Segmentation and Fusion Methodsmentioning

confidence: 97%

The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

Liang

2022

IEEE Access

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The purpose is to solve the problems of solid subjectivity in the traditional manual analysis of volleyball game videos and the traditional Human Behavior Recognition (HBR) algorithm, such as excessive calculation, high hardware conditions, and poor long-stream video modeling ability. Firstly, this paper expounds on the relevant theories. Secondly, a fusion Convolutional Neural Network (CNN)-based HBR model is implemented by combining two-stream CNN (TSCNN), Three-Dimensional (3D) CNN, and Long Short-Term Memory (LSTM) network. Notably, the LSTM has an excellent long-term Dynamic Information Extraction (DIE) ability. Finally, the public dataset is selected to verify the model's volleyball-game-videooriented HBR performance. The experimental results corroborate that: (1) The parameters of the proposed fusion-CNN-based HBR model are determined as follows: the number of video segments is three, the average method is used for feature fusion, and then the HBR accuracy is the highest when the fusion ratio of spatial feature map and temporal feature map is 4:6, and the learning rate is 0.0014. (2) The average HBR accuracy of the proposed fusion-CNN-based HBR model on three different datasets is 4%, 2.7%, and 3% higher than that of other popular networks, respectively. The improvement effect of the model is remarkable, and it is suitable for the study of Human Behavior Analysis (HBA) in volleyball match videos. Finally, the proposed HBR model can provide more accurate results for volleyball video-based HBR, which is significant in promoting the rapid development of volleyball sports.

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HDL-PSR: Modelling Spatio-Temporal Features Using Hybrid Deep Learning Approach for Post-Stroke Rehabilitation

Cited by 50 publications

References 48 publications

Differences in Tridimensional Shoulder Kinematics between Asymptomatic Subjects and Subjects Suffering from Rotator Cuff Tears by Means of Inertial Sensors: A Cross-Sectional Study

Differences in Tridimensional Shoulder Kinematics between Asymptomatic Subjects and Subjects Suffering from Rotator Cuff Tears by Means of Inertial Sensors: A Cross-Sectional Study

On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network

The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

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