Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation.
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ABSTRACT:
SUBMITTER: Stanev D
PROVIDER: S-EPMC7961635 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time.
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Project description:BackgroundStationary cycling is commonly used for postoperative rehabilitation of physical disabilities; however, few studies have focused on the three-dimensional (3D) kinematics of rehabilitation. This study aimed to elucidate the three-dimensional lower limb kinematics of people with healthy musculoskeletal function and the effect of sex and age on kinematics using a controlled bicycle configuration.MethodsThirty-one healthy adults participated in the study. The position of the stationary cycle was standardized using the LeMond method by setting the saddle height to 85.5% of the participant's inseam. The participants maintained a pedaling rate of 10-12 km/h, and the average value of three successive cycles of the right leg was used for analysis. The pelvis, hip, knee, and ankle joint motions during cycling were evaluated in the sagittal, coronal, and transverse planes. Kinematic data were normalized to 0-100% of the cycling cycle. The Kolmogorov-Smirnov test, Mann-Whitney U test, Kruskal-Wallis test, and k-fold cross-validation were used to analyze the data.ResultsIn the sagittal plane, the cycling ranges of motion (ROMs) were 1.6° (pelvis), 43.9° (hip), 75.2° (knee), and 26.9° (ankle). The coronal plane movement was observed in all joints, and the specific ROMs were 6.6° (knee) and 5.8° (ankle). There was significant internal and external rotation of the hip (ROM: 11.6°), knee (ROM: 6.6°), and ankle (ROM: 10.3°) during cycling. There was no difference in kinematic data of the pelvis, hip, knee, and ankle between the sexes (p = 0.12 to 0.95) and between different age groups (p = 0.11 to 0.96) in all anatomical planes.ConclusionsThe kinematic results support the view that cycling is highly beneficial for comprehensive musculoskeletal rehabilitation. These results might help clinicians set a target of recovery ROM based on healthy and non-elite individuals and issue suitable guidelines to patients.
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Project description:BackgroundArtificial intelligence is being used for rehabilitation, including monitoring exercise compliance through sensor technology. AI classification of shoulder exercise wearing an IMU sensor has only been reported in normal (i.e. painless) subjects. To prove the feasibility of monitoring exercise compliance, we aimed to classify 11 types of shoulder rehabilitation exercises using an AI (artificial intelligence) algorithm in patients with shoulder pain. We had the patients wear an IMU-based sensor, collected data during exercise, and determined the accuracy of exercise classification.MethodsData were collected from 58 patients (27 males, 31 females, age range 37-82 years) diagnosed with shoulder diseases such as adhesive capsulitis and rotator cuff disease. 11 types of shoulder pain rehabilitation exercise programs were developed and repeated each exercise ten times per session while wearing an IMU sensor. The study applied the Rectified Linear Unit (ReLU) and the SoftMax as the activation function for hidden layers, the output layer.ResultsThe acquired data was used to train a DNN model using the multilayer perceptron algorithm. The trained model was used to classify 11 types of shoulder pain rehabilitation exercises. The training accuracy was 0.975 and the test accuracy was 0.925.ConclusionThe study demonstrates that IMU sensor data can effectively classify shoulder pain rehabilitation exercises, providing more appropriate feedback for patients. The model can be utilized to establish a system for remotely monitoring patients' exercise performance. The use of deep learning in patient monitoring and rehabilitation has significant potential to bring innovative changes to healthcare service delivery.
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Project description:PurposeTo validate the feasibility of real time kinematography with four-dimensional (4D) dynamic functional wrist joint imaging using dual source CT.Materials and methodsTwo healthy volunteers performed radioulnar deviation and pronation- supination wrist motions for 10 s and 4 s per cycle in a dual source CT scanner. Scan and reconstruction protocols were set to optimize temporal resolution. Cine images of the reconstructed carpal bone of the moving wrist were recorded. The quality of the images and radiation dosage were evaluated.ResultsThe 4D cine images obtained during 4 s and 10 s of radioulnar motion showed a smooth stream of movement with good quality and little noise or artifact. Images from the pronation-supination motion showed noise with a masked surface contour. The temporal resolution was optimized at 0.28 s.ConclusionUsing dual source CT, 4D cine images of in vivo kinematics of wrist joint movement were obtained and found to have a shorter scan time, improved temporal resolution and lower radiation dosages compared with those previously reported.
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Project description:BackgroundInertial measurements (IMUs) facilitate the measurement of human motion outside the motion laboratory. A commonly used open-source software for musculoskeletal simulation and analysis of human motion, OpenSim, includes a tool to enable kinematics analysis of IMU data. However, it only enables offline analysis, i.e., analysis after the data has been collected. Extending OpenSim's functionality to allow real-time kinematics analysis would allow real-time feedback for the subject during the measurement session and has uses in e.g., rehabilitation, robotics, and ergonomics.MethodsWe developed an open-source software library for real-time inverse kinematics (IK) analysis of IMU data using OpenSim. The software library reads data from IMUs and uses multithreading for concurrent calculation of IK. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. We published the code under an open-source license on GitHub.ResultsA standard desktop computer calculated full-body inverse kinematics from treadmill walking at 1.5 m/s with data from 12 IMUs in real-time with a mean delay below 55 ms and reached a throughput of more than 90 samples per second. A laptop computer had similar delays and reached a throughput above 60 samples per second with treadmill walking. Minimal walking kinematics, motion of lower extremities and torso, were calculated from treadmill walking data in real-time with a throughput of 130 samples per second on the laptop and 180 samples per second on the desktop computer, with approximately half the delay of full-body kinematics.ConclusionsThe software library enabled real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models. The performance results show that subject-specific full-body motion analysis is feasible in real-time, while a laptop computer and IMUs allowed the use of the method outside the motion laboratory.
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