Estimation of the dynamic spinal forces using a recurrent fuzzy neural network

Development of a basic evaluation model for manual therapy learning in rehabilitation students based on the Delphi method

OBJECTIVE

Manual therapy is a crucial component in rehabilitation education, yet there is a lack of models for evaluating learning in this area. This study aims to develop a foundational evaluation model for manual therapy learning among rehabilitation students, based on the Delphi method, and to analyze the theoretical basis and practical significance of this model.

METHODS

An initial framework for evaluating the fundamentals of manual therapy learning was constructed through a literature review and theoretical analysis. Using the Delphi method, consultations were conducted with young experts in the field of rehabilitation from January 2024 to March 2024. Fifteen experts completed three rounds of consultation. Each round involved analysis using Dview software, refining and adjusting indicators based on expert opinions, and finally summarizing all retained indicators using Mindmaster.

RESULTS

The effective response rates for the three rounds of questionnaires were 88%, 100%, and 100%, respectively. Expert familiarity scores were 0.91, 0.95, and 0.95; coefficient of judgment were 0.92, 0.93, and 0.93; authority coefficients were 0.92, 0.94, and 0.94, respectively. Based on three rounds of consultation, the model established includes 3 primary indicators, 10 secondary indicators, 17 tertiary indicators, and 9 quaternary indicators. A total of 24 statistical indicators were finalized, with 8 under the Cognitive Abilities category, 10 under the Practical Skills category, and 6 under the Emotional Competence category.

CONCLUSION

This study has developed an evaluation model for manual therapy learning among rehabilitation students, based on the Delphi method. The model includes multi-level evaluation indicators covering the key dimensions of Cognitive Abilities, Practical Skills, and Emotional Competence. These indicators provide a preliminary evaluation framework for manual therapy education and a theoretical basis for future research.

Predicting Cervical Spine Compression and Shear in Helicopter Helmeted Conditions Using Artificial Neural Networks

OCCUPATIONAL APPLICATIONSMilitary helicopter pilots around the globe are at high risk of neck pain related to their use of helmet-mounted night vision goggles. Unfortunately, it is difficult to design alternative helmet configurations that reduce the biomechanical exposures on the cervical spine during flight because the time and resource costs associated with assessing these exposures in vivo are prohibitive. Instead, we developed artificial neural networks (ANNs) to predict cervical spine compression and shear given head-trunk kinematics and joint moments in the lower neck, data readily available from digital human models. The ANNs detected differences in cervical spine compression and anteroposterior shear between helmet configuration conditions during flight-relevant head movement, consistent with results from a detailed model based on in vivo electromyographic data. These ANNs may be useful in helping to prevent neck pain related to military helicopter flight by facilitating virtual biomechanical assessment of helmet configurations upstream in the design process.

A machine-learning method for classifying and analyzing foot placement: Application to manual material handling

Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabilitation or ergonomics. The method is based on machine learning using a weighted k-nearest neighbors algorithm. The learning phase is performed by an observer who classifies a set of trials. The algorithm then automatically reproduces this classification on subsequent sets. The method also provides detailed analysis of foot placement strategy, such as estimating the average foot placements for each class or visualizing the variability of strategies. An example of applying the method to a manual material handling task demonstrates its usefulness. During the lifting phase, the foot placements were classified into four groups: front, contralateral foot behind, ipsilateral foot behind, and parallel. The accuracy of the classification, assessed with a holdout method, is about 97%. In this example, the classification method makes it possible to observe and analyze the handler's foot placement strategies with regards to the performed task.

Using a deep learning network to recognise low back pain in static standing

Low back pain (LBP) remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognise LBP patients from healthy population using balance performance data are rarely seen. In this study, human balance and body sway performance during standing trials were utilised to recognise chronic LBP populations using deep neural networks. To be specific, 44 chronic LBP and healthy individuals performed static standing tasks, while their spine kinematics and centre of pressure were recorded. A deep learning network with long short-term memory units was used for training, prediction and implementation. The performance of the model was evaluated by: (a) overall accuracy, (b) precision, (c) recall, (d) F1 measure, (e) receiver-operating characteristic and (f) area under the curve. Results indicated that deep neural networks could recognise LBP populations with precision up to 97.2% and recall up to 97.2%. Meanwhile, the results showed that the model with the C7 sensor output performed the best. Practitioner summary: Low back pain (LBP) remains the most common musculoskeletal disorder. In this study, we investigated the feasibility of applying artificial intelligent deep neural network in detecting LBP population from healthy controls with their kinematics data. Results showed a deep learning network can solve the above classification problem with both promising precision and recall performance.

An intelligent algorithm for identification of optimum mix of demographic features for trust in medical centers in Iran

Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for treatment outcomes. The presented study identifies optimum mixture of patient demographic features with respect to trust in three large and busy medical centers in Tehran, Iran. The presented algorithm is composed of adaptive neuro-fuzzy inference system and statistical methods. It is used to deal with data and environmental uncertainty. The required data are collected from three large hospitals using standard questionnaires. The reliability and validity of the collected data is evaluated using Cronbach's Alpha, factor analysis and statistical tests. The results of this study indicate that middle age patients with low level of education and moderate illness severity and young patients with high level of education, moderate illness severity and moderate to weak financial status have the highest trust to the considered medical centers. To the best of our knowledge this the first study that investigates patient demographic features using adaptive neuro-fuzzy inference system in healthcare sector. Second, it is a practical approach for continuous improvement of trust features in medical centers. Third, it deals with the existing uncertainty through the unique neuro-fuzzy approach.

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

Simulation of movement in three-dimensional musculoskeletal human lumbar spine using directional encoding-based neurocontrollers

Despite development of accurate musculoskeletal models for human lumbar spine, the methods for prediction of muscle activity patterns in movements lack proper association with corresponding sensorimotor integrations. This paper uses the directional information of the Jacobian of the musculoskeletal system to orchestrate adaptive critic-based fuzzy neural controller modules for controlling a complex nonlinear redundant musculoskeletal system. The proposed controller is used to control a 3D 3-degree of freedom (DOF) musculoskeletal model of trunk, actuated by 18 muscles. The controller is capable of learning to control from sensory information, without relying on pre-assumed model parameters. Simulation results show satisfactory tracking of movements and the simulated muscle activation patterns conform to previous EMG experiments and optimization studies. The proposed controller can be used as a computationally inexpensive muscle activity generator to distinguish between neural and mechanical contributions to movement and for study of sensory versus motor origins of motor function and dysfunction in human spine.

Hybrid soft computing systems for electromyographic signals analysis: a review

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

Towards the application of one-dimensional sonomyography for powered upper-limb prosthetic control using machine learning models

BACKGROUND

The inherent properties of surface electromyography limit its potential for multi-degrees of freedom control. Our previous studies demonstrated that wrist angle could be predicted by muscle thickness measured from B-mode ultrasound, and hence, it could be an alternative signal for prosthetic control. However, an ultrasound imaging machine is too bulky and expensive.

OBJECTIVE

We aim to utilize a portable A-mode ultrasound system to examine the feasibility of using one-dimensional sonomyography (i.e. muscle thickness signals detected by A-mode ultrasound) to predict wrist angle with three different machine learning models - (1) support vector machine (SVM), (2) radial basis function artificial neural network (RBF ANN), and (3) back-propagation artificial neural network (BP ANN).

STUDY DESIGN

Feasibility study using nine healthy subjects.

METHODS

Each subject performed wrist extension guided at 15, 22.5, and 30 cycles/minute, respectively. Data obtained from 22.5 cycles/minute trials was used to train the models and the remaining trials were used for cross-validation. Prediction accuracy was quantified by relative root mean square error (RMSE) and correlation coefficients (CC).

RESULTS

Excellent prediction was noted using SVM (RMSE = 13%, CC = 0.975), which outperformed the other methods.

CONCLUSION

It appears that one-dimensional sonomyography could be an alternative signal for prosthetic control. Clinical relevance Surface electromyography has inherent limitations that prohibit its full functional use for prosthetic control. Research that explores alternative signals to improve prosthetic control (such as the one-dimensional sonomyography signals evaluated in this study) may revolutionize powered prosthesis design and ultimately benefit amputee patients.

A review of human factors challenges of complex adaptive systems: discovering and understanding chaos in human performance

OBJECTIVE

In this paper, the author explores a need for a greater understanding of the true nature of human-system interactions from the perspective of the theory of complex adaptive systems, including the essence of complexity, emergent properties of system behavior, nonlinear systems dynamics, and deterministic chaos.

BACKGROUND

Human performance, more often than not, constitutes complex adaptive phenomena with emergent properties that exhibit nonlinear dynamical (chaotic) behaviors.

METHOD

The complexity challenges in the design and management of contemporary work systems, including service systems, are explored. Examples of selected applications of the concepts of nonlinear dynamics to the study of human physical performance are provided.

RESULTS

Understanding and applications of the concepts of theory of complex adaptive and dynamical systems should significantly improve the effectiveness of human-centered design efforts of a large system of systems.

CONCLUSION

Performance of many contemporary work systems and environments may be sensitive to the initial conditions and may exhibit dynamic nonlinear properties and chaotic system behaviors. Human-centered design of emergent human-system interactions requires application of the theories of nonlinear dynamics and complex adaptive system.

APPLICATION

The success of future human-systems integration efforts requires the fusion of paradigms, knowledge, design principles, and methodologies of human factors and ergonomics with those of the science of complex adaptive systems as well as modern systems engineering.

Utilization of a Hybrid Neuro-Fuzzy Engine to Predict Trunk Muscle Activity for Sagittal Lifting

The ability to assess the loads on the spine in industry using biologically-assisted models has been limited by the current capability to obtain accurate muscle activities that could be entered into an EMG-assisted model. One crucial aspect of EMG-assisted models is the capability to capture the antagonistic coactivity in dynamic lifting conditions. However, limitations of electromyography equipment make it difficult to assess the muscle activity in industry. The overall project developed a complex engine using fuzzy average with fuzzy cluster distribution techniques in combination with neural network structure. The objective of the current study was compare the predicted spine loads for the actual and predicted muscle activities during sagittal lifting conditions. The model fidelity of the EMG-assisted spine load model was actually improved with the predicted EMG as compared to the actual EMG with improved r-square and average absolute error values. Furthermore, the three-dimensional spine loads were almost identical for the predicted EMG as compared to the actual EMG (within 35 N in each plane). The compression forces predicted within 1% while shear forces were within 11%. Overall, the new neuro-fuzzy engine provides an accurate estimation of the coactivity pattern during lifting that can now be applied in industrial settings where traditional muscle activity assessment methods are subjected to noise or are difficult to administer.