The effect of body weight on joint torques in teenagers: Investigation of sit-to-stand movement

Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms

The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.

Assistance force-line of exosuit affects ankle multidimensional motion: a theoretical and experimental study

BACKGROUND

The talocrural joint and the subtalar joint are the two major joints of the ankle-joint complex. The position and direction of the exosuit force line relative to these two joint axes can influence ankle motion. We aimed to understand the effects of different force-lines on ankle multidimensional motion.

METHODS

In this article, three assistance force line schemes for ankle exosuits were proposed: perpendicular to the talocrural joint axis (PT), intersecting with the subtalar joint axis (IS), and parallel to the triceps surae (PTS). A theoretical model was proposed to calculate the exosuit's assistance moment. Seven participants completed four experimental tests of ankle plantarflexion, including three passive motions assisted by the PT, PTS and IS schemes, and one active motion without exosuit assistance (Active).

RESULTS

The simulation results demonstrated that all three exosuits were able to produce significant moments of ankle plantarflexion. Among these, the PT scheme exhibited the highest moments in all dimensions, followed by the PTS and IS schemes. The experimental findings confirmed the effectiveness of all three exosuit schemes in assisting ankle plantarflexion. Additionally, as the assistive force lines approached the subtalar joint, there was a decrease in ankle motion assisted by the exosuits in non-plantarflexion directions, along with a reduction in the average distance of ankle angle curves relative to active ankle motion. Furthermore, the linear correlation coefficients between inversion and plantarflexion, adduction and plantarflexion, and adduction and inversion gradually converged toward active ankle plantarflexion motion.

CONCLUSIONS

Our research indicates that the position of the exosuit force line to the subtalar joint has a significant impact on ankle inversion and adduction. Among all three schemes, the IS, which has the closest distance to the subtalar joint axes, has the greatest kinematic similarity to active ankle plantarflexion and might be a better choice for ankle assistance and rehabilitation.

Estimation of lower limb joint moments based on the inverse dynamics approach: a comparison of machine learning algorithms for rapid estimation

The aim of this study is to estimate the joint moments of the ankle, knee, and hip joints during walking. A sit-to-stand (STS) movement analysis was first performed on 20 participants with different anthropometric characteristics. Then, analysis of the dynamics of the STS motion was used to develop a biomechanical model. Decision tree (DT), linear regression (LR), support vector machine (SVM), random forest (RF), and three deep learning (DL) algorithms and deep neural network (DNN), long-short-term memory (LSTM), and convolutional neural network (CNN) are examined in this work to estimate three joint moments: ankle, knee, and hip. The results of the seven algorithms were evaluated using four statistical benchmarks: MSR, RMSE, correlation coefficient (R), and MAE to find the most accurate one. The results show that the most successful algorithms were LSTM in estimating knee, hip, and ankle joint moments using 19 and 7 inputs. The R value was 0.9990 using 19 inputs and 0.9972 using 7 inputs. The other algorithms have a correlation coefficient (R) success of 0.9902, 0.9770, 0.9884, 0.9577, 0.9786, and 0.9022 for RF, CNN, DT, DNN, SVM, and LR, respectively. The prediction of joint moments plays a crucial role in the design of the biomechanical system with the desired mechanical properties. Especially, the need has arisen to predict joint moments in a shorter time to utilize in real-time active prosthesis/orthosis controllers.

Effects of weight gaining to lower limb joint moments: a gender-specific sit-to-stand analysis

The prevalence of obesity, a worldwide health problem, is increasing. Obesity or overweight has significant effects, especially on lower limb biomechanics. Previous studies have investigated the biomechanical effects of weight gain on the knee and hip joints. These studies have been conducted on different individuals with normal weight and overweight. However, no investigation has been carried out between women and men in terms of weight gain. Females usually gain weight in the gluteal-femoral region, whereas males gain weight in the abdominal region. Due to this difference, it is thought that the effects of weight gain should be examined in a gender-specific manner. In this study, a link-segment model of the lower limb was created. Then the sit-to-stand movement was simulated according to female and male-specific weight gain scenarios. According to these results, weight gain in the abdominal region (men-specific) increases the ankle and knee joint moments more than weight gain in the gluteal-femoral region (women-specific). In obese scenarios for males and females, while the ankle and knee joint moment increases, the hip joint moment decreases. These results would be beneficial for considering biomechanical differences caused by gender-specific weight gain in rehabilitation processes and orthotic and prosthetic designs.

A predictive model to quantify joint torques and support reaction forces when using a smartphone while standing with support

The present study had a dual objective: (1) to present and validate a predictive model of standing posture in the sagittal plane, joint torques and support forces for a smartphone user built from biomechanical principles; (2) propose risk scales for joint torques and reaction forces based on simulations in order to use them into the musculoskeletal disorders prevention. Comparison of the modelled data with experimental measurements (400 tested postures with sample size verification) for calling and texting tasks highlights the model's ability to correctly estimate posture and reaction forces on the ground. The model was able to provide estimates of the range of variation of each parameter for a wide range of environmental conditions as a function of the user body mass index (setting between 12.5 and 50). Joint torques risk scales have been constructed, especially for shoulder and elbow, to characterise the risks incurred by the users. Practitioner summary: The proposed model enables the postures, joint torques and reaction forces to be estimated from subject's body mass index and environmental configuration without resorting to experimentation, which is relevant in industry. This approach allows the proposition of new scales based on joint torques to reinforce the recommendations for MSDs prevention. Abbreviations: BMI: body mass index; LUBA: postural loading on the upper body assessment; MSDs: musculoskeletal disorders; RULA: rapid upper limb assessment; WHO: World Health Organization.

Kinematic analysis of the human body during sit-to-stand in healthy young adults

Sit-to-stand (STS) motion is one of the most important and energy-consuming basic motions in everyday life. Kinematic analysis provides information regarding what strategy or motion pattern is used by the healthy people, and through which, we can understand and obtain the law of the STS motion. The objective of this article is to study the law of STS motion through the experiment to determine a suitable description of STS motion in healthy adults, so as to provide a starting point and bases for future design and control of STS assistive devices.Thirty healthy adult subjects participated in this study and carried out STS motion experiment of standing up naturally. The STS motions were recorded using a high-definition camera. The experimentally collected kinematic data and a link segment model of the human body were used to obtain the coordinates of joints and to calculate the coordinates, velocity, and momentum of center of gravity; the postures of human body during STS are also obtained. The relationship between human body parameters and motion parameters is analyzed by using Pearson correlation method.The STS motion is divided into 4 phases; the phases are differentiated in terms of STS motion characteristics and postures, and momentum of center of gravity of human body. The main factors determining the differences in STS motion among individuals are horizontal distance between hip joint and ankle joint, lower leg length, thigh length, and the length of the transition period. The horizontal distance between hip joint and ankle joint is positively correlated with the duration from motion begin to trunk stops flexing forward (P = .021 < .05), but not so with the duration from motion begin to the end of phase 2 (P = .15 > .05).The results suggest that when designing the sit-to-stand assistive devices, one should pay attention to the whole-body posture control in STS motion, such as the posture guidance of trunk and lower leg, and should carry out specific training according to different STS phases. Sit-to-stand assistive devices should provide the same horizontal distance between hip joint and ankle joint for different individuals during the STS motion. Transition period should be properly controlled, and the degree of freedom of the lower leg should not be limited.