Human Body Posture Research Articles

Simulation of gymnastics performance based on MEMS sensor

The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.

Variability of Body Build and Physiological Spinal Curvatures of Young People in an Accelerated Longitudinal Study.

The human body is characterized by the variability of the characteristics of body build, which is expressed in the instability of spinal curvatures, which change during ontogeny. This phenomenon leads to a phylogenetic evolution of the human body build and posture. The aim of the study was to assess the dynamics of the variability of traits and indices of body build and posture and their mutual relations. It was assumed that over the 10-year observation period, a significant variability and relationships will be observed between the characteristics of body build and posture in young adults. Between 2006 and 2016, 2154 women and men aged 19.6 ± 0.8 were examined (first-year students at the University of Economics in Katowice, Poland). Measurements of basic anthropometric measurements and angular values of lumbar lordosis and thoracic kyphosis were performed. The collected data were analysed statistically, and the intergroup and intragroup differences were evaluated (ANOVA with repeated measures). The dynamics of variability (by building time series) and absolute and single-base increments were determined. The body build of young men and women in the period of ontogenetic stabilization in the study group has changed statistically significantly over the decade. A prediction of the increases in body weight and hip circumference was recorded over a period of 10 years, especially in men. The characteristics of body posture have also undergone a significant progressive change. In women, thoracic kyphosis increased by 96.15%, whereas in men, lumbar lordosis decreased by 52.65%. Significant sexual differentiation was shown for such characteristics as body height, waist circumference and the angle of lumbar lordosis. The relationships between the characteristics of body build and posture were verified. There was a moderate relationship between the hip circumference and lumbar lordosis in women and a weak relationship between body height and the angle of thoracic kyphosis in men.

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.

Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data.

With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solve this, we propose a novel three-staged deep adversarial neural network architecture capable of denoising and refining real-world depth sensor input for full human body posture reconstruction. The proposed network has achieved Earth Mover and Chamfer distances of and on synthetic datasets, respectively, which indicates on-par experimental results with other approaches, in addition to the ability of reconstructing from maskless real world depth frames. Additional visual inspection to the reconstructed pointclouds has shown that the suggested approach manages to deal with the majority of the real world depth sensor noise, with the exception of large deformities to the depth field.

Intelligent Sports Training System Based on Artificial Intelligence and Big Data

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.

The Impact of the Human Body Position Changes During Wheelchair Propelling on Motion Resistance Force: A Preliminary Study.

The aim of this research was to analyze the impact of the human body position changes caused by propelling a wheelchair with the pushrim propulsion on the value of motion resistance force. The discussed research works are in progress; therefore, the presented results should be treated as preliminary. The research was carried out in the group of six volunteers propelling a wheelchair of which frame was inclined, in respect to the horizontal plane, under the angle of 0 deg, 7 deg, and 14 deg. The area of the position variability of the human body center of gravity (COG) and the coefficients of wheelchair rolling resistance have been determined. Based on the measurements conducted, rolling resistance force FT and motion resistance force FR have been defined for three values of frame inclination angle. The determined force of rolling resistance Ft depended on the location of the COG of the human body and the value of the coefficients of rolling resistance of the front and rear wheels of a wheelchair. This force was a component of the resistance to motion FR, which also took into account the influence of gravity resulting from the inclination of the wheelchair on an inclined plane. For the tested inclination angles relative to the horizontal plane, the rolling resistance force ranged from 9.82 N to 22.81 N. Analyzing the variability of the rolling resistance force FT, it was found that for the final phase of the driving motion, it increased by 36% for the inclination angle of 0 deg and 43% for the inclination angle of 7 deg. Its increase was 48% for the inclination angle of 14 deg in relation to the human body position for the beginning of the driving motion. In the case of measuring the value of the resistance to motion FR, it was observed that, depending on the angle of the incline of the wheelchair, it ranged from 14.69 N to 256.33 N. The measurements conducted enabled the derivation of an analytical model for determining rolling resistance force depending on the position of the human body COG and the wheelchair inclination angle. The conducted research demonstrated the impact of the COG position on the changes of motion resistance force, thus expanding the state of knowledge, introducing a new parameter which, like a surface type and wheel type, affects motion resistances.

The influence of human interaction on the vibration of hand-held human-machine systems – The effect of body posture, feed force, and gripping forces on the vibration of hammer drills

In hand-held human machine systems the biodynamic response of the human hand arm system influences the overall dynamic behavior. The biodynamic response of the hand arm system is affected by several influencing factors. These influencing factors include the feed force, body posture, gripping force, and anthropometric properties. In present studies the importance of the gripping force on the biodynamic response is highlighted. In the state of the art; however, the magnitude of gripping forces as well as the influence of the human factors body posture and feed force on hammer drills for a larger group of professional users is unclear. To analyze the influence that these human factors have on hammer drill vibrations, a study with 15 professional power tool test personnel has been conducted. Characteristic gripping force values have been measured (Median: 84 N–156 N at the main handle, 10 N–30 N on the auxiliary handle) for different feed forces (100 N, 150 N, 200 N) and body postures. Then the influence of body posture, feed force, and gripping force on the hammer drill vibration is determined. A significant influence of the body posture (ANOVA: p = .003, r = .367) on the RMS acceleration of the main handle was observed. Furthermore, the feed force has a significant influence on the vibration of the hammer-drill for some groups. The data also demonstrates that for increasing gripping forces, the RMS of the acceleration and frequency weighted acceleration ahv on the main and auxiliary handle decrease. The findings of this study represent a starting point for the development of adjustable hand-arm models for the reproducible vibration assessment of power tools.

Effect of human body position on gamma radiation dose rate from granite stones.

The use of granite stones as building materials in homes or offices can result in the residents' long-term whole-body exposure to gamma radiation. Although the whole-body annual dose has been investigated in the literature, it is obvious that different human organs receive different equivalent dose due to different position respect to the walls and floor covered by granite stones. In this paper, the effect of distance from the walls and floor of a room on the equivalent dose is investigated by using MCNPX code. An "ORNL" phantom is simulated in three situations; standing (P1), sleeping one meter above the floor (P2) and sleeping on the floor (P3) and the equivalent dose in different organs is calculated. Excess lifetime cancer risk (ELCR) is calculated in the whole of the body for these three positions. By the results, the value of ELCR in the third position is more than the average world value (2.9 × 10-4). The results show the importance of considering body position in dose determination, especially in some organs such as the brain and eyes which are close to the granite stones in certain positions such as sleeping.

Intelligent Human Body Posture Recognition and Its Comparative Study

An algorithm of activity pattern recognition based on fuzzy mathematics used the excellent characteristics and the classification ability of multiple classifier for human pasture identification is analyzed and compared. Time-domain analysis is used as the only method through large amounts of data, and corresponding BP neural network and Support vector machine (SVM) was established were compared with the algorithm of fuzzy mathematics, and some method were evaluated at last. The results showed that the algorithm based on fuzzy mathematics had a good performance and value.

A Specific Algorithm Based on Motion Direction Prediction

In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.

Role of Human Body Posture Recognition Method Based on Wireless Network Kinect in Line Dance Aerobics and Gymnastics Training

With the rapid development of the information society, human body gesture recognition has become an important technology for human‐computer interaction. This paper combines Kinect’s human bone monitoring technology with auxiliary gymnastics training. The gymnastics and dance training can correct students’ wrong movements in time through feedback and improve the training efficiency, so as to meet the needs of nature and harmony of human‐machine interaction. In this paper, based on the wireless network Kinect, the human body posture recognition method and tracking technology are studied, and the joint point angle representation method based on the fixed axis is proposed, and the posture recognition method based on the joint point angle is improved, which can accurately recognize the human body posture. Aiming at the situation that the human joint points are occluded, the human joint point repair algorithm is improved. The algorithm is based on the proportion of human bone nodes and the characteristics of human motion, and based on geometric principles, it repairs the occluded points. The feasibility of the original joint point data, angle feature, and distance feature in expressing human behavior is analyzed through experiments, a standard gymnastics movement database is established, and new gymnastics movements can be entered at any time. A gymnastics auxiliary training system is designed, which can analyze and evaluate the exercises of the trainer from the joint point coordinates and the angle formed by the joints and provide the trainer with intuitive error correction prompts. The human body posture recognition method studied in this paper can accurately give the difference between the trainer’s movement and the standard movement, and the trainer can adjust the movement posture according to the prompts, improve the level of gymnastics, and achieve the purpose of auxiliary training. Experiments show that the algorithm model has an accuracy rate of 95.7% for human body posture recognition, and it plays a huge role in line dance aerobics and gymnastics training.

Discrete pointvv 3D reconstruction algorithm based human pose estimation

The traditional method of human pose estimation lacks depth label in depth image, which leads to the low generalization ability of model due to single pose. A novel method of human pose estimation based on discrete point 3D reconstruction algorithm is proposed. Several cameras are placed around each other, and the internal parameters of the camera are calibrated by the classical calibration method , and the external parameters of the camera group are obtained. According to the two-dimensional skeleton, the human body region model is established, and the contour is matched with the human body model, so that the contour region is segmented, and then the three-dimensional human body model is constructed. According to the principle of discrete point 3D reconstruction algorithm, principal component analysis is used to transform the original data, eliminate the redundant correlation between the original human body posture data, establish the human body posture model, obtain 10 individual voxel sets, and estimate the 3D information of each point in the voxel set. In the process of feature extraction, two-dimensional attitude prediction, two-dimensional fusion layer and final conversion, the human body attitude estimation is realized. Experimental results show that this method can reduce the influence of image shadow on image recognition, and the estimation results are more accurate.

A Pose Estimation-Based Fall Detection Methodology Using Artificial Intelligence Edge Computing

As the population worldwide continues to age and the percentage of elderly people continues to increase, falls have been become the second leading cause of death from accidental or unintentional injuries. Although many imaging sensing devices have been used to detect falls for elderly people, most involve using the Internet to transfer images taken by a camera to a large back-end server, which performs the necessary calculations; however, algorithm limitations and computational complexity may cause bottlenecks in image outflow, and the image transfer can result in privacy problems. To address these problems, in this paper, an artificial intelligence (AI) fall detection method is proposed that operates using an edge computing architecture, called the pose estimation-based fall detection methodology (PEFDM), which is based on a human body posture recognition technique. The proposed PEFDM can effectively reduce the computational load, runs smoothly on mainstream edge computing systems and possesses artificial intelligence computing capabilities. By using edge computing, the privacy and upload bandwidth issues caused by image outflow can be eliminated. Experiments with real humans show that the PEFDM can detect falls for elderly people with a recognition accuracy of up to 98.1%.

ВЗАИМОСВЯЗЬ СТАБИЛОГРАФИЧЕСКИХ ХАРАКТЕРИСТИК ПОЗЫ ТЕЛА ЧЕЛОВЕКА С УРОВНЕМ ТРЕВОЖНОСТИ И САМОРЕГУЛЯЦИИ

The article presents the results of a study of the relationship between the level of anxiety and general self-regulation with the stabilographic characteristics of the human body posture before and after using the stabilographic balance training with visual biological feedback aimed at the development of voluntary regulation of movements. It is shown that after the exercise, a decrease in all stabilographic characteristics was observed; this indicates an increase in posture stability, and activation of voluntary regulation mechanisms reduces the influence of personal anxiety on the sustainability of maintaining the body posture. The interrelation of stabilographic characteristics of oscillations of the projection of the center of gravity of the human body onto the platform with the level of anxiety and self-regulation in conditions of involuntary and voluntary regulation of the body posture is shown theoretically and experimentally. The study involved 25 female students of the 2nd year. The effectiveness of posture maintenance was assessed using the automated complex «Stabilan-01». The stabilographic method is based on recording the indicators of fluctuations in the projection of the center of gravity of the subject on the plane of the platform, in the center of which he stands. The study of the characteristics of the projection onto the platform of the center of gravity was carried out using the Romberg test, which consisted of two tests – with open and closed eyes. Romberg’s test was performed twice: before and after using the training stabilographic technique. As a means of teaching posture control, a computer stabilographic game «Balls» was used, during which motor actions are performed that provide an arbitrary movement of the center of gravity of the subject’s body, thereby training the function of balance. Personal anxiety (PA) was assessed using the Spielberger-Khanin method. The level of general self-regulation was determined using the V. I. Morosanova. In the initial state, significant differences were found between groups of students differing in the level of radiation therapy in terms of such indicators as frontal spread with closed eyes, sagittal spread with open eyes, length of the trajectory of spread with open and closed eyes, and projection area with open eyes, which were significantly higher in a group of highly anxious subjects. The dependence of stabilographic indicators on the level of anxiety was also confirmed by the results of correlation analysis. After passing the training game, there was a tendency to decrease in stabilographic indicators. This indicates an increase in posture stability. Correlation analysis showed that the effect of PA on posture maintenance decreased, while the role of self-regulation increased. It was found that the higher the level of self-regulation, the lower the amount of body oscillations. Thus, the activation of voluntary regulation mechanisms reduced the influence of personal anxiety on the stability of maintaining the posture of the human body.

Volleyball Action Extraction and Dynamic Recognition Based on Gait Tactile Sensor

At present, the industry research of volleyball technology is relatively in‐depth, and the analysis of the muscle strength characteristics and coordination of the jumping ball is less, which is not conducive to the control of technical movements. This study used a wireless portable surface EMG tester (16 lines) to analyze the EMG of the main muscle groups in athletes’ volleyball and conducted a video synchronization test method to find the position of the human body. Therefore, a background‐based frame difference method is proposed to detect the position and obtain the precise position of the human body. Experiments show that the background‐based three‐frame difference method effectively eliminates the “hole” effect of the original three‐frame difference method and provides an accurate and complete framework for identifying the human body. Adjust the recognition frame according to the proportion of the human body in the image, and use the predefined parameters of the severe frame to perform forward/volleyball background segmentation. The novelty of this document lies in the completion of the complete human body placement of the above three tasks, precapture/background segmentation, and an improved human body position estimation algorithm to extract the human body pose from the video. First, locate the human body in each frame of the video, and then, perform the process of estimating the position of the graphic model based on the color and texture of the unit. After recognizing the gesture of each image in the video, the recognition result will be displayed. Experiments show that after detecting the position of the human body, the predefined frame setting process of the tomb is carried out in two steps, which improves the automation of the human body image detection algorithm, effectively extracts the human motion video, and increases the motion capture rate by more than 30%, to provide a useful reference for the improvement of college volleyball players’ movement skills and training competitions.

Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed, 3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are used coherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus, the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional lift to provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper.

Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.

Breathable, washable and wearable woven-structured triboelectric nanogenerators utilizing electrospun nanofibers for biomechanical energy harvesting and self-powered sensing

With the rapid advancement in wearable electronics, energy harvesting devices based on triboelectric nanogenerators (TENGs) have been intensively investigated for providing sustainable power supply for them. However, the fabrication of wearable TENGs still remains great challenges, such as flexibility, breathability and washability. Here, a route to develop a new kind of woven-structured triboelectric nanogenerator (WS-TENG) with a facile, low-cost, and scalable electrospinning technique is reported. The WS-TENG is fabricated with commercial stainless-steel yarns wrapped by electrospun polyamide 66 nanofiber and poly(vinylidenefluoride-co-trifluoroethylene) nanofiber, respectively. Triggered by diversified friction materials under a working principle of freestanding mode, the open-circuit voltage, short-circuit current and maximum instantaneous power density from the WS-TENG can reach up to 166 V, 8.5 µA and 93 mW/m 2 , respectively. By virtue of high flexibility, desirable breathability, washability and excellent durability, the fabricated WS-TENG is demonstrated to be a reliable power textile to light up 58 light-emitting diodes (LED) connected serially, charge commercial capacitors and drive portable electronics. A smart glove with stitched WS-TENGs is made to detect finger motion in different circumstances. The work presents a new approach for self-powered textiles with potential applications in biomechanical energy harvesting, wearable electronics and human motion monitoring. A brand-new type of nanofiber-based triboelectric nanogenerator with simple woven-fabric structure, remarkable breathability and washability, diversified triggering materials and reliable durability was designed and realized by utilizing novel core-shell yarns composed of functional nanofiber coating layer and core electrode yarn by a versatile electrospinning and scalable weaving strategy for the first time, and its promising potentials for harvesting biomechanical energy and self-powered sensing applications were demonstrated. • A brand-new type of nanofiber-based TENG with woven fabric structure was prepared by an electrospinning and weaving strategy. • The obtained WS-TENGs possess remarkable air and moisture permeability, robust durability and good washability. • The WS-TENGs can be triggered by diverse frictional materials and influential factors on output performance were studied. • Fixed at different positions of human body, WS-TENGs show application potential for biomechanical energy harvesting. • A smart-glove with stitched WS-TENGs was fabricated to demonstrate the capacity for self-powered sensing application.

Critical Appraisal of Surface Electromyography (sEMG) as a Taught Subject and Clinical Tool in Medicine and Kinesiology.

The characteristics and state of knowledge of bioelectric signals such as ECG, EEG, and EMG are initially discussed. This serves as the basis for exploration of the degree of scholastic coverage and understanding of the level of clinical acceptance of respective bioelectric signal subtypes during the last 60 or so years. The review further proceeds to discuss surface EMG (sEMG). The status of the field in terms of teaching and academic training related to sEMG is examined, and its clinical acceptance in several areas of medicine and kinesiology, including neurology, psychology, psychiatry, physiatry, physical medicine and rehabilitation, biomechanics and motor control, and gnathology, is evaluated. A realistic overview of the clinical utility of the measurement of sEMG signals and their interpretation and usage, as well as of perspectives on its development, are then provided. The main focus is on the state of the field in Croatia. EMG signals are viewed as “windows” into the function of the neuro-muscular system, a complex and hierarchically organized system that controls human body posture and gross body movement. New technical and technological means to enable the detection and measurement of these signals will contribute to increased clinical acceptance, provided current scientific, educational, and financial obstacles can be removed.

Modelling and simulation of an integrated human-vehicle system with non-linear cushion contact force

Driver comfort is related to human-seat interaction forces and the seat suspension system. For a human body subjected to vertical seat vibration, the head exhibits vertical as well as fore-aft motion, which leads to high forces in the neck. In order to capture these effects, a detailed 12 degrees of freedom two-dimensional seated human body model with inclined backrest support is developed to study direct and cross-axis seat to head transmissibility. The human model is then integrated with a non-linear cushion-human interaction model. The cushion parameters are obtained using a genetic algorithm and a global criterion-based scheme to minimize the least square difference between experimental contact force-time signal and analytical results. Subsequently, the human body model, along with a non-linear seat cushion model, are incorporated into a full vehicle model. Seat suspension parameters are obtained by minimizing the seat effective amplitude transmissibility and human body comfort factors. Both random road excitation and sudden bumps on the road have been studied. The influence of the human body's position in the vehicle and parameter sensitivity with respect to cushion-human interaction force, vertical and fore-aft head acceleration are investigated for multi-compression damper with an inclined damper-seat suspension system. The angle of inclination of the damper has been found to be the parameter with the highest sensitivity. These results and the modelling methodology are expected to lead to a better way of analyzing biodynamic response of the driver.