Research of the Joint Workspaces and Kinematic Efficiency of Man-Machine Systems of Bicycles

2010 ◽  
Vol 450 ◽  
pp. 13-18
Author(s):  
Jenq Huey Shyu ◽  
I Tsung Lai ◽  
Ta Chang ◽  
Yun Cheng Wang ◽  
Ta Wei Lin
Keyword(s):  
Kinematic Model ◽  
Human Motion ◽  
Lower Limbs ◽  
Human Factor Engineering ◽  
Physical Measurements ◽  
Effective Moment ◽  
Machine Systems ◽  
Recumbent Bicycle ◽  
Bicycle Design ◽  
Kinematic Efficiency

Bicycle design largely contradicts human motion, necessitating consideration of both the bicycle structure and the kinematic efficiency in the dimensions of the rider’s limbs, as well as human factor engineering, i.e. comfortability. By focusing on the kinematic model of 5-bar linkage and joints workspace, this study examines the most appropriate bicycle design and the riding posture to ensure that muscles can produce the effective moment and increase driving efficiency of a crank necessary. For upright, racing and recumbent bicycle types, assumptions are made regarding mobility analysis and the system of man-machine systems of bicycles estimated as well. Simulation results can identify the major dimensions of bicycle designing for different riders efficiently by inputting physical measurements of the rider and the angle range of driving force, subsequently increasing the riding efficiency to decrease the load of lower limbs of riders and satisfying ergonomic requirements of bicycle riders.

Simulation Analysis of Foot Force in Athletes’ Training Process Based on Sports Biomechanics

2021 ◽  
Vol 7 (5) ◽  
pp. 4900-4913
Author(s):  
Li Huang ◽  
Jianqiu Hu
Keyword(s):  
Simulation Analysis ◽  
Rapid Development ◽  
Human Motion ◽  
Lower Limbs ◽  
Lower Extremity Injury ◽  
Foot Pressure ◽  
Modeling And Analysis ◽  
Analysis System ◽  
Foot Joint ◽  
Sports Biomechanics

Objectives: With the rapid development of sports biomechanics, a new frontier discipline, the modeling and Simulation of human motion, as one of the cutting-edge research topics of sports biomechanics, is receiving more and more attention.. Methods: Based on this, this paper provides theoretical support for the analysis and research of foot stress in the process of training and applies it to guiding practice by using the analysis technology based on sports biomechanics and the method of foot pressure and simulation modeling and analysis system. Results: The results of the study showed that the injury of the athletes in the lower limbs accounted for about 46.7%, followed by the injury of the upper limbs and the injury of the trunk. In the lower extremity injury, the most common part of the foot joint is about 28.1%. Conclusion: Studies have shown that the changes in the force of the athlete’s foot after fatigue have not had the good stability before, the duration of each stage of the completion of the movement is changing, and the control of the ankle joint is decreasing, which greatly increases the foot joint. The possibility of injury.

scholarly journals A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2761 ◽  
Author(s):  
Wenxia Xu ◽  
Jian Huang ◽  
Lei Cheng
Keyword(s):  
Human Locomotion ◽  
Detection Algorithm ◽  
Human Motion ◽  
Lower Limbs ◽  
Support Vector ◽  
Normal Walking ◽  
Coordinated Motion ◽  
Fusion Algorithm ◽  
Robot System ◽  
Walking Aid

Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user’s normal walking state and abnormal walking state for a walking-aid robot system. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user’s upper and lower limbs. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user’s falling mode when he/she is in an abnormal walking state. The experimental results verify the effectiveness of the proposed algorithms.

scholarly journals The Impact Of Thigh And Shank Marker Quantity On Lower Extremity Kinematics Using A Constrained Model

2018 ◽  
Author(s):  
Annelise A Slater ◽  
Todd J. Hullfish ◽  
Josh R. Baxter
Keyword(s):  
Lower Extremity ◽  
Kinematic Model ◽  
Associate Editor ◽  
Human Motion ◽  
Joint Kinematics ◽  
Intellectual Content ◽  
Competing Interests ◽  
Musculoskeletal Models ◽  
Soft Tissue Artifact ◽  
The Impact

AbstractMusculoskeletal models are commonly used to quantify joint motions and loads during human motion. Constraining joint kinematics simplifies these models but the implications of the number of markers used during data acquisition remains unclear. The purpose of this study was to establish the effects of marker placement and quantity on kinematic fidelity when using a constrained-kinematic model. We hypothesized that a constrained-kinematic model would faithfully reproduce lower extremity kinematics regardless of the number of tracking markers removed from the thigh and shank. Healthy-young adults (N = 10) walked on a treadmill at slow, moderate, and fast speeds while skin-mounted markers were tracked using motion capture. Lower extremity kinematics were calculated for 256 combinations of leg and shank markers to establish the implications of marker placement and quantity on joint kinematics. Sagittal joint and hip coronal kinematics errors were smaller than documented errors caused by soft-tissue artifact, which tends to be approximately 5 degrees, when excluding thigh and shank markers. Joint angle and center kinematic errors negatively correlated with the number of markers included in the analyses (R2 > 0.97) and typically showed the greatest error reductions when two markers were included. Further, we demonstrated that a simplified marker set that included markers on the pelvis, lateral knee condyle, lateral malleolus, and shoes produced kinematics that strongly agreed with the traditional marker set. In conclusion, constrained-kinematic models are resilient to marker placement and quantity, which has implications on study design and post-processing workflows.Ethics Approval and Consent to Participate this study was approved by the Institutional Review Board at the University of Pennsylvania (#824466). Subjects provided written-informed consentConsent to Publish this submission does not contain any individual dataAvailability of Data and Materials the datasets analyzed in this study are available from the corresponding author on reasonable request.Competing Interests one author (JB) is an associate editor for BMC Musculoskeletal Disorders. None of the other authors have any competing interests.Funding no funding has been provided for this researchAuthors’ ContributionsAS, TH, and JB designed the experiment; AS and TH collected the data; AS and JB analyzed and interpreted the data; AS and JB drafted the manuscript; AS, TH, and JB revised the intellectual content of the manuscript; AS, TH, and JB approved the final version of the manuscript; and AS, TH, and JB agreed to be accountable for all aspects of the study.

scholarly journals High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1238 ◽  
Author(s):  
Irvin López-Nava ◽  
Angélica Muñoz-Meléndez
Keyword(s):  
Daily Living ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Research Problem ◽  
Human Motion ◽  
Lower Limbs ◽  
Smart Devices ◽  
Joint Orientation ◽  
Human Actions ◽  
Living Environments

Action recognition is important for various applications, such as, ambient intelligence, smart devices, and healthcare. Automatic recognition of human actions in daily living environments, mainly using wearable sensors, is still an open research problem of the field of pervasive computing. This research focuses on extracting a set of features related to human motion, in particular the motion of the upper and lower limbs, in order to recognize actions in daily living environments, using time-series of joint orientation. Ten actions were performed by five test subjects in their homes: cooking, doing housework, eating, grooming, mouth care, ascending stairs, descending stairs, sitting, standing, and walking. The joint angles of the right upper limb and the left lower limb were estimated using information from five wearable inertial sensors placed on the back, right upper arm, right forearm, left thigh and left leg. The set features were used to build classifiers using three inference algorithms: Naive Bayes, K-Nearest Neighbours, and AdaBoost. The F- m e a s u r e average of classifying the ten actions of the three classifiers built by using the proposed set of features was 0.806 ( σ = 0.163).

Lower Limb Cycling Mechanics in Subjects with Unilateral Cerebrovascular Accidents

1998 ◽  
Vol 14 (2) ◽  
pp. 158-179 ◽  
Author(s):  
Karen L. Perell ◽  
Robert J. Gregor ◽  
A.M. Erika Scremin
Keyword(s):  
Power Generation ◽  
Lower Limb ◽  
Lower Limbs ◽  
Biomechanical Analysis ◽  
Lesion Site ◽  
Joint Power ◽  
Joint Kinetics ◽  
Cerebrovascular Accidents ◽  
Segment Model ◽  
Recumbent Bicycle

Biomechanical analysis of the generalized muscle moment and power patterns involved in cycling provides information regarding coordination within each limb. The purpose of this study was to compare individual joint kinetics, bilaterally, in subjects who had experienced cerebrovascular accidents (CVAs). Two-dimensional cinematography and force pedal data in a linked-segment model were used to study 8 ambulatory subjects while they rode a recumbent bicycle. The involved lower limb was defined as the lower limb with the greatest deficits, whereas the contralateral lower limb was defined as the lower limb opposite the involved lower limb and ipsilateral to the lesion site. The contralateral lower limbs of subjects with CVAs demonstrated patterns similar to those reported for nondisabled cyclists on an upright bicycle except for a bimodal hip power generation pattern that was possibly due to compensation for a lack of involved lower limb power generation. There were two critical findings of this study: Single-joint power generation patterns during the power phase indicated that either the hip or the knee, but not both joints, generated power in the involved lower limb, and asymmetrical differences between lower limbs appeared significant at the ankle alone.

scholarly journals An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Chengyu Guo ◽  
Jie Liu ◽  
Xiaohai Fan ◽  
Aihong Qin ◽  
Xiaohui Liang
Keyword(s):  
Low Cost ◽  
Recognition Rate ◽  
Recognition Task ◽  
Graph Model ◽  
Autoregressive Process ◽  
Human Motion ◽  
Lower Limbs ◽  
Body Motion ◽  
Fusion Model ◽  
Full Body

This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM) and autoregressive process, a predictive fusion model (PFM) is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

scholarly journals An innovative equivalent kinematic model of the human upper limb to improve the trajectory planning of exoskeleton rehabilitation robots

2021 ◽  
Vol 12 (1) ◽  
pp. 661-675
Author(s):  
Qiaolian Xie ◽  
Qiaoling Meng ◽  
Qingxin Zeng ◽  
Hongliu Yu ◽  
Zhijia Shen
Keyword(s):  
Upper Limb ◽  
Trajectory Planning ◽  
Wrist Joint ◽  
Kinematic Model ◽  
Human Motion ◽  
Upper Limb Exoskeleton ◽  
Rehabilitation Robots ◽  
Proposed Model ◽  
Human Arm ◽  
Human Upper Limb

Abstract. Upper limb exoskeleton rehabilitation robots have been attracting significant attention by researchers due to their adaptive training, highly repetitive motion, and ability to enhance the self-care capabilities of patients with disabilities. It is a key problem that the existing upper limb exoskeletons cannot stay in line with the corresponding human arm during exercise. The aim is to evaluate whether the existing upper limb exoskeleton movement is in line with the human movement and to provide a design basis for the future exoskeleton. This paper proposes a new equivalent kinematic model for human upper limb, including the shoulder joint, elbow joint, and wrist joint, according to the human anatomical structure and sports biomechanical characteristics. And this paper analyzes the motion space according to the normal range of motion of joints for building the workspace of the proposed model. Then, the trajectory planning for an upper limb exoskeleton is evaluated and improved based on the proposed model. The evaluation results show that there were obvious differences between the exoskeleton prototype and human arm. The deviation between the human body and the exoskeleton of the improved trajectory is decreased to 41.64 %. In conclusion, the new equivalent kinematics model for the human upper limb proposed in this paper can effectively evaluate the existing upper limb exoskeleton and provide suggestions for structural improvements in line with human motion.

scholarly journals Health Index Monitoring of Sports Injury Rehabilitation Training Based on Wearable Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jiang Yi ◽  
Yuepei Zou
Keyword(s):  
Human Body ◽  
Lower Limb ◽  
Wearable Sensors ◽  
Sports Injury ◽  
Human Motion ◽  
Lower Limbs ◽  
Angle Sensor ◽  
Perception System ◽  
Movement Mechanism ◽  
Analysis System

In order to design the perception system of the lower limb wearable rehabilitation robot, this study established the kinematics theoretical model of human lower limb and conducted the kinematics analysis of human body. By using the dynamic attitude analysis system, combined with the human body mark points, the position data of human body mark points in the process of standing up, sitting up, walking, stepping up, and squatting were collected. Combined with the movement mechanism of human lower limbs, the characteristics of human motion state transition are analyzed, and the perceptual algorithm for judging human motion intention is studied, so as to determine the wearer’s current posture, standing intention while sitting, walking intention while standing, moving intention, and stopping intention during walking. The results show that the angle of the hip joint changes regularly between 0° and 37° and the angle of the knee joint changes regularly between 0° and 70°during the standing process, which is consistent with the angle change trajectory collected by the dynamic attitude analysis system. The angle trajectories of the hip and knee joints measured by the absolute angle sensor are the same as those obtained by the dynamic attitude analysis system. 1.5 rad and 0.3 rad were selected as reasonable and effective thresholds for determining sitting and standing states.

Measurement and Analysis of Human Lower Limbs Movement Parameters during Walking

2015 ◽  
Vol 220-221 ◽  
pp. 538-543 ◽  
Author(s):  
Sergei Zhigailov ◽  
Artem Kuznetcov ◽  
Victor Musalimov ◽  
Gennady Aryassov
Keyword(s):  
Human Motion ◽  
The Body ◽  
Lower Limbs ◽  
Human Gait ◽  
Inertial Measurement ◽  
Double Support ◽  
Motion Parameters ◽  
Using Data ◽  
Movement Parameters ◽  
Different Parts

It is necessary to analyze human gait for treatment and rehabilitation of human with musculoskeletal disorders of the locomotion apparatus (LA). The main goal of this work is evaluation of locomotion apparatus motion parameters captured by inertial measurement units (IMU) during walking. Motion Capture technology is process of getting practical results and data from IMU installed in different parts of human lower limbs. Synchronously, IMU send information about human movements to PC at the same moment of time. Such method gives an opportunity to follow parameters in some points of human leg in real time. The way of devices mounting and instruction for human under monitoring are based on related medical projects. Walking is selected for estimation of the musculoskeletal system as typical action. Experiment results got from several experiments were considered and analyzed.Basically, walking is described as a set of the system “human” discrete states. In the same time, the IMU sensors transmit motion parameters data continuously. It is proposed to present the man as a system with a control signal in the form of the double support period. The length will be measured using data from IMU. Double support period is chosen because its presence distinguishes walking from running.The most attention is given to getting the same practical results and data that can be obtained by placing the devices in different parts of the body. Moreover, a technique of using inertial measurement devices for measuring human motion to get some numerical results is shown. The use of this technique in practice demonstrated that it can be used to obtain an objective parameter describing the motion of the person. Continuation of this work is directed to create a complete model of the lower limbs motion for usage in practice [1].

scholarly journals A wearable motion capture device able to detect dynamic motion of human limbs

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Shiqiang Liu ◽  
Junchang Zhang ◽  
Yuzhong Zhang ◽  
Rong Zhu
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Wearable Device ◽  
Lower Limbs ◽  
Motion Recognition ◽  
Dynamic Motion ◽  
Assistive Robots ◽  
Limb Coordination

Abstract Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.

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