Analysis of muscle forces in lower limbs based on wearable sensors

2010 ◽  
Author(s):  
Enguo Cao ◽  
Yoshio Inoue ◽  
Tao Liu ◽  
Kyoko Shibata
Keyword(s):  
Wearable Sensors ◽  
Lower Limbs ◽  
Muscle Forces

scholarly journals Analysis of 3-D Kinematics Using H-Gait System during Walking on a Lower Body Positive Pressure Treadmill

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2619
Author(s):  
Yoshiaki Kataoka ◽  
Ryo Takeda ◽  
Shigeru Tadano ◽  
Tomoya Ishida ◽  
Yuki Saito ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Wearable Sensors ◽  
Three Dimensional ◽  
Lower Limbs ◽  
Positive Pressure ◽  
Lower Body ◽  
Lower Body Positive Pressure ◽  
Gait Kinematics ◽  
Peak Knee ◽  
Body Positive

Recently, treadmills equipped with a lower-body positive-pressure (LBPP) device have been developed to provide precise body weight support (BWS) during walking. Since lower limbs are covered in a waist-high chamber of an LBPP treadmill, a conventional motion analysis using an optical method is impossible to evaluate gait kinematics on LBPP. We have developed a wearable-sensor-based three-dimensional motion analysis system, H-Gait. The purpose of the present study was to investigate the effects of BWS by a LBPP treadmill on gait kinematics using an H-Gait system. Twenty-five healthy subjects walked at 2.5 km/h on a LBPP treadmill under the following three conditions: (1) 0%BWS, (2) 25%BWS and (3) 50%BWS conditions. Acceleration and angular velocity from seven wearable sensors were used to analyze lower limb kinematics during walking. BWS significantly decreased peak angles of hip adduction, knee adduction and ankle dorsiflexion. In particular, the peak knee adduction angle at the 50%BWS significantly decreased compared to at the 25%BWS (p = 0.012) or 0%BWS (p < 0.001). The present study showed that H-Gait system can detect the changes in gait kinematics in response to BWS by a LBPP treadmill and provided a useful clinical application of the H-Gait system to walking exercises.

Assessment of Internal Loads in the Joints of the Lower Extremities During the Snatch in Young Weightlifters

2019 ◽  
Vol 19 (05) ◽  
pp. 1941011
Author(s):  
Adam Czaplicki ◽  
Krzysztof Dziewiecki ◽  
Zenon Mazur ◽  
Wojciech Blajer
Keyword(s):  
Inverse Dynamics ◽  
Sagittal Plane ◽  
Lower Extremities ◽  
Lower Limbs ◽  
Muscle Forces ◽  
Kinematic Data ◽  
Lower Trunk ◽  
Left And Right ◽  
Ground Reactions ◽  
Inverse Dynamics Problem

The aim of this paper is to present the results of an assessment of internal loads in the joints of the lower limbs during the snatch performed by young weightlifters. A planar model of a weightlifter composed of 7 rigid segments (the lower trunk, thighs, lower legs and feet) connected by six hinge joints was used in the computations. The dynamic equations of the motion of the model were obtained using a projective technique. Kinematic data were recorded by a Vicon system with a sampling frequency of 200 Hz. The ground reactions were measured independently for the left and right limbs on two force platforms. The inverse dynamics problem was solved to assess the internal loads (the muscle forces and joint reactions) in the lower limbs. Relatively high differences in the reactions in the joints and muscle forces in the left and right lower extremities were identified. The obtained results also reveal that the snatch, a lift which tends to be geometrically symmetrical in the sagittal plane, is not necessarily characterized by symmetry of internal loads. Thus, this study has shown that a kinematics analysis of the lifter’s movement, which is commonly used to assess the technique of the snatch, is insufficient and should be supplemented with a dynamics analysis.

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).

scholarly journals Effect of Dropping Height on the Forces of Lower Extremity Joints and Muscles during Landing: A Musculoskeletal Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenxin Niu ◽  
Lejun Wang ◽  
Chenghua Jiang ◽  
Ming Zhang
Keyword(s):  
Computational Simulation ◽  
Reaction Force ◽  
Gluteus Maximus ◽  
Lower Extremities ◽  
Rectus Femoris ◽  
Gluteus Medius ◽  
Biceps Femoris ◽  
Lower Limbs ◽  
Muscle Forces ◽  
Element Analysis

The objective of this study was to investigate the effect of dropping height on the forces of joints and muscles in lower extremities during landing. A total of 10 adult subjects were required to landing from three different heights (32 cm, 52 cm, and 72 cm), and the ground reaction force and kinematics of lower extremities were measured. Then, the experimental data were input into the AnyBody Modeling System, in which software the musculoskeletal system of each subject was modeled. The reverse dynamic analysis was done to calculate the joint and muscle forces for each landing trial, and the effect of dropping-landing on the results was evaluated. The computational simulation showed that, with increasing of dropping height, the vertical forces of all the hip, knee, and ankle joints, and the forces of rectus femoris, gluteus maximus, gluteus medius, vastii, biceps femoris and adductor magnus were all significantly increased. The increased dropping height also resulted in earlier activation of the iliopsoas, rectus femoris, gluteus medius, gluteus minimus, and soleus, but latter activation of the tibialis anterior. The quantitative joint and muscle forces can be used as loading conditions in finite element analysis to calculate stress and strain and energy absorption processes in various tissues of the lower limbs.

scholarly journals Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3713
Author(s):  
Federica Aprigliano ◽  
Silvestro Micera ◽  
Vito Monaco
Keyword(s):  
Wearable Sensors ◽  
Detection Algorithm ◽  
Lower Limbs ◽  
False Alarms ◽  
Daily Life Activities ◽  
Impact Detection ◽  
Wearable Robots ◽  
Adaptive Oscillators ◽  
Young Subjects ◽  
Smart Garments

This study aimed to investigate the performance of an updated version of our pre-impact detection algorithm parsing out the output of a set of Inertial Measurement Units (IMUs) placed on lower limbs and designed to recognize signs of lack of balance due to tripping. Eight young subjects were asked to manage tripping events while walking on a treadmill. An adaptive threshold-based algorithm, relying on a pool of adaptive oscillators, was tuned to identify abrupt kinematics modifications during tripping. Inputs of the algorithm were the elevation angles of lower limb segments, as estimated by IMUs located on thighs, shanks and feet. The results showed that the proposed algorithm can identify a lack of balance in about 0.37 ± 0.11 s after the onset of the perturbation, with a low percentage of false alarms (<10%), by using only data related to the perturbed shank. The proposed algorithm can hence be considered a multi-purpose tool to identify different perturbations (i.e., slippage and tripping). In this respect, it can be implemented for different wearable applications (e.g., smart garments or wearable robots) and adopted during daily life activities to enable on-demand injury prevention systems prior to fall impacts.

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.

scholarly journals OpenSim Versus Human Body Model: A Comparison Study for the Lower Limbs During Gait

2018 ◽  
Vol 34 (6) ◽  
pp. 496-502 ◽  
Author(s):  
Antoine Falisse ◽  
Sam Van Rossom ◽  
Johannes Gijsbers ◽  
Frans Steenbrink ◽  
Ben J.H. van Basten ◽  
...  
Keyword(s):  
Human Body ◽  
Reference Frames ◽  
Sagittal Plane ◽  
Joint Kinematics ◽  
Lower Limbs ◽  
Muscle Forces ◽  
Human Body Model ◽  
Body Model ◽  
Human Movements ◽  
Joint Center

Musculoskeletal modeling and simulations have become popular tools for analyzing human movements. However, end users are often not aware of underlying modeling and computational assumptions. This study investigates how these assumptions affect biomechanical gait analysis outcomes performed with Human Body Model and the OpenSim gait2392 model. The authors compared joint kinematics, kinetics, and muscle forces resulting from processing data from 7 healthy adults with both models. Although outcome variables had similar patterns, there were statistically significant differences in joint kinematics (maximal difference: 9.8° [1.5°] in sagittal plane hip rotation), kinetics (maximal difference: 0.36 [0.10] N·m/kg in sagittal plane hip moment), and muscle forces (maximal difference: 8.51 [1.80] N/kg for psoas). These differences might be explained by differences in hip and knee joint center locations up to 2.4 (0.5) and 1.9 (0.2) cm in the posteroanterior and inferosuperior directions, respectively, and by the offset in pelvic reference frames of about 10° around the mediolateral axis. The choice of model may not influence the conclusions in clinical settings, where the focus is on interpreting deviations from the reference data, but it will affect the conclusions of mechanical analyses in which the goal is to obtain accurate estimates of kinematics and loading.

Lower-Limb Rehabilitation at Home

2020 ◽  
Vol 12 (1) ◽  
pp. 15-27
Author(s):  
Seanglidet Yean ◽  
Bu Sung Lee ◽  
Chai Kiat Yeo
Keyword(s):  
Wearable Sensors ◽  
Initial Study ◽  
Lower Limbs ◽  
Support Vector ◽  
Machine Method ◽  
Average Accuracy ◽  
Sensor Signals ◽  
And Performance ◽  
The Impact ◽  
Rehabilitation Exercise

Aging causes loss of muscle strength, especially on the lower limbs, resulting in a higher risk of injuries during functional activities. To regain mobility and strength from injuries, physiotherapy prescribes rehabilitation exercise to assist the patients' recovery. In this article, the authors survey the existing work in exercise assessment and state identification which contributes to innovating the biofeedback for patient home guidance. The initial study on a machine-learning-based model is proposed to identify the 4-state motion of rehabilitation exercise using wearable sensors on the lower limbs. The study analyses the impact of the feature extracted from the sensor signals while classifying using the linear kernel of the support vector machine method. The evaluation results show that the method has an average accuracy of 95.83% using the raw sensor signal, which has more impact than the sensor fused Euler and joint angles in the state prediction model. This study will both enable real-time biofeedback and provide complementary support to clinical assessment and performance tracking.

scholarly journals Effectiveness of the power and speed dry-land training in female swimmers aged 15–16

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Agata Guzik-Kopyto ◽  
Katarzyna Nowakowska-Lipiec ◽  
Anna Nocoń ◽  
Marek Gzik ◽  
Robert Michnik
Keyword(s):  
Elbow Joint ◽  
Lower Limbs ◽  
Muscle Forces ◽  
Dry Land ◽  
Muscle Groups ◽  
Group 2 ◽  
Water Exercises ◽  
The Impact ◽  
Joint Method ◽  
Group 1

Purpose: This work aimed to define the impact of the introduction of power and speed dry-land training in female swimmers aged 15–16 on the rise of time results at a distance of 200 m and on the increase of the strength level of the muscle groups in the elbow joint. Method: The investigations were conducted on a group of 28 junior female swimmers: group 1 (aged 13–14) with speed and endurance training based on “water” exercises; group 2 (aged 15–16) with extra power and speed dry-land training. The following parameters were analyzed: time results, the moments of muscle forces in the elbow joint at the extension and flexion movements in isokinetic conditions and the ratio of the values of moments of muscle forces of flexors in relation to extensors. Results: Statistically significant differences between groups were found for the following parameters: the time results from swimming 200 m with (p < 0.001) and without using lower limbs (p = 0.031), the ratio of the moments of muscle forces of flexors to extensors (p < 0.05). Conclusions: The results of the correlation analysis show that the higher the moments of muscle forces of flexors and extensors of the elbow joint, the shorter the time obtained in swimming 200 m in the freestyle stroke.

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