Human Kinematics Research Articles

A Power Assistant Algorithm Based on Human–Robot Interaction Analysis for Improving System Efficiency and Riding Experience of E-Bikes

As robots are becoming more accessible in our daily lives, the interest in physical human–robot interaction (HRI) is rapidly increasing. An electric bicycle (E-bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E-bike in close proximity. Most commercially available E-bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E-bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E-bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E-bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm.

The nature effect in motion: visual exposure to environmental scenes impacts cognitive load and human gait kinematics.

Prolonged exposure to urban environments requires higher cognitive processing resources than exposure to nature environments, even if only visual cues are available. Here, we explored the moment-to-moment impact of environment type on visual cognitive processing load, measuring gait kinematics and reaction times. In Experiment 1, participants (n = 20) walked toward nature and urban images projected in front of them, one image per walk, and rated each image for visual discomfort. Gait speed and step length decreased for exposure to urban as compared with nature scenes in line with gait changes observed during verbal cognitive load tasks. We teased apart factors that might contribute to cognitive load: image statistics and visual discomfort. Gait changes correlated with subjective ratings of visual discomfort and their interaction with the environment but not with low-level image statistics. In Experiment 2, participants (n = 45) performed a classic shape discrimination task with the same environmental scenes serving as task-irrelevant distractors. Shape discrimination was slower when urban scenes were presented, suggesting that it is harder to disengage attention from urban than from nature scenes. This provides converging evidence that increased cognitive demands posed by exposure to urban scenes can be measured with gait kinematics and reaction times even for short exposure times.

3D Hand Gestures Segmentation and Optimized Classification Using Deep Learning

Hand gestures recognition system has massive applications which are mainly utilized in robotics and computer vision specially to control Unmanned Aerial Vehicles (UAV). These methods bypass the presence of electronic control to UAVs and provide an ease to the operators. In this paper, we present a method for 3D hand gestures segmentation and classification by combining MASK-RCNN with Grass Hopper Optimization. We created a private 3D and RGB hand gestures dataset using Intel Kinetic and Intel Real sense d435i camera, then proposed a model for RGB hand gestures to estimate the key points using human kinematics, the key points later then utilize to get the best degree of freedom (DoF). The grass hopper optimization besides minimum distance function was applied to achieve the finest deep features from the 3D hand gestures dataset. The ResNet50 network is used as the backbone to calculate the Overlap Coefficient (OC) for segmentation and the ResNet50, ResNet101 networks to calculate the classification for 3D hand gestures. The classification accuracy achieved on the private dataset is 99.05% and 99.29% on public Microsoft Kinect and Leap Motion dataset where the OC are 88.16%. and 88.19% respectively.

Research on the Application of Wireless Wearable Sensing Devices in Interactive Music

Wireless wearable devices can greatly assist and promote the artistic presentation of interactive music and have attracted the attention of more and more composers, musicians, dancers, and visual artists. It can pick up data information in real time, integrate with performers, and provide immersive performance experience. It not only builds a bridge between subjective feeling and spiritual perception for performers and audience but also enables audience to directly observe art information better. This paper mainly introduces the development process of a wearable sensor system designed for monitoring interactive music movement. Firstly, an interactive music motion model is established according to the principle of human body kinematics, and the experimental scheme of measuring the swaying angle of interactive music with a single sensor device is standardized. A multisensor fusion algorithm is proposed to estimate the swing angle of interactive music. Based on the “cost‐incentive” emotional model, the wireless wearable device and interactive music model are regarded as continuous variables determined by the emotional effect value and the incentive value. Extract energy, rhythm, harmony, time domain, and spectrum features of interactive music of wireless wearable devices, and reduce the dimension of a music feature space through principal component analysis, spatial projection, and relief feature selection. Finally, the practicability of the system and the accuracy of the algorithm are verified by experiments. The recognition rate of wireless wearable devices and interactive music realized based on this algorithm was improved.

Upper-Limb Kinematic Parameter Estimation and Localization Using a Compliant Robotic Manipulator

Assistive and rehabilitation robotics have gained momentum over the past decade and are expected to progress significantly in the coming years. Although relevant and promising research advances have contributed to these fields, challenges regarding intentional physical contact with humans remain. Despite being a fundamental component of assistive and rehabilitation tasks, there is an evident lack of work related to robotic manipulators that intentionally manipulate human body parts. Moreover, existing solutions involving end-effector robots are not based on accurate knowledge of human limb dimensions and their current configuration. This knowledge, which is essential for safe human-limb manipulation, depends on the grasping location and human kinematic parameters. This paper addresses the upper-limb manipulation challenge and proposes a pose estimation method using a compliant robotic manipulator. To the best of our knowledge, this is the first attempt to address this challenge. A kinesthetic-based approach enables estimation of the kinematic parameters of the human arm without integrating external sensors. The estimation method relies only on proprioceptive data obtained from a collaborative robot with a Cartesian impedance-based controller to follow a compliant trajectory that depends on human arm kinodynamics. The human arm model is a 2-degree of freedom (DoF) kinematic chain. Thus, prior knowledge of the arm's behavior and an estimation method enables estimation of the kinematic parameters. Two estimation methods are implemented and compared: i) Hough transform (HT); ii) least squares (LS). Furthermore, a resizable, sensorized dummy arm is designed for experimental validation of the proposed approach. Outcomes from six experiments with different arm lengths demonstrate the repeatability and effectiveness of the proposed methodology, which can be used in several rehabilitation robotic applications.

Comparison of action recognition from video and IMUs

The aim of the study is to create an intelligent control system for automatic assistance to the operator of a semi-passive exoskeleton. This work solves the problem of recognizing human movements, since it is necessary to build a control system based on human movements in an exoskeleton. An experimental comparative study of two approaches to create human models for action recognition is considered. The first approach is based on the reconstruction of a simplified human kinematic model from IMU data, represented in special format. The second approach is to obtain 2D coordinates of a person’s key points from video from two different views. The sets of human models obtained from these approaches are classified using the XGBoost algorithm. The resulting classifiers are compared in terms of accuracy.

Алгоритм калибровки группы MIMU датчиков

Non-optical wearable sensors, such as magnetic and inertial measuring units (MIMU), are becoming popular in various fields: sports, medical, industrial - due to their ease of use and relative availability. We propose an algorithm for calibrating wearable sensors based on the rotation algebra. A system for visualizing human kinematics, which is reconstructed from MIMUs' data, is presented.

Whole Body Center of Mass Feedback in a Reflex-Based Neuromuscular Model Predicts Ankle Strategy During Perturbed Walking.

Active prosthetic and orthotic devices have the potential to increase quality of life for individuals with impaired mobility. However, more research into human-like control methods is needed to create seamless interaction between device and user. In forward simulations the reflex-based neuromuscular model (RNM) by Song and Geyer shows promising similarities with real human gait in unperturbed conditions. The goal of this work was to validate and, if needed, extend the RNM to reproduce human kinematics and kinetics during walking in unperturbed and perturbed conditions. The RNM was optimized to reproduce joint torque, calculated with inverse dynamics, from kinematic and force data of unperturbed and perturbed treadmill walking of able-bodied human subjects. Torques generated by the RNM matched closely with torques found from inverse dynamics analysis on human data for unperturbed walking. However, for perturbed walking the modulation of the ankle torque in the RNM was opposite to the modulation observed in humans. Therefore, the RNM was extended with a control module that activates and inhibits muscles around the ankle of the stance leg, based on changes in whole body center of mass velocity. The added module improves the ability of the RNM to replicate human ankle torque response in response to perturbations. This reflex-based neuromuscular model with whole body center of mass velocity feedback can reproduce gait kinetics of unperturbed and perturbed gait, and as such holds promise as a basis for advanced controllers of prosthetic and orthotic devices.

Retracted] Optimization of Interactive Animation Capture System for Human Upper Limb Movement Based on XSENS Sensor

This paper uses the XSENS sensor inertial motion capture device to collect the experimental data of the human body’s typical motion and posture‐upper limb movement, based on the angular acceleration kinematics parameters of the human body’s upper limbs and upper limbs. We study the characteristics of human kinematics, statics, and dynamics and construct the upper limb movement model of the human body. Secondly, based on the principle of human anatomy, the human body is divided into 23 segments, with 18 upper limbs and 36 degrees of freedom; some anatomical terms are defined, and a unified coordinate system for the upper limb model of the human body is planned and established. In the process of experimental simulation, on the basis of analyzing and summarizing the laws and characteristics of the upper limb angles of the hip upper limbs, knee upper limbs, and ankle upper limbs during walking, a general function of the upper limb angles of the three upper limbs changing with time during walking was established. On the basis of analyzing 40 sets of upper limb movement data, with the three parameters of height, weight, and upper limb movement cycle as independent variables, the general function coefficient solving equation is given through function fitting. Finally, the production of interactive animation of upper limb movement is taken as an example. Based on the acceleration sensor and three‐axis gyroscope, the limbs during the movement of the upper limb motion data are collected, preprocessed, and transmitted, and then, coordinate correction and data filtering are used to output quaternary parameters to give Maya an animated character model. The animation interactive demonstration is carried out in the way of web 3D, and the XSENS sensor is explored in the animation capture.

Replicating dynamic humerus motion using an industrial robot.

Transhumeral percutaneous osseointegrated prostheses provide upper-extremity amputees with increased range of motion, more natural movement patterns, and enhanced proprioception. However, direct skeletal attachment of the endoprosthesis elevates the risk of bone fracture, which could necessitate revision surgery or result in loss of the residual limb. Bone fracture loads are direction dependent, strain rate dependent, and load rate dependent. Furthermore, in vivo, bone experiences multiaxial loading. Yet, mechanical characterization of the bone-implant interface is still performed with simple uni- or bi-axial loading scenarios that do not replicate the dynamic multiaxial loading environment inherent in human motion. The objective of this investigation was to reproduce the dynamic multiaxial loading conditions that the humerus experiences in vivo by robotically replicating humeral kinematics of advanced activities of daily living typical of an active amputee population. Specifically, 115 jumping jack, 105 jogging, 15 jug lift, and 15 internal rotation trials—previously recorded via skin-marker motion capture—were replicated on an industrial robot and the resulting humeral trajectories were verified using an optical tracking system. To achieve this goal, a computational pipeline that accepts a motion capture trajectory as input and outputs a motion program for an industrial robot was implemented, validated, and made accessible via public code repositories. The industrial manipulator utilized in this study was able to robotically replicate over 95% of the aforementioned trials to within the characteristic error present in skin-marker derived motion capture datasets. This investigation demonstrates the ability to robotically replicate human motion that recapitulates the inertial forces and moments of high-speed, multiaxial activities for biomechanical and orthopaedic investigations. It also establishes a library of robotically replicated motions that can be utilized in future studies to characterize the interaction of prosthetic devices with the skeletal system, and introduces a computational pipeline for expanding this motion library.

Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

Exoskeleton robots have demonstrated the potential to rehabilitate stroke dyskinesia. Unfortunately, poor human-machine physiological coupling causes unexpected damage to human of muscles and joints. Moreover, inferior humanoid kinematics control would restrict human natural kinematics. Failing to deal with these problems results in bottlenecks and hinders its application. In this paper, the simplified muscle model and muscle-liked kinematics model were proposed, based on which a soft wrist exoskeleton was established to realize natural human interaction. Firstly, we simplified the redundant muscular system related to the wrist joint from ten muscles to four, so as to realize the human-robot physiological coupling. Then, according to the above human-like musculoskeletal model, the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the wrist. The results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3% and 35.6%, respectively. Additionally, the humanoid motion trajectories similarity of the robot reached 91.5%. Stroke patients could recover 90.3% of natural motion ability to satisfy for most daily activities. This work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

Pediatric occupant human body model kinematic and kinetic response variation to changes in seating posture in simulated frontal impacts – with and without automatic emergency braking

Objective The study quantifies the kinematics of children in booster child restraint systems (CRSs) in various naturalistic seating postures exposed to frontal impacts in a full-vehicle environment, with and without the application of pre-crash automatic emergency braking. Methods The PIPER 6YO and 10YO pediatric human body models were positioned in CRSs. The 6YO was restrained on a lowback (LBB) and highback (HBB) booster, while the 10YO was positioned on an LBB and in a NoCRS condition. All simulations used the 3-point seatbelt. The child models were pre-positioned (gravity settled, seatbelt tensioned) in four naturalistic seating postures: leaning-forward, leaning-forward-inward, leaning-forward-outward, and a pre-submarining position, along with a baseline reference seating position. A 2012 Toyota Camry finite element (FE) model was used as the vehicle environment. A standard 3-point lap-shoulder belt system was modeled to restrain the child and CRS in the left-rear vehicle seat. Two vehicle impact cases were considered: with and without a pre-crash AEB. For with-AEB cases, a pre-crash phase was run to incorporate postural changes due to the application of AEB. All seating positions were ultimately subjected to a full-frontal rigid-barrier impact at 35 MPH. A total of 40 conditions were simulated in LS-DYNA. Results Injury metrics varied widely for both occupants. Shoulder belt slippage was observed for the 6YO leaning-forward-inward on HBB. No head contact was observed for any simulated cases. Forward-leaning and forward-inward-leaning postures generally had greater head excursion across all seating postures. The lap belt rode over the pelvis for pre-submarining postures. Injury metrics for cases with pre-crash AEB were lower compared to their corresponding without-AEB cases. HIC15, head acceleration, upper neck tension force, and upper neck flexion moment were similar or lower for with-AEB scenarios. Conclusions Pre-crash AEB reduces the effect of the impact despite the same collision speed as cases without-AEB. This is primarily due to the limited travel distance of the occupant, thus, starting an earlier ride-down during the crash. Moreover, different initial seating postures lead to a wide range of injury exposures. Vehicle and child restraint design should incorporate these seating postures to ensure robust protection of the occupant in a crash.

상대가속도 적분을 통한 IMU 기반 신체 관절각과 상대위치 동시 추정

The joint angle and the relative position between two body segments connected by a joint are the main physical quantities of human joint kinematics. Usually, in the joint kinematics based on inertial measurement units (IMUs), relative positions are byproducts of the joint angle estimation; that is, the primary research theme is the joint angle estimation, while the relative position is subsequently determined based on the joint angle. However, since the human body is not perfectly rigid, deformation always occurs in human joints, resulting in inaccurate estimation of joint angle and relative position. Therefore, the relative position must be considered interdependent, rather than dependent, on the joint angle. This paper proposes an extended Kalman filter that estimates joint angle and relative position simultaneously. The proposed method uses double integration of the relative acceleration to reduce inaccuracy due to the characteristics of the human joint. It also applies joint kinematics of interdependent relationships between the joint angle and relative position. The proposed method was experimentally verified using various movement conditions of an upper limb. The results indicate that the proposed method of estimating the joint angle and relative position simultaneously is superior to conventional methods.

Flexible Piezoelectric Transducers for Energy Harvesting and Sensing from Human Kinematics

This study reports flexible nanocomposite-based piezoelectric nanogenerators (PENGs) fabricated by dispersing various piezoelectric nanoparticles (BaTiO3, ZnO, and PZT) and graphene nanopowder in a silicone matrix. The results indicated that the PZT-based composites showed superior performance in comparison to other ceramics. Subsequently, practical application of PENGs was demonstrated by developing a fully functioning shoe-insole nanogenerator (SING). The SING generated high open-circuit voltage (∼27 V), short-circuit current (429.23 μA), and power density (402 mW/m2) under real-time human walking. Moreover, a facile and inexpensive fabrication method for efficient, skin-friendly, and highly stretchable biomechanical piezoelectric sensors is also proposed. In this regard, multiwall carbon nanotubes/silicone composite stretchable electrodes were prepared to be compatible with the sensors. The electrodes displayed stability even under high uniaxial elongation (100%), and the fabricated sensors responded effectively to almost every joint movement. The results suggested that the fabricated PENGs can be potentially used as self-powered biomechanical energy harvesters/sensors in wearable electronics, haptic sensing, or internet of human-related applications.

Kinematic dataset of actors expressing emotions

Human body movements can convey a variety of emotions and even create advantages in some special life situations. However, how emotion is encoded in body movements has remained unclear. One reason is that there is a lack of public human body kinematic dataset regarding the expressing of various emotions. Therefore, we aimed to produce a comprehensive dataset to assist in recognizing cues from all parts of the body that indicate six basic emotions (happiness, sadness, anger, fear, disgust, surprise) and neutral expression. The present dataset was created using a portable wireless motion capture system. Twenty-two semi-professional actors (half male) completed performances according to the standardized guidance and preferred daily events. A total of 1402 recordings at 125 Hz were collected, consisting of the position and rotation data of 72 anatomical nodes. To our knowledge, this is now the largest emotional kinematic dataset of the human body. We hope this dataset will contribute to multiple fields of research and practice, including social neuroscience, psychiatry, computer vision, and biometric and information forensics.

Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics

Manual labor is still strongly present in many industrial contexts (such as aerospace industry). Such operations commonly involve onerous tasks requiring to work in non-ergonomic conditions and to manipulate heavy parts. As a result, work-related musculoskeletal disorders are a major problem to tackle in workplace. In particular, back is one of the most affected regions. To solve such issue, many efforts have been made in the design and control of exoskeleton devices, relieving the human from the task load. Besides upper limbs and lower limbs exoskeletons, back-support exoskeletons have been also investigated, proposing both passive and active solutions. While passive solutions cannot empower the human's capabilities, common active devices are rigid, without the possibility to track the human's spine kinematics while executing the task. The here proposed paper describes a methodology to design an active back-support exoskeleton with backbone-based kinematics. On the basis of the (easily implementable) scissor hinge mechanism, a one-degree of freedom device has been designed. In particular, the resulting device allows tracking the motion of a reference vertebra, i.e., the vertebrae in the correspondence of the connection between the scissor hinge mechanism and the back of the operator. Therefore, the proposed device is capable to adapt to the human posture, guaranteeing the support while relieving the person from the task load. In addition, the proposed mechanism can be easily optimized and realized for different subjects, involving a subject-based design procedure, making possible to adapt its kinematics to track the spine motion of the specific user. A prototype of the proposed device has been 3D-printed to show the achieved kinematics. Preliminary tests for discomfort evaluation show the potential of the proposed methodology, foreseeing extensive subjects-based optimization, realization and testing of the device.

Kinematic skeleton graph augmented network for human parsing

Human parsing, which is a task of labeling pixels in human images into different fine-grained semantic parts, has achieved significant progress during the past decade. However, there are still several challenges in human parsing, due to occlusions, varying poses and similar appearance between the left/right parts. To tackle these problems, a Human Kinematic Skeleton Graph Layer (HKSGL) is proposed to augment regular neural networks with human kinematic skeleton information. The HKSGL has two major components: kinematic skeleton graph and interconnected modular neural layer. The kinematic skeleton graph is a user pre-defined skeleton graph, which models the interconnections between different semantic parts. Then the skeleton graph is passed to the interconnected modular neural layer which is composed of a set of modular blocks corresponding to different semantic parts. The HKSGL is a lightweight, low costs layer which can be easily attached to any existing neural networks. To demonstrate the power of the HKSGL, a new dataset on human parsing in occlusions is also collected, termed the RAP-Occ. Extensive experiments have been performed on four datasets on human parsing, including the LIP, the CIHP, the ATR and the RAP-Occ. And two popular baselines, i.e., the Deeplab V3+ and the CE2P, are agumented by the proposed HKSGL. Competitive performance of the augmented models has been achieved in comparison with state-of-the-art methods.

Wearable Shoulder Exoskeleton with Spring-Cam Mechanism for Customizable, Nonlinear Gravity Compensation.

Wearable, mechanically passive (i.e. spring-powered) exoskeletons may be more practical and affordable than active, motorized exoskeletons for providing continuous, home-based, antigravity movement assistance for people with shoulder disability. However, the biomechanical moment due to gravity is a nonlinear function of shoulder elevation angle and, thus, challenging to counteract proportionally across the shoulder elevation range of motion with a spring alone. We designed, fabricated, and tested an integrated spring-cam-wheel system that can generate a nonlinear moment to proportionally compensate for the expected antigravity moment at the shoulder. We then incorporated the proposed system in a benchtop model and a novel wearable passive cable-driven exoskeleton that was intended to counteract half of the gravitational moment during shoulder elevation movements. The rotational moment measured from the benchtop model closely matched the theoretical moment during simulated positive shoulder elevation. However, a larger moment (up to 12.5% larger) was required during simulated negative shoulder elevation to stretch the spring to its initial length due to spring hysteresis and friction losses. The wearable exoskeleton prototype was qualitatively tested for assisting shoulder elevation movements; we identified several aspects of the prototype design that need to be improved before further testing on human participants. In future studies, we will quantitatively evaluate human kinematics and neuromuscular coordination with the exoskeleton to determine its suitability for assisting patients with shoulder disability.

Deltoid muscle contribution to shoulder flexion and abduction strength: an experimental approach

The rotator cuff (RC) and the deltoid muscle are 2 synergistic units that enable the functionally demanding movements of the shoulder. A number of biomechanical studies assume similar force contribution of the force couple (RC and deltoid) over the whole range of motion, whereas others propose position-dependent force distribution. There is a lack of invivo data regarding the deltoid's contribution to shoulder flexion and abduction strength. This studyaimed to create reliable invivo dataquantifying the deltoid's contribution to shoulder flexion and abduction strength throughout the range of motion. Active range of motion and isometric muscle strength of shoulder abduction and flexion in 0°, 30°, 60°, 90°, and 120° of abduction/flexion as well as internal and external rotation in 0° and 90° of abduction were obtained in 12 healthy volunteers on the dominant arm before and after an ultrasound-guided isolated axillary nerve block. Needle electromyography was performed before and after the block to confirm deltoid paralysis. Radiographs of the shoulder and an ultrasonographic examination were used to exclude relevant shoulder pathologies. Active range of motion showed a minimal to moderate reduction to 94% and 88% of the preintervention value for abduction and flexion. Internal and external rotation amplitude was not impaired. The abduction strength was significantly reduced to 76% at 0° (P = .002) and to 25% at 120° (P < .001) of abduction. The flexion strength was significantly reduced to 64% at 30° (P < .001) and to 30% at 120° (P < .001) of flexion. The strength reduction was linear, depending on the flexion/abduction angle. The maximal external rotation strength showed a significant decrease to 53% in 90° (P < .001) of abduction, whereas in adduction no strength loss was observed (P = .09). The internal rotation strength remained unaffected in 0° and 90° of abduction (P = .28; P = .13). The deltoid shows a linear contribution to maximal shoulder strength depending on the abduction or flexion angle, ranging from 24% in 0° to 75% in 120° of abduction and from 11% in 0° to 70% in 120° of flexion, respectively. The overall contribution to abduction strength is higher than to flexion strength. The combination of deltoid muscle and teres minor contributes about 50% to external rotation strength in 90° of abduction. The internal rotation strength is not influenced by a deltoid paralysis. This study highlights the position-dependent contribution of the shoulder muscles to strength development and thereby provides an empirical approach to better understand human shoulder kinematics.

Sensor-to-Segment Calibration Methodologies for Lower-Body Kinematic Analysis with Inertial Sensors: A Systematic Review.

Kinematic analysis is indispensable to understanding and characterizing human locomotion. Thanks to the development of inertial sensors based on microelectronics systems, human kinematic analysis in an ecological environment is made possible. An important issue in human kinematic analyses with inertial sensors is the necessity of defining the orientation of the inertial sensor coordinate system relative to its underlying segment coordinate system, which is referred to sensor-to-segment calibration. Over the last decade, we have seen an increase of proposals for this purpose. The aim of this review is to highlight the different proposals made for lower-body segments. Three different databases were screened: PubMed, Science Direct and IEEE Xplore. One reviewer performed the selection of the different studies and data extraction. Fifty-five studies were included. Four different types of calibration method could be identified in the articles: the manual, static, functional, and anatomical methods. The mathematical approach to obtain the segment axis and the calibration evaluation were extracted from the selected articles. Given the number of propositions and the diversity of references used to evaluate the methods, it is difficult today to form a conclusion about the most suitable. To conclude, comparative studies are required to validate calibration methods in different circumstances.