Arm Stiffness Research Articles

Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

O19: FROZEN GEL-PACKS REDUCE POST-OPERATIVE PAIN AND NARCOTIC USAGE IN PATIENTS UNDERGOING SURGERY FOR BREAST CANCER

Abstract Introduction Adequate post-operative analgesia is important for recovery, early rehabilitation and patient satisfaction. We aimed to prove that patients undergoing surgery for breast cancer who receive cryotherapy via frozen gel-packs in the immediate postoperative period would report lower pain, require lesser narcotics and pain medication. Method This prospective randomized study was conducted at our tertiary care referral centre in South India. The pre-operative characteristics, intra-operative variables and post-operative outcomes were compared between two sets of matched cases and controls. Cryotherapy was delivered via frozen gel-packs for 12 hours immediately following surgery. Pain relief was assessed with the visual analog pain scale(VAS). Comparisons between groups were measured by the chi-square test, Fischer's-exact test, Independent samples t-test or Mann-Whitney U test as appropriate. Result 81 patients were included in the study( Study-group=41 and control-group=40). For the primary outcome, mean postoperative pain score on postoperative days(PODs) 1(5.13±0.648 vs. 4.44±.502; t (79) = 5.3; p<0.001) and POD 5(4.1±0.9 vs. 3.29±.642; t (79)=3.26; p<0.001) were significantly lower in the cryotherapy group. The overall post-operative narcotic requirement(morphine equivalence) was reduced in the cryotherapy group(61.32±9.4 vs 55.8±11; t(79) = 2.421; p=0.018). The overall paracetamol requirement(grams) was also reduced in the cryotherapy group(2.1±.38 vs 1.9±.36; t(79) = 2.66; p=0.009). No significant difference was seen in the NSAID use between the groups. The cryotherapy group also demonstrated earlier functional recovery and lesser shoulder and arm stiffness(p=0.015). Conclusion Cryotherapy is a simple, cost-effective adjuvant to standard postoperative pain management which reduces pain, narcotics and promotes early rehabilitation. Take-home message Ice packs are a simple, cost-effective adjuvant to standard postoperative pain management which reduces pain, narcotic use and promotes early rehabilitation.

Design, Modeling, and Manufacturing of a Variable Lateral Stiffness Arm Via Shape Morphing Mechanisms

Abstract In this article, we present a continuously tunable stiffness arm for safe physical human–robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human–robot interactions via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/actuators, variable stiffness links for safe physical human–robot interactions are much less studied. This article details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D-printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26% given a morphing angle of 90 deg. The variable stiffness arm design developed in this study could be a promising approach to address safety concerns for safe physical human–robot interactions.

Musculoskeletal Driver Model for the Steering Feedback Controller

This paper aims to find a mathematical justification for the non-linear steady state steering haptic response as a function of driver arm posture. Experiments show that different arm postures, that is, same hands location on the steering wheel but at different initial steering angles, result in a change in maximum driver arm stiffness. This implies the need for different steering torque response as a function of steering angle, which is under investigation. A quasi-static musculoskeletal driver model considering elbow and shoulder joints is developed for posture analysis. The torque acting in the shoulder joint is higher than in the elbow. The relationship between the joint torque and joint angle is linear in the shoulder, whereas the non-linearity occurs in the elbow joint. The simulation results qualitatively indicate a similar pattern as compared to the experimental muscle activity results. Due to increasing muscle non-linearity at high steering angles, the arm stiffness decreases and then the hypothesis suggests that the effective steering stiffness is intentionally reduced for a consistent on-center haptic response.

Patient-reported shoulder morbidity and fatigue among the breast cancer survivors: An insight from a Tertiary Care Cancer Hospital

Context: Breast cancer is the most common cancer in Indian women with an annual mortality of around 87,000. Treatment for breast carcinoma may lead to swelling of the ipsilateral arm and shoulder stiffness and arm pain and cancer-related fatigue. Very few centers in India have reported the arm and shoulder morbidity treated in their hospital. Aims: The study aims to evaluate the predictive factors of arm and shoulder morbidity and fatigue among the survivors. Settings and Design: Retrospective analysis from a prospectively maintained database. Materials and Methods: Early and locally advanced cases of breast cancer patients were screened for the study during 2015–2018. Eligible participants were invited to fill up the predetermined questionnaire and their demographic and treatment-related information was accrued from a file archive. Follow-up period was estimated from the date of tissue diagnosis to last contact/time of interview. Statistical Analysis Used: Predictive factors of shoulder morbidity and fatigue were analyzed by independent sample t-test and univariate analysis. Significant predictive factors were selected for further validation in the multivariate cox regression model. A P ≤ 0.05 was considered significant. Results: Shoulder stiffness was the most common complaint followed by arm numbness. Obesity and diabetes played a crucial role in most of the morbidities and fatigue. The median fatigue score was 34 and the median time of appearance of lymphedema was 13 months. Modified radical mastectomy, and radiotherapy to axilla were significantly associated with greater shoulder stiffness and arm swelling. Conclusions: Obesity, diabetes, type of surgery, the extent of axillary dissection, and radiation plan are the major predictive factors of arm and shoulder morbidity. Further prospective validation is necessary for future breast cancer survivorship programs.

Development of a Tetrahedral-Shaped Soft Robot Arm as a Wrist Rehabilitation Device Using Extension Type Flexible Pneumatic Actuators

Based on aging Japanese society, there has been a strong desire for development of rehabilitation devices that patients can use at home. The purpose of this study is to develop a home-based compact, lightweight, flexible, and safe rehabilitation device. In this study, a flexible robot arm for wrist rehabilitation that can be used, while patients hold the top of the robot arm, is proposed and tested. The proposed robot arm consists of three extension type flexible pneumatic actuators (EFPAs) restrained by 22 PET sheets. To achieve suitable bending stiffness of the robot arm, three EFPAs are restrained so as to form a tetrahedral shape. The robot arm can bend toward each radial direction. In this paper, the construction and operational principles of the tested robot arm are described. Additionally, the analytical model of the robot arm for attitude control is also described. In addition, the tracking control using the robot arm for the desired orbit is performed. As a result, the tested robot arm can trace the desired orbit based on the model. It is confirmed that the robot arm has the possibility for application as a wrist rehabilitation device for patients.

Online Process Monitoring Based on Vibration-Surface Quality Map for Robotic Grinding

In robotic manufacturing, the machining vibration can easily affect the quality of the processed surfaces due to poor stiffness and variable stiffness of the robot arm. It is important to monitor the processing quality. Therefore, an online process monitor method for robotic grinding is proposed in this article. Since the surface quality is difficult to be measured directly online, a vibration-surface quality map is proposed to show the mapping relationship between the vibration signals and the surface images. Based on this map, the grinding states are classified to lack, normal, and overgrinding, which formed the training set data. Then a convolutional neural network classifier is trained to detect the grinding states during robotic machining only with the vibration signals. To establish the vibration-surface quality map, the symmetrized dot pattern and sparse autoencoder methods are utilized to extract the features from the raw vibrations and images data, and a gray-level co-occurrence matrix index is further used to build the bridge between them. The experimental results in this article have proved the effectiveness and potential of our method for online robotic grinding quality monitoring.

Comparing model-based control methods for simultaneous stiffness and position control of inflatable soft robots

Inflatable robots are naturally lightweight and compliant, which may make them well suited for operating in unstructured environments or in close proximity to people. The inflatable joints used in this article consist of a strong fabric exterior that constrains two opposing compliant air bladders that generate torque (unlike McKibben actuators where pressure changes cause translation). This antagonistic structure allows the simultaneous control of position and stiffness. However, dynamic models of soft robots that allow variable stiffness control have not been well developed. In this work, a model that includes stiffness as a state variable is developed and validated. Using the stiffness model, a sliding mode controller and model predictive controller are developed to control stiffness and position simultaneously. For sliding mode control (SMC), the joint stiffness was controlled to within 0.07 Nm/rad of a 45 Nm/rad command. For model predictive control (MPC) the joint stiffness was controlled to within 0.045 Nm/rad of the same stiffness command. Both SMC and MPC were able to control to within 0.5° of a desired position at steady state. Stiffness control was extended to a multiple-degree-of-freedom soft robot using MPC. Controlling stiffness of a 4-DOF arm reduced the end-effector deflection by approximately 50% (from 17.9 to 12.2cm) with a 4 lb (1.8 kg) step input applied at the end effector when higher joint stiffness (40 Nm/rad) was used compared with low stiffness (30 Nm/rad). This work shows that the derived stiffness model can enable effective position and stiffness control.

A cortical bone milling force model based on orthogonal cutting distribution method

In orthopedic surgery, the bone milling force has attracted attention owing to its significant influence on bone cracks and the breaking of tools. It is necessary to build a milling force model to improve the process of bone milling. This paper proposes a cortical bone milling force model based on the orthogonal cutting distribution method (OCDM), explaining the effect of anisotropic bone materials on milling force. According to the model, the bone milling force could be represented by the equivalent effect of a transient cutting force in a rotating period, and the transient milling force could be calculated by the transient milling force coefficients, cutting thickness, and cutting width. Based on the OCDM, the change in transient cutting force coefficients during slotting can be described by using a quadratic polynomial. Subsequently, the force model is updated for robotic bone milling, considering the low stiffness of the robot arm. Next, an experimental platform for robotic bone milling is built to simulate the milling process in clinical operation, and the machining signal is employed to calculate the milling force. Finally, according to the experimental result, the rationality of the force model is verified by the contrast between the measured and predicted forces. The milling force model can satisfy the accuracy requirement for predicting the milling force in the different processing directions, and it could promote the development of force control in orthopedic surgery.

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this article, we develop such a physical human-robot interaction system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model and the Gaussian mixture regression. More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach.

Reward-Based Improvements in Motor Control Are Driven by Multiple Error-Reducing Mechanisms.

Reward has a remarkable ability to invigorate motor behavior, enabling individuals to select and execute actions with greater precision and speed. However, if reward is to be exploited in applied settings, such as rehabilitation, a thorough understanding of its underlying mechanisms is required. In a series of experiments, we first demonstrate that reward simultaneously improves the selection and execution components of a reaching movement. Specifically, reward promoted the selection of the correct action in the presence of distractors, while also improving execution through increased speed and maintenance of accuracy. These results led to a shift in the speed-accuracy functions for both selection and execution. In addition, punishment had a similar impact on action selection and execution, although it enhanced execution performance across all trials within a block, that is, its impact was noncontingent to trial value. Although the reward-driven enhancement of movement execution has been proposed to occur through enhanced feedback control, an untested possibility is that it is also driven by increased arm stiffness, an energy-consuming process that enhances limb stability. Computational analysis revealed that reward led to both an increase in feedback correction in the middle of the movement and a reduction in motor noise near the target. In line with our hypothesis, we provide novel evidence that this noise reduction is driven by a reward-dependent increase in arm stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate motor performance without compromising accuracy.SIGNIFICANCE STATEMENT While reward is well-known for enhancing motor performance, how the nervous system generates these improvements is unclear. Despite recent work indicating that reward leads to enhanced feedback control, an untested possibility is that it also increases arm stiffness. We demonstrate that reward simultaneously improves the selection and execution components of a reaching movement. Furthermore, we show that punishment has a similar positive impact on performance. Importantly, by combining computational and biomechanical approaches, we show that reward leads to both improved feedback correction and an increase in stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate performance without compromising accuracy. This work suggests that stiffness control plays a vital, and underappreciated, role in the reward-based imporvemenets in motor control.

On the role of stiffness and synchronization in human–robot handshaking

This article presents a system for soft human–robot handshaking, using a soft robot hand in conjunction with a lightweight and impedance-controlled robot arm. Using this system, we study how different factors influence the perceived naturalness, and give the robot different personality traits. Capitalizing on recent findings regarding handshake grasp force regulation, and on studies of the impedance control of the human arm, we investigate the role of arm stiffness as well as the kinesthetic synchronization of human and robot arm motions during the handshake. The system is implemented using a lightweight anthropomorphic arm, with a Pisa/IIT Softhand wearing a sensorized silicone glove as the end-effector. The robotic arm is impedance-controlled, and its stiffness changes according to different laws under investigation. An internal observer is employed to synchronize the human and robot arm motions. Thus, we simulate both active and passive behavior of the robotic arm during the interaction. Using the system, studies are conducted where 20 participants are asked to interact with the robot, and then rate the perceived quality of the interaction using Likert scales. Our results show that the control of the robotic arm kinesthetic behavior does have an effect on the interaction with the robot, in term of its perceived personality traits, responsiveness, and human-likeness. Our results pave the way towards robotic systems that are capable of performing human–robot interactions in a more human-like manner, and with personality.

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centered design philosophy are highly desired in today's robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase the usability and acceptability of robots by users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm and contact force with the environment also play important roles in understanding and generating human-like manipulation behaviors for robots, e.g., in physical HRI and teleoperation. To this end, we present a novel robot learning framework based on dynamic movement primitives (DMPs), taking into consideration both the positional and contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table.

Endpoint stiffness magnitude increases linearly with a stronger power grasp

Humans can increase the endpoint stiffness of their arm to reduce self-generated movement variability and to reject unpredictable perturbations from the environment, like during handheld drilling, thereby increasing movement precision. Existing methods to estimate changes in the endpoint stiffness use robotic interfaces to apply position or force perturbations to measure the arm’s dynamic response. We propose an alternative method of measuring changes in the power grasp force to estimate adaptations in the magnitude of the arm’s endpoint stiffness. To validate our method, we examined how the strength of the power grasp, when holding onto a robotic manipulandum, affected the arm’s endpoint stiffness in three different locations of the workspace. The endpoint stiffness magnitude increased linearly with the grasp force, and this linear relationship did not depend on the arm’s posture or position in the workspace. The endpoint stiffness may have increased as a combination of greater grasp stiffness and greater arm stiffness, since larger co-contraction was observed in the elbow and shoulder with a stronger grasp. Changes in the grasp force could serve as a metric in assessing how humans adapt their endpoint stiffness magnitude.

A Robot-Assisted Spine Surgery System Based on Intraoperative 2D Fluoroscopy Navigation

In this paper, a robotic system based on intraoperative 2D fluoroscopy navigation in which fluoroscopy images are obtained by C-arm popularized in the clinic, for spine surgery is proposed. The procedure of robot-assisted spine surgery is designed and described. An improved special 6-dof hybrid robot considering both the stiffness and workspace is proposed. And the kinematic model and jacobian matrix of robot are established. To realize fluroscopy images acquisition and avoid interfering with the patient during the process of positioning, human-robot cooperation based on fuzzy variable admittance control considering arm stiffness which would influence the stability of robot, is applied in robotic system. In addition, navigation techniques containing a novel bi-planar design, a robust circular control point detector and closed-loop positioning of tool, based on fluoroscopy images are proposed. In experiments, kinematic model of hybrid robot is test based on laser tracker and the distance error is 0.17 mm which is much smaller than the motion range. The effect of tool weight compensation is measured and the max mean force and torque errors are 0.7477N and 0.0617 N.M. The experimental results could meet requirements in human-robot cooperation. Furthermore, we test the the robotic system proposed in this paper on spine model bone and cadaver with the help of surgeons. The experimental results are validated by 2D image c-arm-based and 3D image CT-based and considered clinically acceptable.

Theoretical and Experimental Evaluation on Specific Stiffness of a Integrally-Shaped Lightweight Honeycomb Robotic Arm

Theoretical and Experimental Evaluation on Specific Stiffness of a Integrally-Shaped Lightweight Honeycomb Robotic Arm

Arm Swelling From Subclavian Vein Thrombosis: Factor V Leiden Mutation

Factor V Leiden is a genetic condition that can decrease natural anticoagulant effect in response to activated protein C, increasing thromboembolic risk. We report a 17-year-old man, homozygous for the Factor V Leiden mutation, with sudden onset arm stiffness, pain and swelling caused by a deep vein thrombosis in the subclavian vein. Pediatricians should consider thromboembolic diseases in cases of spontaneous acute limb discomfort and discoloration with no obvious cause. Int J Clin Pediatr. 2020;9(1):1-3 doi: https://doi.org/10.14740/ijcp343

Design and Modeling of a Continuously Tunable Stiffness Arm for Safe Physical Human–Robot Interaction

Abstract To reduce injury in physical human–robot interactions (pHRIs), a common practice is to introduce compliance to joints or arm of a robotic manipulator. In this paper, we present a robotic arm made of parallel guided beams whose stiffness can be continuously tuned by morphing the shape of the cross section through two four-bar linkages actuated by servo motors. An analytical lateral stiffness model is derived based on the pseudo-rigid-body model and validated by experiments. A physical prototype of a three-armed manipulator is built. Extensive stiffness and impact tests are conducted, and the results show that the stiffness of the robotic arm can be changed up to 3.6 times at a morphing angle of 37 deg. At an impact velocity of 2.2 m/s, the peak acceleration has a decrease of 19.4% and a 28.57% reduction of head injury criteria (HIC) when the arm is tuned from the high stiffness mode to the low stiffness mode. These preliminary results demonstrate the feasibility to reduce impact injury by introducing compliance into the robotic link and that the compliant link solution could be an alternative approach for addressing safety concerns of physical human–robot interactions.

Paroxysmal kinesigenic dystonia mimics focal seizure

Dystonia refers to a syndrome of involuntary sustained or spasmodic muscle contractions involving co-contraction of the agonist and the antagonist. Paroxysmal kinesigenic dyskinesia is one of the rare episodic hyperkinetic movement disorders, characterized by brief attacks of abnormal involuntary movements comprising of dystonia, chorea, athetosis, hemiballismus or a combination, usually precipitated by sudden movements. A 16 years old male previously healthy presented with 1 year history of involuntary movement descried as a sudden stiffness of the left arm, sometimes involving the left side of the body after sudden movement of the trunk or while standing up after prolonged physical rest. The spells usually lasted up to 30 to 60 seconds with frequency 3-4/week and were painless. The patient never had a loss of consciousness during spells. He had a normal physical and neurological examination. Brain MRI, routine EEG, laboratory tests including liver functions testes, serum ceruplasmin and copper and 24 hours urine copper were all normal. Simple partial seizure disorder was suspected. As part of workup the patient was admitted in epilepsy monitoring unite and had a long term video EEG. Recoded spells were consistent with a focal dystonic posturing in the left side of the body precipitated by sudden active movement with a normal EEG background, without any epileptiform discharges . Carbamazepine 200 mg /day was initiated as a treatment of kinesigenic dystonia with good clinical response. Paroxysmal kinesigenic dystonia can be frequently misdiagnosed with other medical conditions and required careful clinical assessment and work up.

Haptics Electromyography Perception and Learning Enhanced Intelligence for Teleoperated Robot

Due to the lack of transparent and friendly human-robot interaction (HRI) interface, as well as various uncertainties, it is usually a challenge to remotely manipulate a robot to accomplish a complicated task. To improve the teleoperation performance, we propose a new perception mechanism by integrating a novel learning method to operate the robots in the distance. In order to enhance the perception of the teleoperation system, we utilize a surface electromyogram signal to extract the human operator's muscle activation. As a response to the changes in the external environment, as sensed through haptic and visual feedback, a human operator naturally reacts with various muscle activations. By imitating the human behaviors in task execution, not only motion trajectory but also arm stiffness adjusted by muscle activation, it is expected that the robot would be able to carry out the repetitive tasks autonomously or uncertain tasks with improved intelligence. To this end, we develop a robot learning algorithm based on probability statistics under an integrated framework of the hidden semi-Markov model (HSMM) and the Gaussian mixture method. This method is employed to obtain a generative task model based on the robot's trajectory. Then, Gaussian mixture regression based on HSMM is applied to correct the robot trajectory with the reproduced results from the learned task model. The execution procedures consist of a learning phase and a reproduction phase. To guarantee the stability, immersion, and maneuverability of the teleoperation system, a variable gain control method that involves electromyography (EMG) is introduced. Experimental results have demonstrated the effectiveness of the proposed method.