Force-based control for human-robot cooperative object manipulation

In Physical Human-Robot Interaction (PHRI), humans and robots share the workspace and physically interact and collaborate to perform a common task. However, robots do not have human levels of intelligence or the capacity to adapt in performing collaborative tasks. Moreover, the presence of humans in the vicinity of the robot requires ensuring their safety, both in terms of software and hardware. One of the aspects related to safety is the stability of the human-robot control system, which can be placed in jeopardy due to several factors such as internal time delays. Another aspect is the mutual understanding between humans and robots to prevent conflicts in performing a task. The kinesthetic transmission of the human intention is, in general, ambiguous when an object is involved, and the robot cannot distinguish the human intention to rotate from the intention to translate (the translation/rotation problem).This thesis examines the aforementioned issues related to PHRI. First, the instability arising due to a time delay is addressed. For this purpose, the time delay in the system is modeled with the exponential function, and the effect of system parameters on the stability of the interaction is examined analytically. The proposed method is compared with the state-of-the-art criteria used to study the stability of PHRI systems with similar setups and high human stiffness. Second, the unknown human grasp position is estimated by exploiting the interaction forces measured by a force/torque sensor at the robot end effector. To address cases where the human interaction torque is non-zero, the unknown parameter vector is augmented to include the human-applied torque. The proposed method is also compared via experimental studies with the conventional method, which assumes a contact point (i.e., that human torque is equal to zero). Finally, the translation/rotation problem in shared object manipulation is tackled by proposing and developing a new control scheme based on the identification of the ongoing task and the adaptation of the robot\u27s role, i.e., whether it is a passive follower or an active assistant. This scheme allows the human to transport the object independently in all degrees of freedom and also reduces human effort, which is an important factor in PHRI, especially for repetitive tasks. Simulation and experimental results clearly demonstrate that the force required to be applied by the human is significantly reduced once the task is identified

Force-based Perception and Control Strategies for Human-Robot Shared Object Manipulation

Physical Human-Robot Interaction (PHRI) is essential for the future integration of robots in human-centered environments. In these settings, robots are expected to share the same workspace, interact physically, and collaborate with humans to achieve a common task. One of the primary tasks that require human-robot collaboration is object manipulation. The main challenges that need to be addressed to achieve a seamless cooperative object manipulation are related to uncertainties in human trajectory, grasp position, and intention. The object’s motion trajectory intended by the human is not always defined for the robot and the human may grasp any part of the object depending on the desired trajectory. In addition, the state-of-the-art object-manipulation control schemes suffer from the translation/rotation problem, where the human cannot move the object in all degrees of freedom, independently, and thus, needs to exert extra effort to accomplish the task. To address the challenges, first, we propose an estimation method for identifying the human grasp position. We extend the conventional contact point estimation method by formulating a new identification model with the human applied torque as an unknown parameter and employing empirical conditions to estimate the human grasp position. The proposed method is compared with a conventional contact point estimation using the experimental data collected for various collaboration scenarios. Second, given the human grasp position, a control strategy is suggested to transport the object in all degrees of freedom, independently. We employ the concept of “the instantaneous center of zero velocity” to reduce the human effort by minimizing the exerted human force. The stability of the interaction is evaluated using a passivity-based analysis of the closed-loop system, including the object and the robotic manipulator. The performance of the proposed control scheme is validated through simulation of scenarios containing rotations and translations of the object. Our study indicates that the exerted torque of the human has a significant effect on the human grasp position estimation. Besides, the knowledge of the human grasp position can be used in the control scheme design to avoid the translation/rotation problem and reduce the human effort

An integrated computer-based system to study neuromuscular disorders of the upper limb

A multi-channel computer-based clinical instrument was developed to simultaneously acquire, process, display, quantify and correlate electromyographic (EMG) activity, resistive torque, range of motion (ROM), and pain levels in the upper limbs of humans. Each channel consisted of a time and frequency domain block, a torque and angle measurement block, an experiment number counter block and a data storage and retrieval block. The study showed that there was increased level of EMG activity prior to pain onset (P<0.05). There was also clear evidence that elevated perception of pain and elevated levels of resistive torque (P<0.05) were positively correlated with the EMG activity in the muscles responsible for antalgic posture of the upper limb (P<0.05)

Computer-based clinical instrumentation for processing and analysis of mechanically evoked electromyographic signals in the upper limb

A computer-based clinical instrument was developed to simultaneously acquire, process, display, quantify and correlate electromyographic (EMG) activity, resistive torque, range of motion (ROM), and pain responses evoked by mechanical stimuli (i.e. passive elbow extensions) in humans. This integrated multichannel system was designed around AMLAB® analog modules and software objects called ICAMs. Although this system was designed to specifically study the patterns and nature of evoked motor responses in Carpal Tunnel Syndrome (CTS) patients, it could equally well be modified to allow acquisition, processing and analysis of EMG signals in other studies and applications. In this paper, we describe an integrated system to simultaneously study and analyze the mechanically evoked electromyographic, torque and ROM signals and correlate various levels of pain to these signals

Does the longer application of anodal-transcranial direct current stimulaton increase corticomotor excitability further? A pilot study

Introduction: Anodal transcranial direct current stimulation (a-tDCS) of the primary motor cortex (M1) has been shown to be effective in increasing corticomotor excitability.　Methods: We investigated whether longer applications of a-tDCS coincide with greater increases in corticomotor excitability compared to shorter application of a-tDCS. Ten right-handed healthy participants received one session of a-tDCS (1mA current) with shorter (10 min) and longer (10+10 min) stimulation durations applied to the left M1 of extensor carpi radialis muscle (ECR). Corticomotor excitability following application of a-tDCS was assessed at rest with transcranial magnetic stimulation (TMS) elicited motor evoked potentials (MEP) and compared with baseline data for each participant.　Results: MEP amplitudes were increased following 10 min of a-tDCS by 67% (p = 0.001) with a further increase (32%) after the second 10 min of a-tDCS (p = 0.005). MEP amplitudes remained elevated at 15 min post stimulation compared to baseline values by 65% (p = 0.02).　Discussion: The results demonstrate that longer application of a-tDCS within the recommended safety limits, increases corticomotor excitability with after effects of up to 15 minutes post stimulation.<br /

Computer-based clinical instrumentation for processing and analysis of electroneuromyographic signals in the upper limb

A computer-based clinical instrument was developed to simultaneously acquire, process, display, quantify and correlate electroneuromyographic (ENMG) activity in the upper limb in humans. This system was designed around AMLAB® analog modules and software objects called ICAMs. The system consists of a nerve stimulator block, a time domain, EMG block with evoked response averaging capability, a counter block and a data storage and retrieval block. This system has been designed to study the H-reflex and M-response in the upper limb of normal subjects and Carpal Tunnel Syndrome (CTS) patients. It could be easily modified to acquire, process and analyze the ENMG signals in other parts of the human body to assess the continuity and function of the sensory and motor pathways. In this paper, we present an integrated system to simultaneously measure and analyze the electroneuromyographic activities in the upper limb

What is the Effect of Motor Level Peripheral Electrical Stimulation on Corticospinal Excitability and Functional Outcome Measures in Both Healthy Participants and those with Neurological Disorders? A Systematic Review and Meta-Analysis

Introduction: To explore the effect of Motor Level peripheral Stimulation (MLS) on Corticospinal Excitability (CSE) in healthy participants and those with neurological disorders, and to establish stimulation parameters best suited to this purpose. Methods and Materials: A comprehensive search strategy was developed for identification of papers answering the review question. The studies identified were used to do meta-analyses. Results: Following motor-level stimulation, there was a significant change in CSE from baseline: 57.66% (95% CI). Subgroup analysis showed that there was a significant change in the 100Hz subgroup: 68.31% (95% CI) and the 20-50Hz subgroup: 80.14% (95% CI), but not in the &lt;10Hz subgroup: 9.97% (95% CI). In addition, CSE changes was greater where intervention time = 30mins: 83.19% (95% CI), then where intervention time &gt;30mins: 53.14% (95% CI). CSE showed no significant changes following ‘no stimulation”: 69.61% (95% CI). Conclusions: The findings indicate that MLS leads to increases in CSE; however, magnitude of change depends on the stimulation frequency and the area stimulated. It also appears that stimulation durations of longer than 30mins do not result in greater changes. Significance: The present review article hopes to catalyze further research into the determination of appropriate MLS treatment parameters for specific muscle groups.Key words: Motor level stimulation, corticospinal excitability, functional electrical stimulation, associative stimulation, transcranial magnetic stimulation, motor evoked potential

Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems.Comment: To appear in 2023 IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON

Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior

With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address these challenges, this paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts to facilitate secure and mutually beneficial data sharing while providing incentives, access control mechanisms, and penalizing any dishonest behavior. The experimental results demonstrate that the proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized architecture and use of blockchain technology allow for efficient communication and coordination between workers. This platform has the potential to facilitate decentralized machine learning and support privacy-preserving collaboration in various domains.Comment: To appear in the 5th International Congress on Blockchain and Applications (BLOCKCHAIN'23). Publish by the Lecture Notes in Networks and Systems series of Springer Verla