18 accepted papers
Surface Electromyography (sEMG) is a non-invasive signal that is used in the recognition of hand movement patterns, the diagnosis of diseases, and the robust control of prostheses. Despite the remarkable success of recent end-to-end Deep Learning approaches, they are still limited by the need for la…
Utilizing the intrinsic capability of the human upper limb to absorb energy during kinesthetic human-robot interaction could allow for improved haptic feedback fidelity and reduce the conservatism of control in pHRI and telerobotic systems. However, estimating this energetic signature is complex. In
Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neu…
The human arm has an intrinsic capacity to absorb energy during physical human-robot interaction (pHRI), which can be identified as biomechanical excess of passivity (EoP). This can be used as a central factor in the development of passivity-based pHRI controllers securing haptic transparency while
Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, Deepnets are generally challenged by long training times (affecting practicality and calibration), complex
Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN models have shown promising results with respect…
There has been an accelerated surge in utilizing the deep neural network to decode central and peripheral activations of the human nervous system to boost the spatiotemporal resolution of neural interfaces used in human-centered robotic systems, such as prosthetics, and exoskeletons. Deep learning m
Soft robots have shown great potential for manufacturing exoskeletons, prostheses, and surgical robots. In this paper, we propose the concept of programmable soft robotics and will experimentally evaluate the performance in the context of continuum mechanisms. The proposed novel concept is motivated
In this letter, we propose a novel adaptive iterative stabilization method for physical human-(tele)robot interaction, named Discrete Windowed-Energy Variable Structure Passivity Signature Control (DWE-VSPSC). The proposed stabilizer is capable of adaptively translating the knowledge domain regardin
This work is motivated by the recent advancements of Deep Neural Networks (DNNs) for myoelectric prosthesis control. In this regard, hand gesture recognition via surface Electromyogram (sEMG) signals has shown a high potential for improving the performance of myoelectric control prostheses. Although…
The spectrotemporal information content of surface electromyography has shown strong potential in predicting the intended motor command. During the last decade, with accelerated exploitation of powerful deep-learning techniques aligned with advancements in active prostheses and neurorobots, a great
This letter investigates the feasibility of a generalizable solution for human-robot interfaces through peripheral multichannel Electromyography (EMG) recording. We propose a tangential approach in comparison to the literature to minimize the need for (re)calibration of the system for new users. The
The capacity of the biomechanics of human limbs to absorb energy during physical human-robot interaction (pHRI) can play an imperative role in controlling the performance of human-centered robotics systems. Using the concept of “excess of passivity,” we have recently designed passivity signature map
This study introduces a haptic rendering algorithm for simulating surgical bone machining operations. The proposed algorithm is a new variant of the voxmap point-shell method, where the bone and surgical tool geometries are represented by voxels and points, respectively. The algorithm encompasses co
Capitalizing on the goal of addressing identified shortcomings of recent solutions developed for recognition tasks via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative Xc…
This paper presents a novel light-weight back-drivable inherently-safe robotic mechanism for delivering ankle rehabilitation therapies. The robot is designed to be used as the ankle module of a multi-purpose lower-limb rehabilitation robot. A novel friction-based safety feature has been introduced t…
Pathological hand tremor (PHT) is among the most common movement symptoms of several neurological disorders including Parkinson's disease and essential tremor. Extracting PHT is of paramount importance in several engineering and clinical applications such as assistive and robotic rehabilitation tech
Tremor extraction techniques are considered as the central component of several rehabilitative and compensatory robotic technologies, and the accuracy of such filters can directly affect the performance of the aforementioned technologies. Motivated by this fact, the paper proposes an adaptive estima…