相关论文: ProRuka: A highly efficient HMI algorithm for cont…
Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains…
Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that…
Objective: The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand…
EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability…
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and…
Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are…
The design and evaluation of a robotic prosthesis for a drummer with a transradial amputation is presented. The principal objective of the prosthesis is to simulate the role fingers play in drumming. This primarily includes controlling the…
Hand pose estimation is more challenging than body pose estimation due to severe articulation, self-occlusion and high dexterity of the hand. Current approaches often rely on a popular body pose algorithm, such as the Convolutional Pose…
This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of…
EMG-based hand gesture recognition uses electromyographic~(EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control,…
Surface electromyography (sEMG) is a promising control signal for assist-as-needed hand rehabilitation after stroke, but detecting intent from paretic muscles often requires lengthy, subject-specific calibration and remains brittle to…
Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but they are inadequate to operate the prosthesis for daily activities that are non-cyclic and unpredictable. To address this challenge, this…
We present a system for decoding hand movements using surface EMG signals. The interface provides real-time (25 Hz) reconstruction of finger joint angles across 20 degrees of freedom, designed for upper limb amputees. Our offline analysis…
Stiffness estimation is crucial for delicate object manipulation in robotic and prosthetic hands but remains challenging due to dependence on force and displacement measurement and real-time sensory integration. This study presents a…
Electromyogram (EMG) signals recorded from the skin surface enable intuitive control of assistive devices such as prosthetic limbs. However, in EMG-based motion recognition, collecting comprehensive training data for all target motions…
The lack of haptically aware upper-limb prostheses forces amputees to rely largely on visual cues to complete activities of daily living. In contrast, able-bodied individuals inherently rely on conscious haptic perception and automatic…
Any part of the human body replication procedure commences the prosthetic control science. This paper highlights the hardware design technique of a prosthetic arm with implementation of gear motor control aspect. The prosthetic control arm…
Myoelectric control is an area of electromyography of increasing interest nowadays, particularly in applications such as Hand Gesture Recognition (HGR) for bionic prostheses. Today's focus is on pattern recognition using Machine Learning…
This paper presents a control interface to translate the residual body motions of individuals living with severe disabilities, into control commands for body-machine interaction. A custom, wireless, wearable multi-sensor network is used to…
Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities. Robotic rehabilitation provides tools to restore hand function, while novel methods based on surface electromyography (sEMG)…