Abstract Objective Methods for surface electromyographic (EMG) signal decomposition have been developed in the past decade, to extract neural information transferred from the spinal cord to muscles. Here, we characterize the accuracy in the identification of motor unit activities during hand postures from high-density EMG signals and we propose a mapping approach between these neural signals and hand gestures. Methods High-density EMG signals were recorded during 11 hand gesture tasks from 11 ab...
Last.Ning Jiang(UW: University of Waterloo)H-Index: 29
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This study presented a novel framework to improve the robustness of pattern recognition-based myoelectric control algorithms against sensor shift, which was one of the obstacles for its practical applications outside controlled laboratory conditions. Different from the previous proposed methods, which mostly focused on improving the classification performance in the shift condition, this framework provided the functionality of verifying if the sensor position was shifted. If so, the user was ena...
The aim of the study was to apply the real-time surface electromyography (EMG) decomposition to the continuous estimation of grasp kinematics. A real-time decomposition scheme based on the convolutional compensation kernel algorithm was proposed. High-density surface EMG signals and grasp kinematics were recorded concurrently from five able-bodied subject. The electro-mechanical delay between identified motor unit activities and grasp kinematics was characterized and utilized to optimize the mul...
This paper proposes an algorithm for topological simultaneous localization and mapping (SLAM) using multi-hypothesis method. This algorithm focuses on improving on-board computational efficiency and capability of finding out the correct hypothesis as early as possible. In the algorithm, an innovative data structure is applied, in which the edges and vertexes of the topological graph are stored separately. So that detailed information of the vertexes has only one copy in the storage, which also b...
Abstract Practical implementation of myoelectric interfaces have been largely hindered by cumbersome training and retraining procedures required for use across multiple days or for multiple users. We thus present a common spatial-spectral analysis (CSSA) framework to eliminate the need for retraining over multiple days or for multiple users. The CSSA is implemented through spectral decomposition and common modes analysis, maximumly utilizing the common spatial-spectral electromyography (EMG) mod...
This paper represents a first attempt to perform a priori sample size determination from a "historic" Electroencephalography (EEG) dataset. The importance of adequate sample size is firstly highlighted, and evidence is given against the use of normal distribution for such computations, when the data cannot be assumed to be Gaussian. The "historic" dataset is then thoroughly examined to determine the least less likely underlying distribution for the desired phenomenon, in this case the spontaneou...
Offline classification accuracy (CA) is a widely accepted measure to evaluate the performance in pattern recognition based myoelectric scheme. However, whether offline metrics are able to be transferred to evaluate or predict online performance is still unclear. In this study, the relationship between offline metrics and online metrics are analyzed. In offline scenario, global CA is biased, thus class-wise accuracy standard deviation (std) is proposed as a supplement. Target Achievement Control ...
Mobile manipulators are increasingly used for material handling in industrial environment. In order to improve efficiency, coordinated motion of the mobile base and manipulator is necessary. However, reasonable motion planning and accurate sensor feedback are required to get the pose of the target object and compensate positioning error of the mobile base. In this paper, we propose a task-priority coordinated motion planner combined with visual servo for mobile manipulator. The trajectory of the...
Objective : Brain-computer interface (BCI) decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity in stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR) based BCI performance by integrating motor tasks with tactile stimulation. Methods : Eleven stroke patients were recruited for three experimental conditions, i.e., motor attempt (MA) condition, tactile stimulation (TS) conditi...