773 research outputs found
Feature Analysis for Classification of Physical Actions using surface EMG Data
Based on recent health statistics, there are several thousands of people with
limb disability and gait disorders that require a medical assistance. A robot
assisted rehabilitation therapy can help them recover and return to a normal
life. In this scenario, a successful methodology is to use the EMG signal based
information to control the support robotics. For this mechanism to function
properly, the EMG signal from the muscles has to be sensed and then the
biological motor intention has to be decoded and finally the resulting
information has to be communicated to the controller of the robot. An accurate
detection of the motor intention requires a pattern recognition based
categorical identification. Hence in this paper, we propose an improved
classification framework by identification of the relevant features that drive
the pattern recognition algorithm. Major contributions include a set of
modified spectral moment based features and another relevant inter-channel
correlation feature that contribute to an improved classification performance.
Next, we conducted a sensitivity analysis of the classification algorithm to
different EMG channels. Finally, the classifier performance is compared to that
of the other state-of the art algorithm
Quantifying Performance of Bipedal Standing with Multi-channel EMG
Spinal cord stimulation has enabled humans with motor complete spinal cord
injury (SCI) to independently stand and recover some lost autonomic function.
Quantifying the quality of bipedal standing under spinal stimulation is
important for spinal rehabilitation therapies and for new strategies that seek
to combine spinal stimulation and rehabilitative robots (such as exoskeletons)
in real time feedback. To study the potential for automated electromyography
(EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients
undergoing electrical spinal cord stimulation using both video and
multi-channel surface EMG recordings during spinal stimulation therapy
sessions. The quality of standing under different stimulation settings was
quantified manually by experienced clinicians. By correlating features of the
recorded EMG activity with the expert evaluations, we show that multi-channel
EMG recording can provide accurate, fast, and robust estimation for the quality
of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis
shows that the total number of EMG channels needed to effectively predict
standing quality can be reduced while maintaining high estimation accuracy,
which provides more flexibility for rehabilitation robotic systems to
incorporate EMG recordings
Intelligent upper-limb exoskeleton using deep learning to predict human intention for sensory-feedback augmentation
The age and stroke-associated decline in musculoskeletal strength degrades
the ability to perform daily human tasks using the upper extremities. Although
there are a few examples of exoskeletons, they need manual operations due to
the absence of sensor feedback and no intention prediction of movements. Here,
we introduce an intelligent upper-limb exoskeleton system that uses cloud-based
deep learning to predict human intention for strength augmentation. The
embedded soft wearable sensors provide sensory feedback by collecting real-time
muscle signals, which are simultaneously computed to determine the user's
intended movement. The cloud-based deep-learning predicts four upper-limb joint
motions with an average accuracy of 96.2% at a 200-250 millisecond response
rate, suggesting that the exoskeleton operates just by human intention. In
addition, an array of soft pneumatics assists the intended movements by
providing 897 newton of force and 78.7 millimeter of displacement at maximum.
Collectively, the intent-driven exoskeleton can augment human strength by 5.15
times on average compared to the unassisted exoskeleton. This report
demonstrates an exoskeleton robot that augments the upper-limb joint movements
by human intention based on a machine-learning cloud computing and sensory
feedback.Comment: 15 pages, 6 figures, 1 table, Submitted for possible publicatio
Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton
Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer
Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi
Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well
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