Motion Calculation for Human Lower Extremities Based on EMG-Signal-Processing and Simple Biomechanical Model (original) (raw)
Related papers
A study on muscle activities through surface EMG for lower limb exoskeleton controller
Proceedings - 2013 IEEE Conference on Systems, Process and Control, ICSPC 2013, 2013
The motion of human body is complex but perfect and integrated effort of brain, nerves and muscles. Exoskeleton is a promising idea for human rehabilitation of the lower limb that is weak enough to move. EMG signal contains the information of human movement and can be considered as one of the most powerful input to exoskeleton controller. In this research, the activity of the lower limb muscles that are responsible for human sit to stand and stand to sit movement has been studied. In this regard, the activities of three muscles viz. rectus femoris, vastus lateralis and biceps femoris have been observed and recorded to perceive their activation pattern. The experimental results show that the maximum voltage of vastus lateralis at activation moment is greater or equal to +0.1 mV or lesser or equal to -0.1 mVduring sit to stand and stand to sit movement whereas same throughput was found for biceps femoris during sit to stand and for rectus femoris during stand to sit movement only. © 2013 IEEE. http://ieeexplore.ieee.org/ielx7/6720515/6735086/06735124.pdf?tp=&arnumber=6735124&isnumber=6735086
Frontiers in neuroscience, 2017
Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the...
On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018
The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g., Electromyography based control of a prosthetic hand). One of the biggest challenges in developing a surface Electromyography (sEMG) based interface is the selection of the right muscles for the execution of a desired task. In this work, we investigate optimal muscle selections for sEMG based decoding of dexterous in-hand manipulation motions. To do that, we use EMG signals derived from 14 muscle sites of interest (7 on the hand and 7 on the forearm) and an optical motion capture system that records the object motion. The regression problem is formulated using the Random Forests methodology that is based on decision trees. Regarding features selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 5-fold cross validation procedure is used for model assessment purposes and the importance values are calculated for each feature. This pilot study shows that the muscles of the hand contribute more than the muscles of the forearm to the execution of in-hand manipulation tasks and that the myoelectric activations of the hand muscles provide better estimation accuracies for the decoding of manipulation motions. These outcomes suggest that the loss of the hand muscles in certain amputations limits the amputees ability to perform a dexterous, EMG based control of a prosthesis in manipulation tasks. The results discussed can also be used for improving the efficiency and intuitiveness of EMG based interfaces for healthy subjects.
EMG Signal Processing for Hand Motion Pattern Recognition Using Machine Algorithms
2020
Stroke is a major cause of death and disability in the world. There were approximately 25.7 million stroke survivors and 6.5 million deaths from stroke [1]. Stroke can result in arm disability and reduce daily life activity via weak arm muscle activity [2]. Studies have been performed to discover therapeutic and assistive approaches to compensate for disabilities and restore functions. The emerging rehabilitative robotic systems, including assistive exoskeletons, provide a promising approach to conventional therapy [3-5]. Electromyography signal processing for assistive robot motion control is one of the innovative approaches. Robotic assistive systems have been reported to be able to restore patients’ motor function associated with the neuroplasticity of the brain [6, 7]. In addition, therapy time and quality of therapy are the key factors for functional recovery and improvement [8]. Rehabilitative robots provide significant advantages over classical stroke therapy [9,10]. Robotic ...