135 research outputs found
Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey
Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN
Longitudinal tracking of physiological state with electromyographic signals.
Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A LASSO regularization is performed to observe changes in relationship between electromyography features and force plate outcomes. SVM classifiers are employed to correctly identify the times at which these experiments are performed, which is important as these indicate a trajectory of adaptation
Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis
Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness.
Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks.
Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience.
Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice
Using Artificial Intelligence To Improve The Control Of Prosthetic Legs
For as long as people have been able to survive limb threatening injuries prostheses
have been created. Modern lower limb prostheses are primarily controlled by adjusting
the amount of damping in the knee to bend in a suitable manner for walking and
running. Often the choice of walking state or running state has to be controlled manually
by pressing a button. While this simple tuning strategy can work for many users
it can be limiting and there is the tendency that controlling the leg is not intuitive and
the wearer has to learn how to use leg.
This thesis examines how this control can be improved using Artificial Intelligence (AI)
to allow the system to be tuned for each individual.
A wearable gait lab was developed consisting of a number of sensors attached to the
limbs of eight volunteers. The signals from the sensors were analysed and features
were extracted from them which were then passed through 2 separate Artificial Neural
Networks (ANN). One network attempted to classify whether the wearer was standing
still, walking or running. The other network attempted to estimate the wearer’s
movement speed. A Genetic Algorithm (GA) was used to tune the ANNs parameters
for each individual.
The results showed that each individual needed different parameters to tune the features
presented to the ANN. It was also found that different features were needed for
each of the two problems presented to the ANN.
Two new features are presented which identify the movement states of standing, walking
and running and the movement speed of the volunteer. The results suggest that the
control of the prosthetic limb can be improved
Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback
Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude
Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control
Instrumented wheelchair operates based on surface electromyography (sEMG) is one of alternative to assist impairment person for mobility. SEMG is chosen due to good in accuracy and easier preparation to place the electrodes. Motor neuron transmit electrical potential to muscle fibre to perform isometric, concentric or eccentric contraction. These electrical changes that is called Motor Unit Action Potential (MUAP) can be acquired and amplified by electrodes located on targeted muscles changes can be recorded and analysed using sEMG devices. But, sEMG device cost up to USD 2,100 for a sEMG data acquisition device that available on market is one of the drawback to be used by impairment person that most of them has financial problem due to unable to work like before. In addition, it is a closed source system that cannot be modified to improve the accuracy and adding more features. Open source system such as Arduino has limitation of specifications that makes able to apply nonpattern recognition control methods which is simpler and easier compared to pattern recognition. However, classification accuracy is lower than pattern recognition and it cannot be applied to higher number participants from different background and gender. This research aims are to develop an open-source Arduino based sEMG data acquisition device by formulating hybrid automata algorithm to differentiate MUAP activity during wheelchair propulsion. Addition of hybrid automata algorithm to run pattern and non-pattern recognition based control methods is an advantage to increase accuracy in differentiating forward stroke or hand return activity. Electrodes are placed on Biceps (BIC), Triceps (TRI), Extensor (EXT), Flexor (FIX) and MUAP activity recorded for 30 healthy persons. Then, experiment result was validated with simulation result using OpenSim biomedical modelling software. Mean, standard deviation (SD), confidence interval (CI) and maximum point different (MPD) of MUAP were calculated and to be used as thresholds for non-pattern recognition control method in method selection experiment. Meanwhile, pattern recognition is using Probability Density Function (PDF) to determine MUAP according to type of activities. Total of ten control methods determined from population and individual data were tested against another 10 healthy persons to evaluate the algorithm performance. Assessment of each control method done by misclassification matrix looking at True Positive (TP) and False Negative (FN) of power assist system activation period. Developed sEMG data acquisition device that is operated by Arduino MEGA 2560 and Myoware muscle sensors with sampling rate of above 400Hz successfully recorded MUAP from four arm muscles. Furthermore, 2.5 ms of average data latency for device to record, analyse, validate and creating commands to activate the power assist system. Data obtained from the device shows that most active muscle during wheelchair propulsion is TRI, followed by BIC and matched to OpenSim simulation result. In method selection experiment, 96.28% of average accuracy was achieved and different control methods were selected by misclassification matrix for each of persons. This method would be a control method to activate power assist system and selected based on conditions set in the algorithm. These findings indicated that open source Arduino board is capable of running real time pattern, non-pattern recognition based control methods by producing classification accuracy up to 99.48% even though it is known as just a microcontroller that has limitation to run complex classifiers. At the same time, a device that cost less than USD200 has 400Hz of sampling rate is as good as closed source device that is come with expensive price tag to own it. Based on algorithm evaluation, it shows that one control method couldn’t fit to all persons as per proven in method selection experiment. Different person has different control method that suit them the most. Lastly, BIC and TRI can be reference muscles to activate assistive device in instrumented wheelchair that is using propulsion as indication
On improving control and efficiency of a portable pneumatically powered ankle-foot orthosis
Ankle foot orthoses (AFOs) are widely used as assistive and/or rehabilitation devices to correct gait of people with lower leg neuromuscular dysfunction and muscle weakness. An AFO is an external device worn on the lower leg and foot that provides mechanical assistance at the ankle joint. Active AFOs are powered devices that provide assistive torque at the ankle joint. We have previously developed the Portable Powered Ankle-Foot Orthosis (PPAFO), which uses pneumatic power via compressed CO2 to provide untethered ankle torque assistance. My dissertation work focused on the development of control strategies for the PPAFO that are robust, applicable to different gait patterns, functional in different gait modes, and energy efficient. Three studies addressing these topics are presented in this dissertation: (1) estimation of the system state during the gait cycle for actuation control; (2) gait mode recognition and control (e.g., stair and ramp descent/ascent); and (3) system analysis and improvement of pneumatic energy efficiency.
Study 1 presents the work on estimating the gait state for powered AFO control. The proposed scheme is a state estimator that reliably detects gait events while using only a limited array of sensor data (ankle angle and contact forces at the toe and heel). Our approach uses cross-correlation between a window of past measurements and a learned model to estimate the configuration of the human walker, and detects gait events based on this estimate. The proposed state estimator was experimentally validated on five healthy subjects and with one subject that had neuromuscular impairment. The results highlight that this new approach reduced the root-mean-square error by up to 40% for the impaired subject and up to 49% for the healthy subjects compared to a simplistic direct event controller. Moreover, this approach was robust to perturbations due to changes in walking speed and control actuation.
Study 2 proposed a gait mode recognition and control solution to identify a change in walking environment such as stair and ramp ascent/descent. Since portability is a key to the success of the PPAFO as a gait assist device, it is critical to recognize and control for multiple gait modes (i.e., level walking, stair ascent/descent and ramp ascent/descent). While manual mode switching is implemented on most devices, we propose an automatic gait mode recognition scheme by tracking the 3D position of the PPAFO from an inertial measurement unit (IMU). Experimental results indicate that the controller was able to identify the position, orientation and gait mode in real time, and properly control the actuation. The overall recognition success rate was over 97%.
Study 3 addressed improving operational runtime by analyzing the system efficiency and proposing an energy harvesting and recycling scheme to save fuel. Through a systematic analysis, the overall system efficiency was determined by deriving both the system operational efficiency and the system component efficiency. An improved pneumatic operation utilized an accumulator to harvest and then recycle the exhaust energy from a previous actuation to power the subsequent actuation. The overall system efficiency was improved from 20.5% to 29.7%, a fuel savings of 31%. Work losses across pneumatic components and solutions to improve them were quantified and discussed.
Future work including reducing delay in recognition, exploring faulty recognition, additional options for harvesting human energy, and learning control were proposed
- …