531 research outputs found
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Exploring mechanisms of disuse atrophy and optimal rehabilitation strategies for the restoration of muscle mass, structure & function
Disuse atrophy (DA) occurs during situations of unloading and is characterised by a loss of muscle mass and function. These reductions may be observed as early as 5 days into a period of unloading. While the reduction of muscle size is well studied, the reduction in muscle function is less well characterised. Furthermore, different muscles of the lower leg have been shown to express diverging profiles of muscle size loss as a result of DA. In particular, the medial gastrocnemius (MG) is relatively susceptible to DA while the tibialis anterior (TA) is resistant to even long-term bed rest of over a month. The average length of stay in hospital in the UK was last reported at 4.5 days which is enough time for DA to occur in the quadriceps. In older individuals, loss of muscle mass and function may reduce quality of life to the point of frailty and are less well suited to performing resistance exercise. Hence, alternative therapies to attenuate DA may be needed.
This thesis introduces skeletal muscle and its function as an organ in the human body, along with its composition and how this influences its function. It then discusses the study of DA and the situations in which it occurs, before covering the response of different muscles, the time course and strategies used for rehabilitation. General methods used within this thesis are detailed in Chapter 2. In Chapter 3, results of muscle size, strength, and various aspects of function from the vastus lateralis (VL), the MG and the TA to investigate the difference in response to 15-day unilateral lower limb immobilisation in young adults.
In Chapters 4 and 5, this thesis investigates the neuromuscular adaptation to this intervention in the VL compared to the non-immobilised control, and then the immobilised MG and TA, respectively. These results show an impairment of neural input to the VL and the MG following immobilisation which is not seen in the TA.
Finally, in Chapter 6, peripheral nerve stimulation is shown to potentially recruit from a broader pool of motor units than traditional neuromuscular electrical stimulation and as such may be more favourable for rehabilitation
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System
In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The “virtual stiffness” method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations
Understanding Personal Determinants of Lifting Strategy to Inform Movement-Focused Ergonomic Interventions
Introduction:
Lift training interventions are needed to reduce risk in jobs with non-modifiable demands, but to date have been generally ineffective. The lack of lift training effectiveness has been partially attributed to insufficient quality of content in the training programs. One way to improve the effectiveness of future lift training interventions may be to first understand what factors influence how a lifter chooses to move in the workplace (i.e., root causes). Previous research has identified that some lifters seem to consistently minimize resultant biomechanical exposures at the low back, but it is unclear why. If we can understand what personal factors influence how a lifter moves, lift training may be better targeted to address modifiable personal factors to minimize exposures during lifting.
The overarching objective of this thesis was to quantify the variability in low back exposures during lifting and to further determine if variability could be explained by personal factors including ability to perceive proprioceptive information, expertise, and a range of structural (i.e., body mass and stature) and functional (i.e., strength and flexibility) factors. With this understanding, I then aimed to identify which modifiable personal factors have the greatest prospective benefit of biasing a lifter to adopt a movement strategy with lower resultant biomechanical exposures using a computational modelling approach. The impetus for this thesis is to develop critical evidence as needed to inform the development of future, more efficacious lift training interventions.
Methods:
A cross-sectional between-subjects experimental design was used to address the thesis objectives. A sample of 72 participants were recruited to perform a lifting protocol consisting of both job-specific and generic lifting tasks. Purposive sampling was used to recruit participants with a range of experience and demographics. Ability to perceive sensory feedback was assessed using lift force and lift posture matching tests. The average and variability in resultant peak low back compression and A-P shear force, as well as kinematic features of whole-body movement strategy, during lifting were quantified as dependent variables. Consistently lower magnitudes of biomechanical exposures within a personal factor group would support that this group defines a movement objective that aims to minimize resultant exposures on the low back.
Using the experimentally obtained data, a probabilistic model was then developed that predicts the range of movement strategies and corresponding biomechanical exposures that are likely given a combination of underlying personal factors. Simulations were run to determine if improvements in any of ability to perceive sensory feedback, expertise, flexibility and/or strength capacity resulted in predicted reductions of low back exposure magnitude. Simulations were also conducted across a range of non-modifiable structural factors (i.e., sex, stature, and body mass) to evaluate whether the prospective benefit of improving modifiable factors to reduce low back exposures is generalizable across a working population.
Results:
Ability to perceive proprioceptive information (both force- and posture-sense) was associated with lower average and variability of low back loads. This suggests that individuals with better ability to perceive proprioceptive information may be more likely to define a movement objective to consistently minimize exposures. Albeit small effect sizes were observed with a maximum of 16% of variance in low back loads explained by proprioceptive ability.
Both structural and functional factors were significant predictors of average peak low back loads in lifting. However, except for females having lower variability in exposures than males, no other associations of personal factors to variability in loads was observed. These findings support that the investigated structural and functional factors can bias the range of available movement strategies to lifters, but don’t necessarily influence towards a movement objective aiming to minimize low back loading.
No differences in average or variability in peak low back loads were observed across expertise groups. While this finding highlights that expertise doesn’t seem to influence resultant exposures in lifting, differences in lifting kinematics were observed across groups suggesting other movement objectives may be defined as a function of expertise.
The prospective ability of reducing peak low back loads by improving modifiable personal factors was assessed using the developed probabilistic model. While improving proprioceptive ability, functional knee range of motion and strength were statistically associated with reducing low back loads, only improving functional knee range of motion was interpreted to have clinically significant effects on reducing low back loads during lifting.
Conclusion:
In this thesis the variance in peak low back loads during lifting that could be explained independently and inter-dependently by personal factors was investigated. These findings have implications for the development of future lift training interventions where improvements to functional knee range of motion may lead to retained lifting behaviour changes to reduce resultant peak low back loads during lifting. Secondary benefits may also come from improving proprioceptive ability and strength. Future lift training interventions can be developed to leverage these findings in practice where these results support that improvements to underlying flexibility, strength and proprioceptive ability seem to be important factors allowing individuals to adopt lower exposure lifting strategy
Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation
Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.
One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers.
A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks.
A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation
Principles of Rehabilitation Strategies in Spinal Cord Injury
Spinal cord injury (SCI) is a debilitating condition that affects millions of people worldwide and results in a remarkable health economic burden imposed on patients and the healthcare system annually. The most common causes of SCI are the trauma caused by falls, traffic accidents, or violence. The course of SCI is associated with several complications that severely impair the patient’s quality of life, including sensory and motor dysfunction, pain, neurogenic bladder and bowel, autonomic dysreflexia, cardiovascular and pulmonary dysfunction, spasticity, urinary tract infection, and sexual dysfunction. Despite great strides that have been made in the field of regenerative medicine and neural repair, the treatment of SCI still mostly revolves around rehabilitative strategies to improve patients’ quality of life and function. Rehabilitation following the SCI is a multidisciplinary process that requires the involvement of multiple disciplines. Moreover, recent advances in the field of neurorehabilitation following SCI, are changing the face of this field. Therefore, we decided to review various aspects of rehabilitation following the SCI, including the goals and different modalities whereby we could achieve them
Proceedings XXIII Congresso SIAMOC 2023
Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma.
Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica.
Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale.
Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società . Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione
Quantifying the bilateral deficit in force during maximal arm cycling wingates
The bilateral deficit phenomenon (BLD) is a reduction in performance during a bilateral
motor task when compared to the performance during the unilateral version of the same motor
task. The objective of the current study was to determine if there was a BLD during maximal arm
cycling Wingate tests. Thirteen healthy male participants performed three 30-second maximal
arm cycling Wingate tests during three experimental sessions. Each session the participants
completed Wingate tests with 1) both arms, 2) dominant arm, and 3) non-dominant arm at
randomized intensities including 3% body weight (BW), 4% BW, or 5% BW. Instantaneous
force data on the pedal axis was recorded and used to calculate the BLD. Data were analyzed
using a three-way ANOVA with factors of intensity (3% BW, 4% BW, and 5% BW), time
during the Wingate (1s – 10s, 11s – 20s, and 21s – 30 s), and position (1 o’clock position and 6
o’clock position). There was an overall BLD of -31.68 ± 21.20% (p <.001). The magnitude of
the bilateral index (BI) value was significantly affected by the intensity of the Wingate (p =.006),
and the time period of the Wingate (p<.001), but not the position. There were differences in the
magnitude of the BLD across intensities and time periods. Overall, a BLD in force exists during
maximal arm cycling Wingates and it is affected by fatigue and the movement velocity.
Increases in movement velocity decrease the magnitude of the BLD and increased amounts of
muscle fatigue likely increase the magnitude of the BLD
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