175 research outputs found
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
Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme
In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university
Investigation on the Effects of Fastening Parameters on the Handle Displacement of a Pistol Grip Tool
Workers in automotive assembly lines routinely use DC-powered pistol grip tools to install threaded fasteners. While these tools are easy to use and increase production quality, tool operators are subjected to impulsive reaction torques that produce forceful rotary displacement of the tool handle. While operators try to resist this reaction, forces exerted by the forearm muscles are often insufficient, thereby producing eccentric contractions. Repeated exposure to such forces is known to cause tendonitis, fatigue, and physical stress. The objective of this study was to investigate the operational parameters and optimize conditions that minimize the tool handle displacement.
A deterministic approach was considered to identify the system parameters. The tool-operator system was mathematically represented using a single degree-of-freedom torsional model. An in-vivo study of 10 experienced workers was conducted to estimate the typical ranges of operator stiffness. Tightening tasks were performed at 3 torques (5, 7.5, 10 Nm), and 4 fastener locations that correspond to varying orientations of the wrist. The mean operator stiffness was found to be 1.11 kN/m. A pistol grip tool simulator was designed and developed to emulate the dynamics of tightening operation without the use of human operators. A slider-crank mechanism was considered to represent the kinematics of the torsional system, and a pneumatic actuator was used to represent individual operator stiffness.
A parametric study observed the effects on tool handle response due to varying torque, operator stiffness, spindle speed, fastener material, drive style, and fastener head type. Results showed that an increase in applied torque (5–7.5 Nm) also increased the angular displacement of the tool handle (42.2°–58.5°). Variation in stiffness resulted in an inverse effect on the handle response. At 7.5 Nm, it was observed that wrist ulnar deviation produced the most handle displacement (59.2°), whereas wrist flexion produced the least (57.3°). Variation in the operational speed of tool spindle showed no significant effect on the handle displacement. Three fastener materials, alloy steel, stainless steel, and brass were tested. It was observed that alloy steel resulted in the least displacement (65.4°), whereas brass produced the most (85.7°). Between the two drive styles, it was observed that a Hex drive produced significantly higher response (69.9°) than a Torx Plus drive (63.4°). Button Head fasteners produced significantly higher response (76.2°) compared to Flat Head fasteners (66.9°). Based on this data, it was concluded that the designed pistol grip tool simulator can be used to investigate and optimize the operational parameters such as tool tightening algorithm, fastener types, and task locations, thus minimizing the tool handle displacement and mitigating forearm strain injuries
Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running
Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
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
Robust and reliable hand gesture recognition for myoelectric control
Surface Electromyography (sEMG) is a physiological signal to record the electrical activity of muscles by electrodes applied to the skin. In the context of Muscle Computer Interaction (MCI), systems are controlled by transforming myoelectric signals into interaction commands that convey the intent of user movement, mostly for rehabilitation purposes. Taking the myoeletric hand prosthetic control as an example, using sEMG recorded from the remaining muscles of the stump can be considered as the most natural way for amputees who lose their limbs to perform activities of daily living with the aid of prostheses. Although the earliest myoelectric control research can date back to the 1950s, there still exist considerable challenges to address the significant gap between academic research and industrial applications. Most recently, pattern recognition-based control is being developed rapidly to improve the dexterity of myoelectric prosthetic devices due to the recent development of machine learning and deep learning techniques. It is clear that the performance of Hand Gesture Recognition (HGR) plays an essential role in pattern recognition-based control systems. However, in reality, the tremendous success in achieving very high sEMG-based HGR accuracy (≥ 90%) reported in scientific articles produced only limited clinical or commercial impact. As many have reported, its real-time performance tends to degrade significantly as a result of many confounding factors, such as electrode shift, sweating, fatigue, and day-to-day variation. The main interest of the present thesis is, therefore, to improve the robustness of sEMG-based HGR by taking advantage of the most recent advanced deep learning techniques to address several practical concerns. Furthermore, the challenge of this research problem has been reinforced by only considering using raw sparse multichannel sEMG signals as input. Firstly, a framework for designing an uncertainty-aware sEMG-based hand gesture classifier is proposed. Applying it allows us to quickly build a model with the ability to make its inference along with explainable quantified multidimensional uncertainties. This addresses the black-box concern of the HGR process directly. Secondly, to fill the gap of lacking consensus on the definition of model reliability in this field, a proper definition of model reliability is proposed. Based on it, reliability analysis can be performed as a new dimension of evaluation to help select the best model without relying only on classification accuracy. Our extensive experimental results have shown the efficiency of the proposed reliability analysis, which encourages researchers to use it as a supplementary tool for model evaluation. Next, an uncertainty-aware model is designed based on the proposed framework to address the low robustness of hand grasp recognition. This offers an opportunity to investigate whether reliable models can achieve robust performance. The results show that the proposed model can improve the long-term robustness of hand grasp recognition by rejecting highly uncertain predictions. Finally, a simple but effective normalisation approach is proposed to improve the robustness of inter-subject HGR, thus addressing the clinical challenge of having only a limited amount of data from any individual. The comparison results show that better performance can be obtained by it compared to a state-of-the-art (SoA) transfer learning method when only one training cycle is available. In summary, this study presents promising methods to pursue an accurate, robust, and reliable classifier, which is the overarching goal for sEMG-based HGR. The direction for future work would be the inclusion of these in real-time myoelectric control applications
Assessment of Physical Fitness and Training Effect in Individual Sports
Physical fitness is the basis for the success of players in sports, and its monitoring makes it possible to assess the effectiveness of training and identify possible errors. During training, thanks to the use of control results, these activities are modified, which better prepares players for competition. This Special Issue, entitled "Assessment of Physical Fitness and the Effect of Training in Individual Sports" presents the results of coaching control and the results of monitoring progression in training, as well as an assessment of the physical fitness of athletes practicing individual sports
Successfully Controlled BCI Through Minimal Dry Electrodes
ABSTRACT:
There are approximately 185,000 amputations a year in the United States according to the Amputee Coalition with the number of amputations going up. While it is common for someone with a lower limb amputation to use a prosthetic, approximately 84%, it is not as common for people with upper limb amputations, approximately 56% (Raichle et al., 2008). The time it takes an amputee to get a prosthetic affects the likelihood of use, in addition to functionality (Miller et al., 2020). The purpose of this project is to show proof of concept of an EEG-controlled prosthetic, using only 2 dry-electrodes, through the use of BCI2000 using imagined movements. Eight (N-8) participants were recruited to complete a pre-training mu task, a 1D cursor training task, a 2D cursor training task, and the main 2D cursor task.
After a frequency was established for each participant, they completed 200 trials of the 1D cursor task for three different conditions (left, right, and both hand(s)) or reached a success rate of 80% for 4 trials in a row with random targets. The participants then completed the 2D cursor task with random targets until a success rate of 70% for 4 trials in a row was achieved, followed by a 2D cursor task where the targets were pre-determined. A chi-squared test determined the goodness of fit for the success rate was significant (p < 0.001) for all participants completing the 1D cursor task. The combined success rate for the participants during task 1 for their right hand was 30.16%, 47.11% for their left hand, and 61.47% for both hands. The combined success rate for task 2 was 69.40% and 79.59% for the main task.
Overall, this study successfully showed that 2 dry electrodes can be used to detect imagined movements through BCI. While the accuracy can still be improved, by enhancing the equipment and developing the training protocol, both participants that completed the main task were able to surpass the expected overall accuracy and surpass 4 out of the 6 individual accuracies. Whether it is to control a mechanical arm, leg, or other body part, the framework of this study grants development opportunities for BCI from a few dry electrodes
Brachial Plexus Injury
In this book, specialists from different countries and continents share their knowledge and experience in brachial plexus surgery. It discusses the different types of brachial plexus injury and advances in surgical treatments
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