Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface

Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses

Systems for Noninvasive Assessment of Biomechanical Load in the Lower Limb

Every move you make—and, yes, every step you take—is the result of action at a joint, and so proper joint function is pivotal to the way we explore and interact with the world around us. Unfortunately, joint function is often disrupted by injuries, chronic disorders, or neurological deficits, which can, in turn, disrupt quality of life. Many forms of joint dysfunction derive from adverse biomechanical loading conditions—that is, the forces and torques to which our limbs are subjected—and, thus, techniques for monitoring these loads during daily life may improve our understanding of how injuries and disorders arise and progress—and, most importantly, how best to treat them. The standard methods for assessing these loading conditions, however, are almost all benchtop-bound and confined to laboratories or clinics, so their utility in at-home or ambulatory settings—where they may be most impactful—is limited. In an attempt to address this void, in this work, we present three novel techniques for extracting information related to joint loading using a synthesis of noninvasive / wearable sensing and machine learning. First, we detail the development of an adjustable-stiffness ankle exoskeleton with multimodal sensing capabilities and use it to explore how humans interact with external elastic loading of the ankle during walking. Then, in an attempt to peer “under the skin,” we develop a novel form-factor for capturing joint sounds— the skin-surface vibrations produced by articulating structures internal to the joint—and demonstrate that these noninvasive measurements can be used to discriminate levels of axial loading at the knee. Finally, taking the concept of joint acoustics one step further, we introduce a new, active acoustics-based technique whereby the tensile loading of a particular tissue—the Achilles tendon—can be estimated by measuring the tissue’s mechanical response to a burst vibration on the skin surface. Using this approach, we are able to assess this loading state (and, by association, the net moment at the ankle) reliably across several activities of daily life, and, through a proof-of-concept study, we demonstrate how the technique can effectively translate to a fully wearable device. Collectively, the efforts reported in this thesis represent a novel, multi-path approach to assessing biomechanical loading states in the lower limb and the effects thereof. These tools and insights may serve as a basis for future development of wearable, accessible technologies for monitoring joint load during daily life, thereby reducing injury risk, tracking disease progress, assessing the efficacy of treatment, and accelerating recovery.Ph.D

Fall prevention strategy for an active orthotic system

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)Todos os anos, são reportadas cerca de 684,000 quedas fatais e 37.3 milhões de quedas não fatais que requerem atenção médica, afetando principalmente a população idosa. Assim, é necessário identificar eficientemente indivíduos com alto risco de queda, a partir da população alvo idosa, e prepará los para superar perturbações da marcha inesperadas. Uma estratégia de prevenção de queda capaz de eficientemente e atempadamente detetar e contrariar os eventos de perdas de equilíbrio (PDE) mais frequentes pode reduzir o risco de queda. Como slips foram identificados como a causa mais prevalente de quedas, estes eventos devem ser abordados como foco principal da estratégia. No entanto, há falta de estratégias de prevenção de quedas por slip. Esta dissertação tem como objetivo o design de uma estratégia de prevenção de quedas de slips baseada na conceção das etapas de atuação e deteção. A estratégia de atuação foi delineada com base na resposta biomecânica humana a slips, onde o joelho da perna perturbada (leading) apresenta um papel proeminente para contrariar LOBs induzidas por slips. Quando uma slip é detetada, a estratégia destaca uma ortótese de joelho que providencia um torque assisstivo para prevenir a queda. A estratégia de deteção considerou as propriedades atrativas dos controladores Central Pattern Generator (CPG) para prever parâmetros da marcha. Algoritmos baseados em threshold monitorizam o erro de previsão do CPG, que aumenta após uma perturbação inesperada na marcha, para a deteção de slips. O ângulo do joelho e a velocidade angular da canela foram selecionados como os parâmetros de monitorização da marcha. Um protocolo experimental concebido para provocar perturbações de slip a sujeitos humanos permitiu a recolha de dados destas variáveis para posteriormente validar o algoritmo de deteção de perturbações. Algoritmos CPG foram capazes de produzir aproximações aceitáveis dos sinais de marcha em estado estacionário do ângulo do joelho e da velocidade angular da canela com sucesso. Além disso, o algoritmo de threshold adaptativo detetou LOBs induzidas por slips eficientemente. A melhor performance global foi obtida usando este algoritmo para monitorizar o ângulo do joelho, que detetou quase 80% (78.261%) do total de perturbações com um tempo médio de deteção (TMD) de 250 ms. Além disso, uma média de 0.652 falsas perturbações foram detetadas por cada perturbação corretamente identificada. Estes resultados sugerem uma performance aceitável de deteção de perturbações do algoritmo, de acordo com os requisitos especificados para a deteção.Every year, an estimated 684,000 fatal falls and 37.3 million non-fatal falls requiring medical attention are reported, mostly affecting the older population. Thus, it is necessary to effectively screen high fall risk individuals from targeted elderly populations and prepare them to successfully overcome unexpected gait perturbations. A fall prevention strategy capable of effectively and timely detect and counteract the most frequent loss of balance (LOB) events may reduce the fall risk. Since slips were identified as the main contributors to falls, these events should be addressed as a main focus of the strategy. Nonetheless, there is a lack of slip-induced fall prevention strategies. This dissertation aims the design of a slip-related fall prevention strategy based on the conception of an actuation and a detection stage. The actuation strategy was delineated based on the human biomechanical reactions to slips, where the perturbed (leading) leg’s knee joint presents a prominent role to counteract slip-induced LOBs. Thereby, upon the detection of a slip, this strategy highlighted a knee orthotic device that provides an assistive torque to prevent the falls. The detection strategy considered the attractive properties of biological-inspired Central Pattern Generator (CPG) controllers to predict gait parameters. Threshold-based algorithms monitored the CPG’s prediction error produced, which increases upon an unexpected gait perturbation, to perform slip detection. The knee angle and shank angular velocity were selected as the monitoring gait parameters. An experimental protocol designed to provoke slip perturbations to human subjects allowed to collect data from these variables to further validate the perturbation detection algorithm. CPG algorithms were able to successfully produce acceptable estimations of the knee angle and shank angular velocity signals during steady-state walking. Furthermore, an adaptive threshold algorithm effectively detected slip-induced LOBs. The best overall performance was obtained using this algorithm to monitor the knee angle from the perturbed leg, which detected almost 80% (78.261%) of the total perturbations with a mean detection time (MDT) of 250 ms. In addition, a mean of 0.652 false perturbations were detected for each correct perturbation identified. These results suggest an acceptable perturbation detection performance of the algorithm implemented in light of the detection requirements specified

Smart Fabric sensors for foot motion monitoring

Smart Fabrics or fabrics that have the characteristics of sensors are a wide and emerging field of study. This thesis summarizes an investigation into the development of fabric sensors for use in sensorized socks that can be used to gather real time information about the foot such as gait features. Conventional technologies usually provide 2D information about the foot. Sensorized socks are able to provide angular data in which foot angles are correlated to the output from the sensor enabling 3D monitoring of foot position. Current angle detection mechanisms are mainly heavy and cumbersome; the sensorized socks are not only portable but also non-invasive to the subject who wears them. The incorporation of wireless features into the sensorized socks enabled a remote monitoring of the foot

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

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

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Proceedings XXII Congresso SIAMOC 2022

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica dà l’occasione a tutti i professionisti, dell’ambito clinico e ingegneristico, di incontrarsi, presentare le proprie ricerche e rimanere aggiornati sulle più recenti innovazioni nell’ambito dell’applicazione clinica dei metodi di analisi del movimento, al fine di promuoverne lo studio e le applicazioni cliniche per migliorare la valutazione dei disordini motori, aumentare l’efficacia dei trattamenti attraverso l’analisi quantitativa dei dati e una più focalizzata pianificazione dei trattamenti, ed inoltre per quantificare i risultati delle terapie correnti