Promoting a healthy ageing workforce: use of Inertial Measurement Units to monitor potentially harmful trunk posture under actual working conditions

Musculoskeletal disorders, particularly those involving the low back, represent a major health concern for workers, and originate significant consequences for the socio-economic system. As the average age of the population is gradually (yet steadily) increasing, such phenomenon directly reflects on labor market raising the need to create the optimal conditions for jobs which must be sustainable for the entire working life of an individual, while constantly ensuring good health and quality of life. In this context, prevention and management of low back disorders (LBDs) should be effective starting from the working environment. To this purpose, quantitative, reliable and accurate tools are needed to assess the main parameters associated to the biomechanical risk. In the last decade, the technology of wearable devices has made available several options that have been proven suitable to monitor the physical engagement of individuals while they perform manual or office working tasks. In particular, the use of miniaturized Inertial Measurement Units (IMUs) which has been already tested for ergonomic applications with encouraging results, could strongly facilitate the data collection process, being less time- and resources-consuming with respect to direct or video observations of the working tasks. Based on these considerations, this research intends to propose a simplified measurement setup based on the use of a single IMUs to assess trunk flexion exposure, during actual shifts, in occupations characterized by significant biomechanical risk. Here, it will be demonstrated that such approach is feasible to monitor large groups of workers at the same time and for a representative duration which can be extended, in principle, to the entire work shift without perceivable discomfort for the worker or alterations of the performed task. Obtained data, which is easy to interpret, can be effectively employed to provide feedback to workers thus improving their working techniques from the point of view of safety. They can also be useful to ergonomists or production engineers regarding potential risks associated with specific tasks, thus supporting decisions or allowing a better planning of actions needed to improve the interaction of the individual with the working environment

A machine learning approach for clinical gait analysis and classification of polymyalgia rheumatica using myoelectric sensors

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe study focuses on Polymyalgia Rheumatica (PMR), an autoimmune musculoskeletal disease primarily affecting the shoulder blade and hip muscles in older adults, particularly women aged 50 and above. The research aims to address two main challenges: the need for more clarity on the disease's pathophysiology and the challenge of identifying disease severity in patients. The study introduces a novel approach involving movement assessment, by designing a low-cost MyoTracker system, and using electromyography (EMG) features to understand the impact on patients' hip muscles. A clinical trial was conducted at Komfo Anokye Teaching Hospital in Ghana, where the study employed a qualitative research approach to monitor movement patterns. Participants were tasked to perform exercises comprising of gait, knee lifting, and knee extension with sensors attached to the hip muscles. This research unfolds in three iterations, the first investigation involved hip muscular imbalances where the significant difference between patients and healthy controls in the maximum voluntary contraction (MVC) values was recorded. The bilateral difference computed between the left and right hip in patients exhibited 15% MVC on average compared to the healthy control group's 6%, indicating substantial hip muscular imbalances. The second iteration involved a movement assessment to identify specific movement patterns in patients. Support Vector Machine (SVM) achieves 85% accuracy for gait exercises, while Decision Tree (DT) performs less efficiently at 70%. SVM also excels in knee lifting exercises (70% accuracy), outperforming DT (60%). Based on hip muscle activation, patients' movement patterns significantly differ from healthy controls. In the third iteration, deep learning techniques, specifically RNN-LSTM and Vision Transformer (ViT), classify PMR disease severity based on EMG features. The study's results carry significant clinical implications with the evidence of hip muscular imbalances aiding in designing tailored rehabilitation protocols. Importantly, this study uses a cost-effective method for determining disease severity, enabling predictions about patients with severe PMR conditions. The key contribution of this thesis is the identification of patients’ specific movement patterns and the determination of PMR severity among patients. Other contributions are the detection of hip muscular imbalance in patients and the design of rehabilitation protocols to address hip muscular imbalances and improve patients' range of motion, enhancing overall well-being. In conclusion, this comprehensive study leverages innovative approaches, from a MyoTracker system for movement assessment to deep learning models, to unravel the complexities of PMR disease. The collaboration with medical experts emphasises the potential real-world impact of this research in enhancing the treatment and recovery processes for individuals.Ghana Scholarship Secretaria

An investigation into the ultrastructural parameters of abdominal muscles in children and adolescents with spastic type cerebral palsy and the effect on postural muscle performance

Includes bibliographical references.Cerebral palsy (CP) is an abnormality in motor function and postural tone that usually occurs at an early age. Spastic type cerebral palsy (STCP) - the most common type of motor disorder - involves increased muscle tone, a rigid posture in the limbs and muscle weakness resulting in impairment of gross motor function, trunk instability and co-ordination. The management of CP cases includes a broad spectrum of therapeutic interventions, therefore involving a large multi-professional team, and providing an ideal opportunity for collaboration amongst professionals. The primary determinants of muscle function are the architectural parameters (MAP's) of the muscle which determine the macroscopic arrangement of muscle fibres relative to the axis of force generation. Ultrasonography was used to quantify these MAP's while the NORAXAN® electromyograph was used to monitor neuromuscular activity in children and adolescents with STCP (N = 63) and the results were compared with the findings from aged-matched individuals with typical development (TD), (N = 82). All the muscles - external oblique (EO), internal oblique (IO), transversus abdominis (TrA) and rectus abdominis (RA) - were thicker in the STCP group than in the TD group. The EO, IO and TrA muscles in the STCP group were thicker at rest than in individuals with TD. The MAP's of EO, IO and TrA in the STCP group decreased when the muscles changed from the resting to an active state, as opposed to increasing in the TD group. The four muscles of individuals with TD and the RA of the STCP group showed significant changes (p < 0.001) in the frequency of EMG activity between the resting and active states. With regards to pennation, the abdominal muscles could be regarded as a transition group of muscles, lying somewhere between pennated and non-pennated muscle bellies. The findings from this study revealed that the RA may be targeted during rehabilitation regimens in the provision of stability for the bony pelvis, however, the force generated by this muscle may not be sufficient for the maintenance of trunk stability without optimal support from the EO and IO. An elevated tone at rest in the EO and IO, coupled with unilateral activity of the RA may lead to mal-rotation of the bony pelvis. The gross motor function measure (GMFM), which tested the five main domains of activity in individuals with STCP was well aligned with the gross motor function classification system (GMFCS) Levels (disability status) but did not correlate with changes in MAP's or with changes in the frequency of EMG activity between resting and active states. The performance of daily activities by individuals with STCP may not be a reflection of the activity of a muscle. The physiological cost index (PCI) was performed as an outcome measure to determine and compare the level of energy consumption between the two groups. The participants with STCP consumed significantly more (p < 0.001) energy than the TD group. However, this test showed no association with MAP, EMG activity and the changes in these muscle parameters from resting to active states (rho ranged from -0.009 to 0.27 in the STCP group; rho ranging from - 0.423 to 0.199 in the TD group). The PCI may not be a useful test in determining the morphological transformation taking place in a muscle or muscle groups. The MAP's of the unaffected side of the abdominal muscles of the STCP individuals with hemiplegia showed similar characteristics to those of TD individuals. The STCP adversely affects the trunk musculature in a similar fashion to the limbs. Knowledge of the macroscopic arrangement of the abdominal muscles is important in the management of pelvic stability in individuals with STCP

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

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

Dinamik kasılmalarda kas yorgunluğunun elektromiyogram ve mekanomiyogram ölçümleri ile analizi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Kas yorgunluğunu tanımlayabilecek indeksleri arttırmak ve çeşitli parametrelerle ilişkisini belirlemek, kişilere özel antrenman programlarının geliştirilmesine, kişinin günlük hayattaki aktivite programını düzenlenmesine ve olası kas hasarlarının engellenmesine destek olacaktır. Bu doğrultuda yapılan çalışmada, Bruce protokolü ve 100m sprint performans testi ile gönüllülerin kaslarında oluşturulan yorgunluğun, farklı parametreler kullanılarak belirlenmesi hedef alınmıştır. Ayrıca çalışmaya katılan gönüllülerin antrene olup olmamasının, kas yorgunluğu tespiti üzerine etkileri de incelenmiştir. Literatürde yer alan yorgunluk tespit çalışmalarından farklı olarak bu çalışmada, sadece EMG, sadece MMG ve EMG-MMG kombinasyonları karşılaştırmalı olarak değerlendirilmiştir. Analizler için ilk olarak, spor geçmişi olan fakat şuan aktif bir spor dalıyla uğraşmayan kişilerden oluşan bir gönüllü grubu oluşturulmuştur. Çalışmanın başında antrene olmayan bu gönüllü grubunun belirlenen prosedüre göre kayıtları alınmıştır. Daha sonra aynı grup 8 hafta boyunca eğim antrenmanlarına katılmış ve kasları anterene hale geldiğinde, gönüllülere aynı prosedür tekrar uygulanmıştır. Elde edilen kayıtlar önişleme, DPD tabanlı enerji değerlerinin hesaplanması ve sınıflandırma aşamalarından geçirilmiştir. DPD ayrışımı 8 seviyede gerçekleştirilmiş ve sınıflandırma yapmak için ÇKYSA kullanılmıştır. Çalışma sonucunda EMG ve MMG kayıtlarının kombine uygulamasının, antrene olmayan kişilerin kas yorgunluğunu belirlemede daha başarılı bir yöntem olduğu tespit edilmiştir. Antrene kişilerin kas yorgunluğunun belirlenmesinde ise sadece EMG kayıtlarının kullanılması durumunda en başarılı sonuçlara ulaşılmıştır. Yine antrene kişilerde MMG'nin, EMG ile kombinasyona girmesi sonucunda bu yüksek test başarı değerlerini düşürdüğü görülmüştür. Ayrıca kas yorgunluğunun belirlenmesinde kullanılacak parametrelerin sadece kendi başına değerlendirilemeyeceği, EMG ve MMG kayıtlarının alındığı kişilerin antrenman düzeyinin, yaptığı aktivite ya da sporun EMG ve MMG'nin kas yorgunluğu belirlemedeki etkinliğini tamamen değiştirdiği açıkça ortaya konulmuştur.Increasing the indexes that can define muscle fatigue and determining it's relationship with various parameters will help the development of personel training programs, the regulation of personel daily life activity program and the prevention of possible muscle impairment. Therefore, in this study carried out, determination of the fatigue, which occurs in the volunteers' muscles via Bruce protocol and 100 m sprint performance test, was aimed by using various parameters. Moreover, the effects of whether the volunteers being trained or not, over the determination of muscle fatigue were analysed as well. Differently from the fatigue determination studies that are present in the literature, the records of solely Electromyogram (EMG), solely Mechanomyogram (MMG) and the combination of EMG and MMG were evaluated comperatively. For the analysis, firstly, a volunteers group, made of people who had a suport background in the past but now does not engage in any sport branches were performed. At the beginning of the study, the records of this group of volunteers who were untrained were taken according to the determined procedure. Afterwards, the same group participated in the trainings for 8 weeks and once their muscles became trained, the same procedure was applied to the volunteers again. The obtained records were passed through the stages of pre-processing, calculation of energy values based Wavelet Packet Transform (WPT), and classification. Decomposition of WPT was carried out in 8 levels and the Multi-layer Perceptron Artificial Neural Network (MLPNN) was used for classification. As the result of the study, it was determined that the combined application of EMG and MMG records was a more successful method for determining the muscle fatigue of those who were untrained. As for the determination of fatigue levels of those who were trained, the most successful results were attained by the use of solely EMG records. Once again, it was clearly revealed that the parameters to be used for the determination of muscle fatigue should not be evaluated single-handedly, and the training level of the persons, whose EMG and MMG records were taken, the daily activities they do and the sport activities they take part in totaly changed the effectiveness of determination of muscle fatigue by using EMG and MMG

Digital CMOS ISFET architectures and algorithmic methods for point-of-care diagnostics

Over the past decade, the surge of infectious diseases outbreaks across the globe is redefining how healthcare is provided and delivered to patients, with a clear trend towards distributed diagnosis at the Point-of-Care (PoC). In this context, Ion-Sensitive Field Effect Transistors (ISFETs) fabricated on standard CMOS technology have emerged as a promising solution to achieve a precise, deliverable and inexpensive platform that could be deployed worldwide to provide a rapid diagnosis of infectious diseases. This thesis presents advancements for the future of ISFET-based PoC diagnostic platforms, proposing and implementing a set of hardware and software methodologies to overcome its main challenges and enhance its sensing capabilities. The first part of this thesis focuses on novel hardware architectures that enable direct integration with computational capabilities while providing pixel programmability and adaptability required to overcome pressing challenges on ISFET-based PoC platforms. This section explores oscillator-based ISFET architectures, a set of sensing front-ends that encodes the chemical information on the duty cycle of a PWM signal. Two initial architectures are proposed and fabricated in AMS 0.35um, confirming multiple degrees of programmability and potential for multi-sensing. One of these architectures is optimised to create a dual-sensing pixel capable of sensing both temperature and chemical information on the same spatial point while modulating this information simultaneously on a single waveform. This dual-sensing capability, verified in silico using TSMC 0.18um process, is vital for DNA-based diagnosis where protocols such as LAMP or PCR require precise thermal control. The COVID-19 pandemic highlighted the need for a deliverable diagnosis that perform nucleic acid amplification tests at the PoC, requiring minimal footprint by integrating sensing and computational capabilities. In response to this challenge, a paradigm shift is proposed, advocating for integrating all elements of the portable diagnostic platform under a single piece of silicon, realising a ``Diagnosis-on-a-Chip". This approach is enabled by a novel Digital ISFET Pixel that integrates both ADC and memory with sensing elements on each pixel, enhancing its parallelism. Furthermore, this architecture removes the need for external instrumentation or memories and facilitates its integration with computational capabilities on-chip, such as the proposed ARM Cortex M3 system. These computational capabilities need to be complemented with software methods that enable sensing enhancement and new applications using ISFET arrays. The second part of this thesis is devoted to these methods. Leveraging the programmability capabilities available on oscillator-based architectures, various digital signal processing algorithms are implemented to overcome the most urgent ISFET non-idealities, such as trapped charge, drift and chemical noise. These methods enable fast trapped charge cancellation and enhanced dynamic range through real-time drift compensation, achieving over 36 hours of continuous monitoring without pixel saturation. Furthermore, the recent development of data-driven models and software methods open a wide range of opportunities for ISFET sensing and beyond. In the last section of this thesis, two examples of these opportunities are explored: the optimisation of image compression algorithms on chemical images generated by an ultra-high frame-rate ISFET array; and a proposed paradigm shift on surface Electromyography (sEMG) signals, moving from data-harvesting to information-focused sensing. These examples represent an initial step forward on a journey towards a new generation of miniaturised, precise and efficient sensors for PoC diagnostics.Open Acces