503 research outputs found
Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review
With the number of people surviving a stroke soaring, automated upper limb impairment assessment has been extensively investigated in the past decades since it lays the foundation for personalised precision rehabilitation. The recent advancement of sensor systems, such as high-precision and real-time data transmission, have made it possible to quantify the kinematic and physiological parameters of stroke patients. In this paper, we review the development of sensor-based upper limb quantitative impairment assessment, concentrating on the capable of comprehensively and accurately detecting motion parameters and measuring physiological indicators to achieve the objective and rapid quantification of the stroke severity. The paper discusses various features used by different sensors, detectable actions, their utilization techniques, and effects of sensor placement on system accuracy and stability. In addition, both the advantages and disadvantages of the model-based and model-free algorithms are also reviewed. Furthermore, challenges encompassing comprehensive assessment of medical scales, neurological deficits assessment, random movement detection, the effect of the sensor placement, and the effect of the number of sensors are also discussed
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
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
Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene
Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern
Mobility classification of cattle with micro-Doppler radar
Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification.
The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17).
The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data
Stroke rehabilitation. A mixed method study evaluating a novel physiotherapy intervention and patients’ experiences
In this thesis, the possible benefits and challenges regarding taking part in a comprehensive individualized physiotherapy intervention, called I-CoreDIST and usual care physiotherapy following an acute stroke, along with patient perceptions of participation along the rehabilitation continuum are addressed. This is a mixed method study consisting of a randomised controlled trial (RCT) and an in-depth interview study.
Sixty participants with acute strokes were recruited for the RCT and randomised into receiving I-CoreDIST or usual care physiotherapy for 12 weeks in equal doses. Assessments of postural control, levels of physical activity, balance gait and health related quality of life were undertaken at baseline and at 12 weeks post inclusion. Between- and within-group effects were calculated. From the full sample, 19 participants were purposely selected for interviews. The interviews were transcribed and analysed using systematic text condensation. The findings from the quantitative and qualitative analysis were integrated through extracting the main findings and identifying common themes, divergences, and inconsistencies. In doing so we aimed to highlight aspects related to the overall aim of the study that either study could not have alone. The analysis was informed by the International Classification of Functioning, Disability and Health framework and enactive theory.
There were no differences between groups in term of effect of I-CoreDIST or usual care physiotherapy except for significant gains in health-related quality of life in the usual care group. The integrated findings show improvements in postural control, balance and gait that align with experiences of becoming able, indicating that partaking in 12 weeks of intensive physiotherapy was beneficial regardless of group allocation. Variations in organisational structures and cultures shape opportunities for active patient participation in post-stroke rehabilitation. Meaning and motivation is connected to experiencing bodily changes and tailored treatment. Sustained inactivity despite functional improvements is a challenge in long-term care
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
2023- The Twenty-seventh Annual Symposium of Student Scholars
The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp
AI-based smart sensing and AR for gait rehabilitation assessment
Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.info:eu-repo/semantics/publishedVersio
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