Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait

7openopenTrojaniello D; CEREATTI, Andrea; Pelosin E; Avanzino L; Mirelman A; Hausdorff JM; DELLA CROCE, UgoTrojaniello, D; Cereatti, Andrea; Pelosin, E; Avanzino, L; Mirelman, A; Hausdorff, Jm; DELLA CROCE, Ug

Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors

Movement analysis carried out in laboratory settings is a powerful, but costly solution since it requires dedicated instrumentation, space and personnel. Recently, new technologies such as the magnetic and inertial measurement units (MIMU) are becoming widely accepted as tools for the assessment of human motion in clinical and research settings. They are relatively easy-to-use and potentially suitable for estimating gait kinematic features, including spatio-temporal parameters. The objective of this thesis regards the development and testing in clinical contexts of robust MIMUs based methods for assessing gait spatio-temporal parameters applicable across a number of different pathological gait patterns. First, considering the need of a solution the least obtrusive as possible, the validity of the single unit based approach was explored. A comparative evaluation of the performance of various methods reported in the literature for estimating gait temporal parameters using a single unit attached to the trunk first in normal gait and then in different pathological gait conditions was performed. Then, the second part of the research headed towards the development of new methods for estimating gait spatio-temporal parameters using shank worn MIMUs on different pathological subjects groups. In addition to the conventional gait parameters, new methods for estimating the changes of the direction of progression were explored. Finally, a new hardware solution and relevant methodology for estimating inter-feet distance during walking was proposed. Results of the technical validation of the proposed methods at different walking speeds and along different paths against a gold standard were reported and showed that the use of two MIMUs attached to the lower limbs associated with a robust method guarantee a much higher accuracy in determining gait spatio-temporal parameters. In conclusion, the proposed methods could be reliably applied to various abnormal gaits obtaining in some cases a comparable level of accuracy with respect to normal gait

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Wearable devices in clinical gait analysis

Portable, efficient, exact, detailed, early and cost-effective clinical gait analysis (CGA) has key influence for planning, development and assessment rehabilitation strategies and models, as far as for prosthetics assessment. Novel families of mobile CGA solutions may provide earlier detection, more exact diagnosis, and more effective therapy of the gait disorders. Remote integration of aforementioned solutions to hospital information system may provide better and more actual knowledge for clinical decision-making purposes. This study aims at review of the alternative wearable devices to measure selected gait parameters, depending on the desired accuracy level.Mobilna, efektywna, dokładna, szczególowa, wczesna i tania kliniczna analiza chodu ma kluczowy wpływ na planowanie,postęp i ocenę strategii i modeli rehabilitacji, jak również przedmiotów zaopatrzenia ortopedycznego. Nowe rodziny mobilnych rozwiązań do klinicznej analizy chodu mogą zapewnić wczesniejsze wykrywanie, dokładniejszą diagnostykęoraz efektywniejszą terapię deficytów chodu. Zdalna integracja ww. rozwiązań ze szpitalnym systemem informacyjnym może zapewnić lepszą i aktualniejszą wiedzę na potrzeby klinicznego podejmowania decyzji. Niniejszy artykuł stanowi przegląd urządzeń do pomiaru wybranych parametrów chodu, w zależności od poządanej dokładności

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio

Detection of Human Vigilance State During Locomotion Using Wearable FNIRS

Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a

Gait monitoring: from the clinics to the daily life

Monitoring of gait in daily living allows a quantitative analysis of walking in unrestricted conditions, with many potential clinical applications. This thesis aims at addressing the limitations that still hinder the wider adoption of this approach in clinical practice, providing healthcare professionals and researchers new tools which may impact on current gait assessment procedures and improve the treatment of many diseases leading to – or generated by – mobility impairments. The thesis comprises four experimental sections: Accuracy of commercially-available devices. Step detection accuracy in currently available physical activity monitors was assessed in healthy individuals. The best performing device was then tested in multiple sclerosis patients, showing reliability but highly speed-dependent accuracy. These findings suggest that a short set of tests performed in controlled conditions could inform researchers before starting unsupervised monitoring of gait in patients. Differences between laboratory and free-living gait parameters. The study assessed the accuracy of two algorithms for gait event detection, and provided normative values of gait temporal parameters for healthy subjects in different environments and types of walking. A pilot study toward clinical application. This pilot study compared laboratory based tests with daily living assessment of gait features in multiple sclerosis patients. Results provided clear evidence that in this population clinical gait tests might not represent typical gait patterns of daily living. Analysis of free-living walking in patients with Diabetes. A systematic review is presented looking for evidence of the effectiveness of walking as physical activity to reduce inflammation. Then, cadence and step duration variability are examined during free-living walking in a group of patients with diabetes. This thesis systematically highlighted potential and actual limitations in the use of wearable sensors for gait monitoring in daily life, providing clear practical indications and normative values which are essential for the widespread informed and effective clinical adoption of this technology

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research