Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version

Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs)

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field

Fifteen years of wireless sensors for balance assessment in neurological disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Wearable Haptic Devices for Gait Re-education by Rhythmic Haptic Cueing

This research explores the development and evaluation of wearable haptic devices for gait sensing and rhythmic haptic cueing in the context of gait re-education for people with neurological and neurodegenerative conditions. Many people with long-term neurological and neurodegenerative conditions such as Stroke, Brain Injury, Multiple Sclerosis or Parkinson’s disease suffer from impaired walking gait pattern. Gait improvement can lead to better fluidity in walking, improved health outcomes, greater independence, and enhanced quality of life. Existing lab-based studies with wearable devices have shown that rhythmic haptic cueing can cause immediate improvements to gait features such as temporal symmetry, stride length, and walking speed. However, current wearable systems are unsuitable for self-managed use for in-the-wild applications with people having such conditions. This work aims to investigate the research question of how wearable haptic devices can help in long-term gait re-education using rhythmic haptic cueing. A longitudinal pilot study has been conducted with a brain trauma survivor, providing rhythmic haptic cueing using a wearable haptic device as a therapeutic intervention for a two-week period. Preliminary results comparing pre and post-intervention gait measurements have shown improvements in walking speed, temporal asymmetry, and stride length. The pilot study has raised an array of issues that require further study. This work aims to develop and evaluate prototype systems through an iterative design process to make possible the self-managed use of such devices in-the-wild. These systems will directly provide therapeutic intervention for gait re-education, offer enhanced information for therapists, remotely monitor dosage adherence and inform treatment and prognoses over the long-term. This research will evaluate the use of technology from the perspective of multiple stakeholders, including clinicians, carers and patients. This work has the potential to impact clinical practice nationwide and worldwide in neuro-physiotherapy

Artificial intelligence-based software for recognizing parkinsonian gait patterns based on wearable miniaturized sensors

Dissertação de mestrado em Informatics EngineeringA Doença de Parkinson (DP) é uma doença degenerativa do sistema nervoso central, geralmente caracterizada por prejudicar vários aspetos da marcha dos pacientes, como bradicinesia, comprimento do passo encurtado e congelamento da marcha. As escalas de avaliação clínica são tipicamente usadas com base em exames para monitorizar esses sintomas motores associados à marcha. Além disso, estas avaliações são baseadas na memória dos pacientes e pesquisas subjetivas, fornecendo dados tendenciosos. Assim, são necessários dados de longo prazo sobre as atividades motoras diárias do paciente. Avanços tecnológicos forneceram dispositivos sensores pequenos e vestíveis capazes de capturar dados de longo prazo, podendo ser utilizados em ambientes domiciliares permitindo a captura de dados precisos. A combinação desses sensores com inteligência artificial (IA) produz modelos capazes de biomarcar os níveis de doença, condições motoras e bem-estar dos pacientes, e de fornecer dados não tendenciosos sobre os padrões de marcha dos pacientes. A integração destes modelos num aplicativo para médicos facilitará gerir o estado de DP e tratamentos mais personalizados serão alcançados. Tendo isto em conta, esta tese tem como objetivo usar dados de pacientes que apresentam deficiências de marcha para treinar modelos baseados em IA que sejam capazes de classificar níveis de doença, condições motoras e qualidade de vida desses pacientes. Para isso, foram adquiridos dados de 40 pacientes com DP, com o objetivo de desenvolver 3 modelos de IA diferentes, um usado para classificar o nível de doença de um paciente na escala UPDRS-III, outro para classificar as condições motoras escala H&Y e outro usado para classificar a qualidade de vida. Esses modelos foram implementados numa APP para auxiliar os médicos durante as suas consultas. Os resultados obtidos foram positivos. O modelo UPDRS-III conseguiu uma acurácia de 91,67%, uma sensibilidade de 90,43% e uma especificidade de 93,98%, enquanto o modelo H&Y alcançou uma acurácia de 88,98%, uma sensibilidade de 88,71%, e especificidade de 92,79%, sendo que o modelo PDQ-39 obteve acurácia de 84,19%, sensibilidade de 82,13% e especificidade de 90,24%.Parkinson’s Disease (PD) is a degenerative disease of the central nervous system, usually characterized by causing several gait impairment symptoms, such as bradykinesia, shortened stride length, shuffling gait and freezing of gait. Clinical assessment scales are typically used based on observational examinations to monitor these motor symptoms associated with gait. Further, these assessments are based on patients’ memory recall, subjective surveys, medication phase, and mood during the appointment, providing biased data. Thus, long-term data regarding the patient’s daily motor activities is required. Technological advancements provided small and wearable sensor devices able to capture long-term acquisitions of data. Given their miniaturized size and portability, these sensors can be used in domiciliary environments enabling to capture accurate data. Combining these sensors with artificial intelligence (AI) produces models able to biomark patients’ disease levels, motor conditions and well-being. These AI models can provide non-biased data about patients’ gait-associated patterns. Integrating these AI-based solutions in a user-friendly clinic APP for physicians will facilitate PD management, and more personalized treatments will be achieved. Taking this in mind, this thesis aims to use data from patients who show developed gait impairments to train AI-based models that are able to classify disease levels, motor conditions and the quality of life of said patients. For that, data from 40 patients with PD was gathered. This data was then used to develop 3 different AI models, one used to classify a patient’s disease level on the Unified Parkinson’s Disease Rating Scale (UPDRS-III) scale, another to classify a patient’s motor conditions on the Hoehn and Yahr (H&Y) scale, and another one used to classify a patient’s quality of life (QoL). These models were then implemented in an easy to use APP to help the physicians during their appointments with the patients. Positive results were obtained, being observed that. The UPDRS-III model manged to achieve achieve an accuracy of 91.67%, a sensitivity of 90.43%, and a specificity of 93.98%, while the H&Y model achieved an an accuracy of 88.98%, a sensitivity of 88.71%, and a specificity of 92.79%, and the Parkinson’s Disease Questionnaire (PDQ-39) model achieved an accuracy of 84.19%, a sensitivity of 82.13%, and a specificity of 90.24%

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

Wearable Inertial Devices in Duchenne Muscular Dystrophy: A Scoping Review

In clinical practice and research, innovative digital technologies have been proposed for the characterization of neuromuscular and movement disorders through objective measures. Among these, wearable devices prove to be a suitable solution for tele-monitoring, tele-rehabilitation, and daily activities monitoring. Inertial Measurement Units (IMUs) are low-cost, compact, and easy-to-use wearable devices that evaluate kinematics during different movements. Kinematic variables could support the clinical evaluation of the progression of some neuromuscular diseases and could be used as outcome measures. The current review describes the use of IMUs for the biomechanical assessment of meaningful outcome measures in individuals affected by Duchenne muscular dystrophy (DMD). The PRISMA methodology was used and the search was conducted in different databases (Scopus, Web of Science, PubMed). A total of 23 articles were examined and classified according to year of publication, ambulatory/non-ambulatory subjects, and IMU positioning on human body. The analysis points out the recent regulatory identification of Stride Velocity 95th Centile as a new endpoint in therapeutic DMD trials when measured continuously from a wearable device, while only a few studies proposed the use of IMUs in non-ambulatory patients. Clinical recognition of reliable and accurate outcome measures for the upper body is still a challeng

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention