Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared. © 2023 Mamidipaka Hema et al.

A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic

Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Spine patient care with wearable medical technology: state-of-the-art, opportunities, and challenges: a systematic review.

BACKGROUND CONTEXT: Healthcare reforms that demand quantitative outcomes and technical innovations have emphasized the use of Disability and Functional Outcome Measurements (DFOMs) to spinal conditions and interventions. Virtual healthcare has become increasingly important following the COVID-19 pandemic and wearable medical devices have proven to be a useful adjunct. Thus, given the advancement of wearable technology, broad adoption of commercial devices (ie, smartwatches, phone applications, and wearable monitors) by the general public, and the growing demand from consumers to take control of their health, the medical industry is now primed to formally incorporate evidence-based wearable device-mediated telehealth into standards of care. PURPOSE: To (1) identify all wearable devices in the peer-reviewed literature that were used to assess DFOMs in Spine, (2) analyze clinical studies implementing such devices in spine care, and (3) provide clinical commentary on how such devices might be integrated into standards of care. STUDY DESIGN/SETTING: A systematic review. METHODS: A comprehensive systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) across the following databases: PubMed; MEDLINE; EMBASE (Elsevier); and Scopus. Articles related to wearables systems in spine healthcare were selected. Extracted data was collected as per a predetermined checklist including wearable device type, study design, and clinical indices studied. RESULTS: Of the 2,646 publications that were initially screened, 55 were extensively analyzed and selected for retrieval. Ultimately 39 publications were identified as being suitable for inclusion based on the relevance of their content to the core objectives of this systematic review. The most relevant studies were included, with a focus on wearables technologies that can be used in patients' home environments. CONCLUSIONS: Wearable technologies mentioned in this paper have the potential to revolutionize spine healthcare through their ability to collect data continuously and in any environment. In this paper, the vast majority of wearable spine devices rely exclusively on accelerometers. Thus, these metrics provide information about general health rather than specific impairments caused by spinal conditions. As wearable technology becomes more prevalent in orthopedics, healthcare costs may be reduced and patient outcomes will improve. A combination of DFOMs gathered using a wearable device in conjunction with patient-reported outcomes and radiographic measurements will provide a comprehensive evaluation of a spine patient's health and assist the physician with patient-specific treatment decision-making. Establishing these ubiquitous diagnostic capabilities will allow improvement in patient monitoring and help us learn about postoperative recovery and the impact of our interventions.

Physical Therapy Management of a Patient with Bilateral Knee Pain and Long COVID: A Case Report

Background: The purpose of this case study is to describe how post-exertional symptom exacerbation (PESE), a hallmark of long COVID, may affect the clinical course of physical therapy. Physical therapists can provide patient education and activity guidance to individuals with long COVID to improve clinical outcomes. Case Description: The patient was a 39-year-old female with a 20-year history of bilateral knee pain. Following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the patient developed PESE and increased bilateral knee joint effusion and pain, affecting her ability to perform cognitive tasks and walk, respectively. Outcomes: Following 8 weeks of intervention, the patient's Lower Extremity Functional Scale score improved from 35/80 to 59/80 and numeric pain rating scale decreased from 7/10 to 4/10 at maximum, but she experienced an apparent PESE relapse. Discussion: Post-exertional symptom exacerbation can affect multiple body systems, which may affect a patient's ability to participate in physical therapy. Clinical Relevance: Physical therapy management of individuals with long COVID must include monitoring during and after exertion for signs and symptoms of PESE.

A Pose Detection based Continuous Authentication System Design via Gait Feature Analysis

In recent years, with the increasing demand for public safety and the impact of pneumonia (Corona Virus Disease 2019, COVID-19), long-distance, contactless authentication has become a hot topic. Gait recognition technology has broad application prospect in computer vision field because of its ability of long-distance gait recognition and identification verification. On the other hand, with the development of big data, cloud computing, 5G, IoT and other technologies, which makes the Continuous authentication based on cameras is already possible. Therefore, we propose a continuous authentication system based on human pose estimation framework by analyzing and extracting gait characteristics. This system not only has the advantages of easy acquisition, long distance, contactless, and hard to disguise gait recognition, but also has the functions of dynamic authorization and continuous authentication, This method will bring a new development direction for the research of human pose estimation and gait recognition and other related fields. © 2022 IEEE.

Prominent Fatigue but No Motor Fatigability in Non-Hospitalized Patients With Post-COVID-Syndrome.

Objectives: Fatigue is a frequent and often disabling symptom in patients with post-COVID syndrome. To better understand and evaluate the symptom of motor fatigue in the context of the post-COVID syndrome, we conducted treadmill walking tests to detect the phenomenon of motor fatigability or to evaluate whether evidence of organic lesions of the motor system could be found, similar to patients with multiple sclerosis. Method: Twenty-nine non-hospitalized patients with post-COVID syndrome completed the Fatigue Scale for Motor and Cognitive Function (FSMC) questionnaire to determine the trait component of subjective fatigue before they were tested on a treadmill walking at a moderate speed for up to 60 min or until exhaustion. During the walking test oxygen uptake, ventilation and acceleration data of both feet were collected. To determine motor performance fatigability, the Fatigue Index Kliniken Schmieder (FKS) was calculated using the attractor method. Results: The average walking duration was 42.7 ± 18.6 min with 15 subjects stopping the walking test prematurely. The FSMC score revealed a severe cognitive (37.6 ± 8.2) and motor (37.1 ± 7.8) fatigue averaged over all subjects but only two subjects showed an FKS above the normal range (>4), representing performance fatigability. There was no significant correlation between subjective fatigue (FSMC) and FKS as well as walking time. Absolute values of oxygen uptake and ventilation were in the normal range reported in literature (r = 0.9, p < 0.05), although eight subjects did not produce a steady-state behavior. Conclusion: Almost all patients with post-COVID syndrome and subjectively severe motor fatigue, did not show motor fatigability nor severe metabolic anomalies. This is argued against organic, permanent damage to the motor system, as is often seen in MS. Many of the patients were - to our and their own surprise - motorically more exertable than expected.

Individuals with a COVID-19 history exhibit asymmetric gait patterns despite full recovery.

COVID-19 is a multisystem infectious disease affecting the body systems. Its neurologic complications include -but are not limited to headache, loss of smell, encephalitis, and cerebrovascular accidents. Even though gait analysis is an objective measure of the neuro-motor system and may provide significant information about the pathophysiology of specific diseases, no studies have investigated the gait characteristics in adults after full recovery from COVID-19. This was a cross-sectional, controlled study that included 12 individuals (mean age, 23.0 ± 4.1 years) with mild-to-moderate COVID-19 history (COVD) and 20 sedentary controls (CONT; mean age, 24.0 ± 3.6 years). Gait was evaluated using inertial sensors on a motorized treadmill. Spatial-temporal gait parameters and gait symmetry were calculated by using at least 512 consecutive steps for each participant. The effect-size analyses were utilized to interpret the impact of the results. Spatial-temporal gait characteristics were comparable between the two groups. The COVD group showed more asymmetrical gait patterns than the CONT group in the double support duration symmetry (p = 0.042), single support duration symmetry (p = 0.006), loading response duration symmetry (p = 0.042), and pre-swing duration symmetry (p = 0.018). The effect size analyses of the differences showed large effects (d = 0.68-0.831). Individuals with a history of mild-to-moderate COVID-19 showed more asymmetrical gait patterns than individuals without a disease history. Regardless of its severity, the multifaceted long-term effects of COVID-19 need to be examined and the scope of clinical follow-up should be detailed.

Annual and Scientific Meeting Virtual Meeting of the Irish Gerontological Society

The proceedings contain 181 papers. The topics discussed include: using quality improvement methodology to visualize change interventions to improve managing the unwell care home resident;simulation-based education for acute stroke;pathfinder;an alternative to the emergency department for older patients who dial 999/112;exploring the impact of age and frailty status on the immune response to covid-19 illness using detailed immuno-phenotyping;gaitkeeper- a novel artificial intelligence based video gait analysis system to objectively measure health status;using co-design to develop a core competency framework for interprofessional collaboration within integrated care teams for older people in Ireland;and the new chronic disease management program for general practitioners: making the case for its revision to include dementia.

Technology outcome measures in neuromuscular disorders: A systematic review.

BACKGROUND AND PURPOSE: Portable and wearable devices can monitor a number of physical performances and lately have been applied to patients with neuromuscular disorders (NMDs). METHODS: We performed a systematic search of literature databases following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles, including all studies reporting the use of technological devices for motor function assessment in NMDs from 2000 to 2021. We also summarized the evidence on measurement properties (validity, reliability, responsiveness) of the analyzed technological outcome measures. RESULTS: One hundred studies fulfilled the selection criteria, most of them published in the past 10 years. We defined four categories that gathered similar technologies: gait analysis tools, for clinical assessment of pace and posture; continuous monitoring of physical activity with inertial sensors, which allow "unsupervised" activity assessment; upper limb evaluation tools, including Kinect-based outcome measures to assess the reachable workspace; and new muscle strength assessment tools, such as Myotools. Inertial sensors have the evident advantage of being applied in the "in-home" setting, which has become especially appealing during the COVID-19 pandemic, although poor evidence from psychometric property assessment and results of the analyzed studies may limit their research application. Both Kinect-based outcome measures and Myotools have already been validated in multicenter studies and different NMDs, showing excellent characteristics for application in clinical trials. CONCLUSIONS: This overview is intended to raise awareness on the potential of the different technology outcome measures in the neuromuscular field and to be an informative source for the design of future clinical trials, particularly in the era of telemedicine.

The Impact of SARS-CoV-2 (COVID-19) and its Lockdown Measures on the Mental and Functional Health of Older Individuals.

The effect of the COVID-19 on the physical and mental health of Italian older individuals displaying signs of cognitive deterioration has not been deeply investigated. This longitudinal study examined the impact of COVID-19 lockdown measures on the psychological well-being and motor efficiency of a sample of Italian community-dwellers with and without cognitive decline. Forty-seven participants underwent instrumental gait analysis performed in ecological setting using wearable sensors, and completed a battery of tasks assessing cognitive functioning and psychological well-being, before and after the full lockdown due to the COVID-19 spreading. A series of Multivariate Analyses of Variance (MANOVAs) documented that the superior gait performance of the cognitively healthy participants exhibited before the COVID-19 spread, vanished when they were tested at the end of the lockdown period. Moreover, before the outbreak of the COVID-19, cognitively healthy participants and those with signs of cognitive decline reported similar levels of psychological well-being, whereas, after the lockdown, the former group reported better coping, emotional competencies, and general well-being than the participants displaying signs of cognitive decline. In conclusion, the full COVID-19 outbreak had a significant impact on the mental and motor functioning of older individuals with and without signs of cognitive deterioration living in Italy.