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Every government in the world introduced restrictions to human mobility – that is, the movement of persons across and within state borders – in response to the COVID-19 pandemic. Such restrictions thus constituted a global phenomenon, but they were by no means globally uniform;rather, they varied significantly between and within states, as well as over time. This research note presents different data sources for studying the drivers and outcomes of mobility restrictions, highlighting specific ways in which the data can be used. We begin by surveying seven new databases capturing various aspects of the regulation of human movement during the COVID-19 pandemic. Drawing inspiration from research on previous pandemics, we then outline five possible research avenues prompted by these data. We suggest that explaining the causes and consequences of such restrictions, as well as the differences between them, can significantly advance research on the governance of mobility, migration, and citizenship.
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The economic turmoil and restrictions on human movement precipitated by the COVID-19 pandemic severely impacted conservation efforts. Many conservation actors rapidly implemented various adaptive measures in response to the cessation of the nature-based tourism industry, the primary revenue source for much of conservation in sub-Saharan Africa. This timely preliminary study examined the innovative use of virtual safaris, a form of virtual nature-based tourism, as an adaptive response to the crisis. Eight in-depth semi-structured interviews and two written responses from a range of ‘conservation operators' provided insight into motivations, benefits, and challenges associated with using virtual safaris. This novel study found three mechanisms through which virtual safaris helped to alleviate the effects of COVID-19 with the potential to develop conservation resilience: 1) as a stopgap measure, 2) for revenue diversification, and 3) as a means of scaling ecosystem services. Virtual safaris provided a critical lifeline for conservation operators, created a new tool to connect with distant audiences, and strengthened relationships with donors. However, this research highlighted a need to re-evaluate the role of sustainable tourism within conservation, with transformative changes essential to enhance future conservation resilience.
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Rehabilitation services are among the most severely impacted by the COVID-19 pandemic. This has increased the number of people not receiving the needed rehabilitation care. Home-based rehabilitation becomes alternative support to face this greater need. However, monitoring kinematics parameters during rehabilitation exercises is critical for an effective recovery. This work proposes a detailed framework to estimate knee kinematics using a wearable Magnetic and Inertial Measurement Unit (MIMU). That allows at-home monitoring for knee rehabilitation progress. Two MIMU sensors were attached to the shank and thigh segments respectively. First, the absolute orientation of each sensor was estimated using a sensor fusion algorithm. Second, these sensor orientations were transformed to segments orientations using a functional sensor-to-segment (STS) alignment. Third, the relative orientation between segments, i.e., knee joint angle, was computed and the relevant kinematics parameters were extracted. Then, the validity of our approach was evaluated with a gold-standard optoelectronic system. Seven participants completed three to five Timed-Up-and-Go (TUG) tests. The estimated knee angle was compared to the reference angle. Root-mean-square error (RMSE), correlation coefficient, and Bland-Altman analysis were considered as evaluation metrics. Our results showed reasonable accuracy (RMSE < 8°), strong to very-strong correlation (correlation coefficient > 0.86), a mean difference within 1.1°, and agreement limits from -16° to 14°. In addition, no significant difference was found (p-value > 0.05) in extracted kinematics parameters between both systems. The proposed approach might represent a suitable alternative for the assessment of knee rehabilitation progress in a home context.
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The COVID-19 outbreak has restricted most outdoor activities, leads to increasing interest in exercise at home with online trainers. One issue of online exercise technology is the safety since improper motion might result in injury. As a basis to prevent improper motion, methods for evaluating the motion similarity between an instructor and a trainee are essential. Cosine similarity, Angular difference, and Euclidean distance are three general ways for the motion evaluation. This study aimed to determine the most effective way for analyzing the similarity of human motion on the dataset of instructor-led dances. We first experimented with the data to find the appropriate cut-off value for classifying posture into two classes based on the similarity score. Confusion matrix, precision, recall, F1-score, accuracy of the results were then used to compare the efficiency. We discovered that Cosine similarity had the highest accuracy, 82.77 percent at cut-off 93. © 2022 IEEE.
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Environmental damage that occurs is generally caused by human activities. The activities carried out lead to mobilities with a tendency to a certain pattern. During the Covid-19 Pandemic, the Indonesian government issued a policy of restricting the human mobility to reduce the spread of the Covid-19 virus. The reduced human mobility in mining activities may lessen the level of environmental damage that occurs. Therefore, this study aims to determine the effect of human mobility restrictions during the Covid-19 pandemic on the level of environmental damage that has occurred. Pangkalpinang City was taken as the location for data collection in this study. Data collection will be undertaken by distributing questionnaires online to people who live in the research location. From the results of the questionnaire, human mobility patterns will be mapped to the activities conducted and the level of environmental damage that has occurred. The results of this study can show the distribution of human mobility patterns to environmental damage that occurred due to policies to restrict human movement during Covid-19 Pandemic.
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During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention. Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period. How to improve the performance and generalization of the monitoring model is a critical but challenging task. This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’ services, such as patient-care, disinfection, garbage disposal, etc. We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs). By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs. All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics. The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances. These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic.
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Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.
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Torticollis , Feasibility Studies , Humans , Reproducibility of Results , Research Design , Severity of Illness Index , Torticollis/diagnosis , Treatment OutcomeABSTRACT
During the COVID-19 pandemic, the government decided to suspend all arts cultural events to prevent the spread of the virus. This situation is a challenge for batik artisans to survive. This research aims to develop a virtual try-on platform that is an alternative medium for artisans to solve their problems. Development platforms based on augmented reality technology can be an option for the problems. Platform designed based on mobile devices has advantages in the practicality of use that is not limited by space and time. Implementation of human motion capture and hand gesture recognition provides an immersive experience for users. Motion capture is used for a virtual try-on scheme for batik apparel that can make users try batik apparel virtually and can automatically fit the user’s body. In addition, the implementation of hand gesture recognition allows users to apply batik motifs to virtual apparel interactively combined with material fitting function, which can assist users in positioning batik motifs. Apart from technical matters, this platform also provides information about the history of batik motifs. Alpha testing is used in testing the platform and confusion matrix to validate the accuracy of implementing the functions that exist on the platform. The results of testing the accuracy of hand gesture recognition reached 97%, and human motion capture reached 93%, which means the system can run well. This paper describes the initial efforts made to develop a virtual try-on platform for batik apparel based on Augmented Intelligence Technology. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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It has become a scientific obligation to unveil the underlying mechanisms and the fabrication methods behind wearable/stretchable strain sensors based on intelligent nanomaterials in order to explore their possible potential in the field of biomedical and healthcare applications. This report is based on an extensive literature survey of fabrication of stretchable strain sensors (SSS) based on nanomaterials in the fields of healthcare, sports, and entertainment. Although the evolution of wearable strain sensors (WSS) is rapidly progressing, it is still at a prototype phase and various challenges need to be addressed in the future in special regard to their fabrication protocols. The biocalamity of COVID-19 has brought a drastic change in humans' lifestyles and has negatively affected nations in all capacities. Social distancing has become a mandatory rule to practice in common places where humans interact with each other as a basic need. As social distancing cannot be ruled out as a measure to stop the spread of COVID-19 virus, wearable sensors could play a significant role in technologically impacting people's consciousness. This review article meticulously describes the role of wearable and strain sensors in achieving such objectives.
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Background Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Survey Methodology Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. Results A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults;Hereditary/Genetic Neuromuscular Disorders;Frailty;and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Conclusions Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.
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As the SARS-CoV-2 coronavirus swept around the world in 2020, outpacing public health efforts to contain it, many everyday human mobilities were brought to an abrupt halt, while others were drastically reorganized. Viral mobilities have unleashed not just a disruption of human mobilities, but also a vast intensification of existing uneven relations of (im)mobilities. Critical mobilities studies range from the microscopic scales of viral mobilities that hitch a ride on human movement (Lavau 2014;see also Jensen, this Issue), to the macroscale planetary mobilities of the Anthropocene (Szerszynski 2016). In this Special Issue on ‘Pandemic (Im)mobilities’ we have invited leading contributors in the field of mobilities studies to address the multiple issues generated by the pandemic’s relation to complex (im)mobilities at many scales, as well as deeper theoretical issues that arise when we consider the pandemic from a critical mobilities approach.
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Skin‐like electrical sensor has been widely employed for wearable human healthcare monitoring but is limited by electromagnetic interferences, poor waterproof performance, and point‐type measurement. Herein, a skin‐like and stretchable optical fiber (SSOF) sensor with excellent stretchability (up to 100%), flexibility, and excellent compliance with skin is reported. A hybrid coding based on the light intensity difference of two fiber Bragg gratings (FBGs) is created to achieve the resistance for light power fluctuations and the capability of distributed measurement. The SSOF sensor has outstanding durability (>10 000 cycles), waterproofness, and impact resistance. And it can stably work in heat (55 °C) or cold (≈0 °C) environment as well. Furthermore, the SSOF sensor‐based human–computer interaction system is created to achieve the distributed monitoring of physiological parameters and human full‐body movement leading to the enormous potential for virtual reality (VR) and rehabilitation therapy.
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The flexible and wearable capacitive sensors have captured tremendous interest due to their enormous potential for healthcare monitoring, soft robotics, and human−computer interface. However, despite recent progress, there are still pressing challenges to develop a fully integrated textile sensor array with good comfort, high sensitivity, multisensing capabilities, and ultra-light detection. Here, we demonstrate a pressure and non-contact bimodal fabric-only capacitive sensor with highly sensitive and ultralight detection. The graphene nanoplatelets-decorated multidimensional honeycomb fabric and nickel-plated woven fabric serve as the dielectric layer and electrode, respectively. Our textile-only capacitive bimodal sensor exhibits an excellent pressure-sensing sensitivity of 0.38 kPa−1, an ultralow detection limit (1.23 Pa), and cycling stability. Moreover, the sensor exhibits superior non-contact detection performance with a detection distance of 15 cm and a maximum relative capacitance change of 10%. The sensor can successfully detect human motion, such as finger bending, saliva swallowing, etc. Furthermore, a 4 × 4 (16 units) textile-only capacitive bimodal sensor array was prepared and has excellent spatial resolution and response performance, showing great potential for the wearable applications.
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Robotic platforms for helping people with disabilities are being developed with the aim of providing both rehabilitation treatment and assistance in improving their quality of life, mainly for those who have mobility problems or some type of functional disability. Currently, according to the WHO (World Health Organization), existing rehabilitation services have been disrupted in 60–70% of countries due to this pandemic, because of the need to avoid human contact. [...]countries must face major challenges to ensure the health and autonomy of their disabled population. In [7], Vicente-Samper et al. propose mixing data from a personal device (that measures motor activity with an inertial sensor, heart rate, and body temperature) with that from an environmental device (based on a camera for tracking people around the user in order to detect social interactions) into a standard database where machine learning algorithms can extract user models (i.e., concentration level—distracted vs. focused—of the user while performing a task, such as reading a book).
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The green building certification system has long-lasting benefits by improving building efficiency and sustainability. The ultimate goal of such classification is to promote the preservation of the global environment as well as the occupants’ well-being and their health. In this paper, we present examples of buildings that have been designed and built in Poland and have been certified with BREEAM, LEED and WELL. Our study investigates human factors in certification systems and examines the WELL Building Standard as a supplement to other green systems, which will probably be the most popular in the future. The green building movement should prioritize pro-human factors and the associated environmental beliefs to improve indoor environment quality for users’ needs. We present this matter on the example of the Polish office space market, providing statistics and analyzing the architecture of six certified office buildings from Warsaw, Poznań and Wrocław. They are a representative sample of buildings designed following the certification regime. It was demonstrated how this aids in improving work comfort, enhances the program of office spaces and the organization of service spaces within buildings, which increases the rank of this architecture and positively affects the urban environment.