Individual Mobility Research Articles

Long-term impact of a community-based adapted boxing program on physical functioning and quality of life of individuals with Parkinson's disease.

Adapted boxing can help improve the physical functioning and health-related quality of life (HRQoL) of individuals with Parkinson's disease (PD). Whether these benefits persist longitudinally is unclear. The purpose of this retrospective study was to evaluate the impact of a community-based adapted boxing program on the physical functioning and HRQoL of individuals with PD over 1-1.5 years. Twenty-six individuals with PD agreed to share their results on tests administered upon enrollment in the program (PRE) and ∼431 days later (POST). The tests included the Fullerton Advanced Balance scale, (FAB), the Timed Up-and-Go test (TUG), the 30-second Sit-to-Stand test (30-STS), and the PD questionnaire-39 (PDQ-39). From PRE to POST, performance significantly improved on the TUG and 30-STS tests (both p < 0.001), but not on the FAB (p = 0.79). Over the same period, PDQ-39 scores significantly increased (p = 0.05). No PRE to POST changes surpassed the minimal detectable change threshold. The results of this study suggest that adapted boxing is at worst non-detrimental and at best potentially beneficial for muscle strength, endurance, and functional mobility in individuals with PD. However, adapted boxing probably cannot fully counteract the HRQoL decrements that accompany PD progression.

On the Actuation Technologies of Biomedical Microrobot: A Summarized Review

In recent years, wireless microrobots have gotten more attention due to their huge potential in the biomedical field, especially drug delivery. Microrobots have several benefits, including small size, low weight, sensitivity, and flexibility. These characteristics have led to microscale improvements in control systems and power delivery with the development of submillimeter-sized robots. Wireless control of individual mobile microrobots has been achieved using a variety of propulsion systems, and improving the actuation and navigation of microrobots will have a significant impact. On the other hand, actuation tools must be integrated and compatible with the human body to drive these untethered microrobots along predefined paths inside biological environments. This study investigated key microrobot components, including medical applications, actuation systems, control systems, and design schemes. The efficiency of a microrobot is impacted by many factors, including the material, structure, and environment of the microrobot. Furthermore, integrating a hybrid actuation system and multimodal imaging can increase the microrobot's navigation effect, imaging algorithms, and working environment. In addition, taking into account the human body's moving distance, autonomous actuating technology could be used to deliver microrobots precisely and quickly to a specific position using a combination of quick approaches.

Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph

Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that—given the node occupation at a given time—it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes—through an embedding construction for each node in the graph—the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation.

HAND GESTURE CONTROLLED WHEEL CHAIR

The project for controlling wheelchairs through gestures offers a complete solution to enhance the mobility and independence of individuals with limited motor abilities using an intuitive gesture control system. This system incorporates essential elements, such as an Arduino Nano microcontroller, MPU (Motion Processing Unit), NRF wireless communication module, 775 DC motors, and a motor driver, to allow for smooth and responsive wheelchair operation.

Smart Gloves for Blind Person using Ultrasonic Sensor &amp; Arduino Nano

This paper presents the design and development of smart gloves aimed at enhancing the mobility and independence of blind individuals. The proposed system integrates an Arduino Nano microcontroller with an ultrasonic sensor to detect obstacles in the user's path. The ultrasonic sensor continuously scans the surroundings and provides real-time feedback to the wearer through haptic feedback mechanisms incorporated into the gloves. The system employs vibration patterns to convey the proximity and location of obstacles, allowing the user to navigate safely and confidently in various environments. Additionally, the compact and lightweight nature of the Arduino Nano ensures portability and ease of use. The effectiveness of the smart gloves was evaluated through user testing, demonstrating promising results in improving the mobility and autonomy of visually impaired individuals. This innovative solution has the potential to significantly enhance the quality of life for blind individuals by providing them with greater freedom of movement and increased safety in their daily activities. Keywords: Ultrasonic Sensors Arduino Nano, Vibrator motor, Speaker, Connecting wires, JQ6500 voice module.

Creating an autoencoder single summary metric to assess gait quality to compare surgical outcomes in children with cerebral palsy: The Shriners Gait Index (SGI)

Gait for individuals with movement disorders varies widely and the variability makes it difficult to assess outcomes of surgical and therapeutic interventions. Although specific joints can be assessed by fewer individual measures, gait depends on multiple parameters making an overall assessment metric difficult to determine. A holistic, summary measure can permit a standard comparison of progress throughout treatments and interventions, and permit more straightforward comparison across varied subjects. We propose a single summary metric (the Shriners Gait Index (SGI)) to represent the quality of gait using a deep learning autoencoder model, which helps to capture the nonlinear statistical relationships among a number of disparate gait metrics. We utilized gait data of 412 individuals under the age of 18 collected from the Motion Analysis Center (MAC) at the Shriners Children’s - Chicago. The gait data includes a total of 114 features: temporo-spatial parameters (7), lower extremity kinematics (64), and lower extremity kinetics (43) which were min–max normalized. The developed SGI score captured more than 89% variance of all 144 features using subject-wise cross-validation. Such summary metrics holistically quantify an individual’s gait which can then be used to assess the impact of therapeutic interventions. The machine learning approach utilized can be leveraged to create such metrics in a variety of contexts depending on the data available. We also utilized the SGI to compare overall changes to gait after surgery with the goal of improving mobility for individuals with gait disabilities such as Cerebral Palsy.

Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis: a cross-sectional study

BackgroundIncorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis.MethodsThis is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants’ kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied.ResultsCompared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively).ConclusionThis study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

Broomcorn Millet

Broomcorn millet (Panicum miliaceum) was one of the most important and enigmatic crops of the ancient world. The integration of millet into existing crop systems drove significant transformations in past societies. Thanks to the environmental adaptability and short growing period of millet, many societies across Eurasia were dependent on millet cultivation for food security. For modern researchers, broomcorn millet also possesses unique botanical and biochemical characteristics that make it an ideal candidate for tracing its ancient dispersal and integration, which in turn provides a unique avenue for understanding the broader mechanisms of dietary transformations. This paper offers a review of the multiproxy evidence for the initial broomcorn millet dispersal across Eurasia. In light of millet’s unique biomolecular properties, multiple archaeological examples are drawn on to describe how millet consumers can be traced down to demographic categories of sex, age, social status, and individual mobility history. In combination with other research methods, this paper reviews evidence for past millet preparation for human consumption, using various archaeological sites as case studies, along with offering a theoretical reasoning for the discontinuities in millet exploitation over time, which is likely to be the result of past climate change.

Shaping the Future of Transportation with Automation

This study explores the advancements and challenges of AI-powered self-driving cars, specifically in the context of urban planning, traffic management, and transportation systems. It investigates the technological components of autonomous vehicles, including computer vision, machine learning algorithms, sensor fusion, and real-time decision-making systems. The research further delves into the training and learning procedures, focusing on the use of large datasets, deep neural networks, and reinforcement learning to continuously enhance driving capabilities through interaction with the environment. The goal is to assess the potential of AI to improve road safety, transit efficiency, and individual mobility, while acknowledging the obstacles that need to be overcome for widespread adoption and societal trust.

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

In this paper, two machine learning algorithms—local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN)—that are used to identify outliers in the context of a continuous framework for point of interest (PoI) detection are analyzed. The mobile trajectories of users are continuously and almost instantaneously loaded into this system. These frameworks are still in their infancy, but they are already essential for large-scale sensing deployments, such as Smart City planning deployments, where the anonymous individual mobile user trajectories can be valuable to improve urban planning. There are two contributions made by this paper. First, the functional design of the entire PoI detection architecture is provided by the study. Second, the study evaluates the effectiveness.

Analyzing Movement Densities in AlKarkh Districts: A Comparative Study

In recent years, many city centers have experienced severe traffic congestion due to focusing on personal transportation, with cars being the easiest and fastest means of travel. The excessive use of cars has led to neglect of social and environmental aspects, further exacerbating the issue. Historical city centers are particularly affected as they are often commercial hubs with an organic network of roads that increase the difficulty of access and movement. As a result, congestion has intensified, and cars have become the only means of transportation between different parts of the city. This research aims to analyze the effects of traffic congestion in city centers by focusing on a case study of AlKarkh. The study relied on observing and collecting information available at the site, calculating movement densities during peak times on five busy days, and analyzing the data. Additionally, the study compared the results with data from 2014 to assess changes in movement densities. The findings from this research can provide evidence for urban planning that aims to develop the road network in historical city centers and improve transportation policies.

Easiness and amount. Contribution of digitalization to the act of listening and its possibilities

ABSTRACT This research focuses on the social-spatial relationship between device and subject and identifies the possibilities that digital technology lets listeners perform as listening modalities, such as individual listening, mobility, and multimedia listening. Analyzing the most relevant specialized literature, this work explores the socio-technological origins of current possibilities of listening to classify them as ‘innovative change’ or ‘substantial improvement’. A sound reproduction device timeline and their contributions determine what the enrichment of the digital era to the act of listening is and what reasonable future trends are to be expected. The digital era does not add any new possibility to the social-spatial act of listening itself. Nevertheless, the digital era has prepared the technological basis for a new leap that would tend toward hyperpersonalization and the maximization of operative easiness. This research shows how digital has made great progress ‘inside’ devices functioning, but not so much ‘outside’ them.

Determinants of electromobility development from the perspective of a zero emission, innovative and resilient economy

The aim of this article is to identify the critical factors and assess the specific actions conditioning the development of electromobility from the perspective of a zero-carbon, innovative and resilient economy. These issues have a particular dimension in relation to individual mobility. The study used a combination of primary and secondary data, using various research methods and techniques, such as descriptive analysis, desk research, diagnostic survey, cause-and-effect analysis and statistical analysis. Additionally, in-depth interviews were conducted with experts in managerial positions. The literature review and the results of our own research confirmed the importance of the identified factors in the uptake of electric cars. At the same time, the study highlighted the high complexity of problems regarding investment decisions determining the development of electromobility. Taking active steps to increase the level of sustainability and resilience of the electromobility system should first focus on further development of charging infrastructure, uptake of electric vehicles, development of renewable energy sources and creation of an electric vehicle battery value chain. The main expectations for the development of electromobility are to reduce CO2 emissions, reduce dependence on fossil fuel supplies, increase the competitiveness and innovation of the economy and reduce external costs generated by transport. Attempts were made to achieve the originality of the research carried out through its measurable nature. The proposed electromobility development model may contribute to the improvement of decision-making tools regarding the allocation of public funds and other sources for investments so that they contribute to the sustainable development of mobility systems.

The Interplay Between Individual Mobility, Health Risk, and Economic Choice: A Holistic Model for COVID-19 Policy Intervention

This paper was motivated by the need to simultaneously address two competing policy objectives during the course of the COVID pandemic: namely, the public health objective, which required people to be less mobile, and the economic objective, which aimed to ensure that the economy was not adversely affected by the constraints imposed by the first objective. To realize these objectives, we developed a data-informed approach to model human mobility, health risk, and economic activity jointly. This approach computes equilibrium between epidemic models of public health and economic activity under policy interventions that could be used to change people’s mobility behavior. Our approach is distinctive in its capacity to assemble proprietary data sets from public and private sectors at the individual and the zip code levels, which heretofore had not been used together. These data enabled customization of the population-level epidemic models widely used in public health (e.g., the SIR model) with individual-level data traces of mobility behaviors for assessment of public health risks. The outputs of the proposed model enabled parameterization of economic choice models of individuals’ economic decision-making. Various policy interventions and their capacities to shift the equilibrium between economic activity and public health were investigated in this study. Whereas the data-informed joint modeling approach was developed and tested in the pandemic context, it is generalizable for the evaluation of any counterfactual policy interventions. History: Olivia R. Liu Sheng and W. Nick Street served as senior editors for this article. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.4390192.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0013 ).

Juxtaposing individual and group mobility from sparse Wi-Fi signatures with cloud-assisted computing: a case study for a multidisciplinary university campus

ABSTRACT Understanding human mobility undoubtedly enriches common goods. Among available location-aware technologies, Wi-Fi provides a more sustainable and high-resolution means to study human mobility patterns as it has become conspicuous and affordable recently. Whilst existing studies have shed light on facets of personal mobility, the intricate dynamics of group mobility have garnered comparatively scant empirical scrutiny in real-world settings, especially in the juxtaposition with individual mobility. Moreover, they have often overlooked the multifaceted nature of personal attributes influencing daily routines. This study introduces a comprehensive framework that takes advantage of the readily available Wi-Fi connection data and cloud-assisted computing for juxtaposing individual and group mobility. The framework was tested on auniversity campus that provides representative human mobility patterns with diverse attributes. Two tests that aim to demonstrate the framework’s capability to (1) capture individual mobility patterns by using data processing amenable to the spatiotemporal sparseness and to formulate group mobility and (2) differentiate quantitatively the spatiotemporal signatures of distributions, night activities, transitions, and network topologies were conducted. This study uncovers distinct disparities between individuals and groups, and heterogeneities among different attributes in both an empirical and real-world scenario with a reduction in computation time to approximately 6% of the baseline.

The Allegory of the Favela: The Multifaceted Effects of Socioeconomic Mobility

What are the consequences of upward socioeconomic mobility for disenfranchised individuals? This article examines this question in the context of a business training program offered to residents of Brazilian urban slums, known as “favelas.” The study employs a randomized controlled trial complemented by quantile regressions, field visits, and interviews. The results show that training improves favela dwellers’ economic outcomes, such as by increasing income and participation in entrepreneurship, and some socio-psychological outcomes, such as by improving self-efficacy and optimism. However, these income improvements were accompanied by participants’ enhanced experiences of favela stigma, an adverse socio-psychological outcome related to their residential segregation. Both quantitative and qualitative findings demonstrate the multifaceted nature of socioeconomic mobility, through which favela dwellers who prosper economically become more exposed to prejudice from people living outside favelas. The study illustrates, through the “allegory of the favela,” the bittersweet process of socioeconomic mobility. This abductive research contributes to the literature by showing that while interventions designed to enfranchise individuals may effectively achieve economic inclusion in terms of income gains, they may simultaneously lead participants into discriminatory systems that further stigmatize people based on the same characteristics of their prior exclusion.

Experimental Study of Persons with Disability, Wheelchairs, and Car Interaction

Driving a car independently significantly enhances the quality of life, particularly for individuals with mobility disabilities. Wheelchair users face several challenges when loading and unloading their wheelchairs into and from vehicles, hindering their personal transportation independently. The influence of wheelchair design on the wheelchair and car compatibility for self-loading and unloading hasn't been fully explored in the scientific literature; therefore, this study investigates the impact of various design features found in modern wheelchairs on their suitability for disabled drivers. Four types of wheelchairs were compared in this study; two participants with paraplegia, one male and one female, participated in the tests. Video recordings captured the procedures for getting in and out of the vehicle. Subsequent results analysis involved breaking down these procedures into steps and comparing them. The study demonstrated that times of independent entry into a car and loading of a wheelchair ranged from 48 to 74 seconds, while exiting the vehicle and loading a wheelchair phase took between 56 and 86 seconds. A key takeaway from this research is that dividing the wheelchair frame, the largest and heaviest component, into two parts facilitates loading and unloading processes. However, this may slightly increase the duration of these activities. Detaching the seat aids more individuals with limited upper body strength.

Inferior patellar mobility before and after knee arthroplasty: A comparison with healthy knees.

The aim of this study was to evaluate patellar mobility in patients before and after knee arthroplasty (KA) and compare it with that of healthy subjects. It was hypothesisedthat patellar mobility is diminished in patients with osteoarthritis (OA) and remains unchanged after KA. A total of 101 patients (59 females and 42 males) with a mean age of 70.9 ± 9.9 years underwent KA and were compared with 25 healthy individuals (seven females and 18 males) with a mean age of 32.3 ± 9.3 years. Mediolateral patellar displacement was measured by applying a force of 10 N, and the medial and lateral patellar shifts were recorded separately using a validated novel patellostabilometer. Patients were examined preoperatively and at 3 months postoperatively, assessing the range of knee motion and the clinical and functional status based on the Oxford Knee Score (OKS), Kujala Score, subjective Knee Society Score (sKSS), Forgotten Joint Score (FJS)and Western Ontario and McMaster Universities Arthritis Index (WOMAC). Total patellar displacement was 42.1 ± 6 mm for healthy subjects, 31 ± 6 mm before surgeryand 32.8 ± 7.8 mm after surgery in osteoarthritic patients (p < 0.01). The mean lateral patellar shift differed significantly between healthy individuals (17.9 ± 4 mm) and osteoarthritic patients (15.1 ± 6 mm) (p < 0.01). The mean medial patellar mobility of healthy individuals (24.2 ± 7 mm) was significantly greater than that of osteoarthritic patients (15.8 ± 4.8 mm) (p < 0.01). All scores improved significantly postoperatively. No correlation was found between patellar mobility and OKS, Kujala Score, sKSS, FJSand WOMAC (r = -0.11). Improvement in patellar mobility also showed no correlation with clinical outcomes according to OKS, Kujala Score, sKSS, FJSand WOMAC (r = 0.08). This study has demonstrated reduced patellar mobility in patients with OA. While patellar mobility significantly improved after KA, it may not hold clinical significance (p = 0.04). No impact on clinical outcome can be expected when the presurgical patella mobility is preserved in KA. Level IV.

A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment.

Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.

Examining the directionality of mobility patterns in activity spaces: Introducing the ‘mobility snowflake’ visual analytic and measurement framework

Activity spaces characterise an individual's mobility patterns and provide critical insights important to a broad suite of applications. Emergent forms of disaggregate space-time data offer new opportunities to exploit a more granular approach to measuring and visualising activity spaces. Drawing on individual GPS trajectories for 365 participants covering a seven-day period the current paper presents a new measurement and visualisation framework examining the directionality of mobility patterns. This framework moves beyond existing work that measures activity spaces via a simple geometric shape (traditionally a convex hull) to introduce a new metric and associated visual analytic capturing a new dimension of the concept. Results have important consequences for policy and planning, with the hope that the framework can be redeployed across various situational and cultural contexts, leading to the creation of an expanding collection of comparative studies.