Sensor Data Analysis Research Articles

Bridge Monitoring with the Help of Local Distance Correlation

When analyzing sensor data, it is important to distinguish between environmental effects and actual defects of the structure. Ideally, sensor data behavior can be explained and predicted by environmental effects, for example via regression. However, this is not always the case, and explicite formulas are often missed. Then, comparing the behavior of environmental and sensor data can help to identify similarities. To do so, a classical approach is observing the correlation. Nevertheless, this only captures linear dependencies. Here, the concept of distance correlation as introduced by Székely et al. (2007) comes into play. It is not only not restricted to linear dependence, but it is also able to detect independence and does not require normality. To respond to another particularity of sensor data, that is local stationarity, we use the extension of Jentsch et al. (2020) of this concept, the so-called local distance correlation. We show different examples of application in the field of bridge monitoring from finding similarities and anomalies in sensor outputs over the determination of time spans for temperature transfer up to possible alarm concepts for long term surveillance.

Large scale monitoring of a highway bridge with remote sensing and distributed fiber optic techniques during load tests

Many bridges in western countries are at the end of their lifetime and hence Structural Health Monitoring is vital to ensure their safe operation. Load test are one way to assess the utilization grade and to decide if actions have to be taken. We report on a comprehensive monitoring campaign of load tests of a large highway bridge on the Brenner highway. The Brenner highway is the main alpine truck crossing passages from Germany to Italy. Several different remote sensing techniques like static and dynamic laser scanning, robotic total station measurements and interferometric radar were used to determine displacements of the bridge deck and girders. Strain changes and bending were observed with distributed fiber optic sensing. Furthermore, the movements of the bridge bearings were determined with IoT tilt sensors. Data was acquired during static and dynamic load tests. We demonstrate that the utilization grade of the bridge can be assessed by an integrated analysis of the different sensor data in combination with a numerical model of the structure. As a consequence of the investigations it was decided by the highway operator to close one of the three lanes of the bridge.

A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals.

Studying animal social systems requires understanding variations in contact and interaction, influenced by factors like environmental conditions, resource availability, and predation risk. Traditional observational methods have limitations, but advancements in sensor technologies and data analytics provide new opportunities. We developed a wireless wearable sensor system, "Juxta," with features such as modular battery packs and a smartphone app for data collection. A pilot study on free-living prairie voles (Microtus ochrogaster), a species with complex social behavior, demonstrated Juxta's potential for studying social networks and behavior. We propose a framework for merging temporal, spatial, and event-driven data, which can help explore complex social dynamics across species and environments.

Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring.

Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, the demand for healthcare facilities is high. Yet there is scarcity of medical experts, technicians for providing healthcare to the people affected with some medical problem. This article presents an Internet of Things (IoT) system architecture for health monitoring and how data analytics can be applied in the health sector. IoT is employed to integrate the sensor information, data analytics, machine intelligence and user interface to continuously track and monitor the health condition of the patient. Considering data analytics as the major part, we focused on the implementation of stress classification and forecasted the future values from the recorded data using sensors. Physiological vitals like Pulse, oxygen level percentage (SpO2), temperature, arterial blood pressure along with the patients age, height, weight and movement are considered. Various traditional and ensemble machine learning methods are applied to stress classification data. The experimental results have shown that a hypertuned random forest algorithm has given a better performance with an accuracy of 94.3%. In a view that knowing the future values in prior helps in quick decision making, critical vitals like pulse, oxygen level percentage and blood pressure have been forecasted. The data is trained with ML and neural network models. GRU model has given better performance with lower error rates of 1.76, 0.27, 5.62 RMSE values and 0.845, 0.13, 2.01 MAE values for pulse, SpO2 and blood pressure respectively.

ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES

This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer promising solutions to overcome these challenges. Through an analysis of case studies and applications in various environmental settings, including urban areas, industrial zones, and indoor environments, the review highlights the effectiveness of monitoring networks in enhancing spatial and temporal resolution, as well as the need for community engagement and real-time monitoring solutions. Furthermore, technological innovations such as sensor fusion, data analytics, and artificial intelligence hold great promise for improving the accuracy, reliability, and accessibility of PM2.5 monitoring data. Regulatory agencies and policymakers play a crucial role in advancing PM2.5 monitoring by harmonizing monitoring standards, strengthening quality assurance measures, and developing evidence-based regulations to mitigate air pollution and protect public health. In conclusion, international cooperation and collaboration are essential for addressing transboundary air pollution and global environmental challenges. Regional monitoring networks and international agreements provide frameworks for data sharing, standardization of monitoring practices, and collaborative research efforts. To this end, stakeholders can leverage PM2.5 monitoring by adopting new technologies, improving data quality, and supporting evidence-based actions to safeguard public health, the environment, and sustainability

Review on Different Methods for Smart Dam Operation and Water Monitoring

Abstract: This complete literature review digs into the complex world of automated dam operations and water quality management, emphasising key advances and developing techniques in this crucial subject. The study focuses on the integration of sensor technologies, IoT systems, and data analytics to explain how these advances have transformed dam management and water quality preservation. The study gives essential insights into the cutting-edge approaches used in automated dam operations and water quality management by thoroughly examining numerous studies and research findings. Discussions on the use of real-time monitoring systems to track critical water parameters, as well as the use of predictive modelling tools to detect possible problems with water quality early on, are especially relevant. This literature review not only emphasises the importance of technological advancements in improving operational efficiency and environmental stewardship, but it also lays the groundwork for future exploration and refinement of thee methodologies in the pursuit of sustainable water resource management.

REMOTE VEHICLE DIAGNOSTIC SYSTEM DEVELOPMENT BASED ON THE INTERNET OF THINGS TECHNOLOGY

Background. Advanced artificial intelligence and IoT gateways are working together in the automotive industry to predict potential vehicle problems by analysing sensor data and optimizing quality control processes. Manufacturers can detect anomalies, improve product reliability, and eliminate manufacturing defects or malfunctions in advance. Predictive analytics also lead to improved fuel efficiency, performance and overall vehicle reliability. Objective. The purpose of this work is to develop a model for remote diagnosis of vehicle faults using a Raspberry Pi model B microcomputer and a SIM7600G-H GSM module. Configure data modules, install the necessary software and configure it, demonstrate step-by-step actions, and perform diagnostics and testing of this module for data transmission. Methods. A prototype was created on the basis of Raspberry Pi 4. and provides monitoring of machine operation in remote mode using the SIM7600E-H LTE Cat-4 4G/3G module. The design has small dimensions, easy installation, requires only initial adjustment and has a wide range of improvements. Results. This prototype uses a diagnostic OBD-II car scanner ELM327 with Bluetooth connection support, a Raspberry PI 4 model B microcomputer with 8 GB of RAM, 4 USB connectors (2 ports type USB3 and 2 ports type USB2), a Gigabit Ethernet port, a USB-C power supply port, and two micro HDMI 4K display connectors. On top of the module there are 48 pins (contacts) with which you can connect modules of different types and directions. The SIM7600G-H communication module is connected to these pins. The last element of the prototype is the SIM card of one of the telephone service providers and the micro SD card, which will act as the main memory element on which the operating system will be written and data will be stored. Conclusions. The article proposes the development of a device model using Internet of Things technologies, which is capable of providing remote diagnosis of car malfunctions. This model is based on the use of the SIM7600G-H module, which provides data transmission through the mobile network. The developed model allows you to read data from various car sensors, as well as transfer this data to a remote device for further analysis. This makes it possible to quickly detect malfunctions and make timely decisions on their correction.

SURVEILLANCE CYBER-PHYSICAL SYSTEM AS A PART OF INTERNET OF VEHICLES

Integration of cyber-physical surveillance systems (CPSS) into the Internet of Vehicles (IoV) paradigm represents a transformative approach to enhancing transportation safety and efficiency. This article discusses the design, implementation, and application of CPSS as part of IoV ecosystems. Leveraging advancements in sensor technologies, communication protocols, and data analytics, CPSS within IoV enables real-time monitoring, analysis, and response to road conditions, incidents, and emergencies. Our research explores the architecture and functional capabilities of CPSS, including sensor deployment, data fusion, anomaly detection, and decision support mechanisms. We investigate the synergistic interaction between CPSS and IoV platforms, facilitating seamless data exchange, collaboration, and compatibility between automotive and infrastructural domains. Additionally, we discuss potential applications of CPSS in traffic management, law enforcement, emergency response, and urban planning, emphasizing its role in enhancing transportation safety, optimizing resource allocation, and preventing congestion and accidents. Through empirical evaluations and thematic studies, we demonstrate the effectiveness, scalability, and societal impact of integrating CPSS into IoV ecosystems. This research contributes to the development of intelligent transportation systems and underscores the transformative potential of CPSS within the IoV context.

Enhancing Maritime Safety Standards through Advanced Technologies and Professional Education

This research explores the integration of advanced technologies, such as machine learning and sensor data analysis, into maritime education to enhance safety standards. The study focuses on cadets' knowledge, attitudes, and perceptions of safety practices and examines incident data to predict and prevent maritime incidents. Findings reveal that while cadets have substantial knowledge of safety regulations, their attitudes towards safety culture need reinforcement. The alignment of education programs with industry requirements and adherence to international standards are emphasized. Additionally, the integration of technology into curricula is crucial for preparing future maritime professionals. The research underscores the need for a comprehensive approach combining technical knowledge, professional attitudes, and advanced technology to significantly improve safety standards and ensure the sustainability of the maritime industry.

Assessing Ecological Impacts and Recovery in Coal Mining Areas: A Remote Sensing and Field Data Analysis in Northwest China

In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining equilibrium, through the tumultuous phase of extraction, to the eventual restoration of stability post-reclamation. By harnessing a suite of analytical tools, including sophisticated remote sensing, UAV aerial surveys, and the meticulous ground-level sampling of flora and soil, the study meticulously measures the environmental toll of mining activities and charts the path to ecological restoration. The results are promising, indicating that the restoration initiatives are effectively healing the landscapes, with proactive interventions such as seeding, afforestation, and land rehabilitation proving vital in the swift ecological turnaround. Remote sensing technology, in particular, emerges as a robust ally in tracking ecological shifts, supporting sustainable practices and guiding ecological management strategies. This study offers a promising framework for assessing geological environmental shifts, which may guide policymakers in shaping the future of mining rehabilitation in arid and semi-arid regions.

COMPARATIVE ANALYSIS OF WIRED AND WIRELESS SENSORS IN STRUCTURAL HEALTH MONITORING

Structural Health Monitoring (SHM) systems play a critical role in ensuring the safety and longevity of civil engineering infrastructure. This paper presents a comparative analysis of wired and wireless sensor technologies within SHM applications, drawing insights from scholarly literature, expert interviews, and case studies. The significance of SHM in safeguarding infrastructure durability is emphasised, considering the substantial investment and long service lifespans associated with civil engineering structures. Thematic analysis was utilized and revealed key performance criteria such as reliability, flexibility, environmental adaptability, cost-effectiveness, and maintenance requirements. Wired sensors are lauded for their reliability and accuracy, particularly in critical infrastructure projects, while wireless sensors offer greater flexibility and ease of deployment, especially in remote monitoring scenarios. Environmental adaptability remains crucial for both sensor types, with fiber optic sensors demonstrating effectiveness in harsh conditions. Expert interviews further enrich the understanding of sensor performance, highlighting opportunities for advancements in wireless sensor technology and data analytics. The paper concludes with recommendations for future research and development efforts to address existing constraints and meet the evolving needs of SHM in civil infrastructure monitoring. Overall, this comparative analysis provides valuable insights for guiding advancements in sensor technology and SHM procedures to ensure the continued safety and integrity of essential infrastructure.

Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data

X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson’s Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.

METROLOGICAL CONTROL OF SENSORS FOR MONITORING WORKING CONDITIONS USING ARTIFICIAL INTELLIGENCE

Metrological control plays a vital role in ensuring the accuracy and reliability of the data collected as part of the working conditions monitoring. It helps to prevent potential errors and guarantee the quality of the results, which is critical for the efficient assessment and management of occupational health and safety. The article aims to investigate and analyse the role and importance of metrological control of sensors in the system for monitoring working conditions at production facilities using artificial intelligence. The article examines the possibilities of using artificial intelligence (AI) to optimise metrological control and analysis of sensor data. The authors provide specific applications of AI to improve the metrological control of sensors and identify the advantages and challenges of introducing AI into the metrological control system at production facilities. These tasks will help to reveal the essence and potential of using AI in the metrological control of sensors for monitoring working conditions and emphasise its significance in improving the safety of workers. Using artificial intelligence to improve the accuracy of sensor measurements in monitoring working conditions helps to increase the efficiency and safety of production processes and reduce health risks for employees. The metrological control methodology is essential for ensuring the reliability of sensor and measuring device measurements. Applying machine learning algorithms to develop sensor calibration models can automate and optimise the processes of measuring working conditions, improving the accuracy and reliability of data. The proposed flowchart demonstrates an innovative approach to calibrating a sound level meter using artificial intelligence (AI). The results show that integrating AI into the occupational health and safety management system contributes to monitoring process automation, predicting risks and hazards to employee health, and optimising safety processes. These approaches can enhance the production processes’ efficiency, safety, and productivity. Keywords: measuring instruments, production environment, hazards, inspection, calibration.

Identification of a Person in a Trajectory Based on Wearable Sensor Data Analysis.

Human trajectories can be tracked by the internal processing of a camera as an edge device. This work aims to match peoples' trajectories obtained from cameras to sensor data such as acceleration and angular velocity, obtained from wearable devices. Since human trajectory and sensor data differ in modality, the matching method is not straightforward. Furthermore, complete trajectory information is unavailable; it is difficult to determine which fragments belong to whom. To solve this problem, we newly proposed the SyncScore model to find the similarity between a unit period trajectory and the corresponding sensor data. We also propose a Likelihood Fusion algorithm that systematically updates the similarity data and integrates it over time while keeping other trajectories in mind. We confirmed that the proposed method can match human trajectories and sensor data with an accuracy, a sensitivity, and an F1 of 0.725. Our models achieved decent results on the UEA dataset.

A Healthcare System Employing Lightweight CNN for Disease Prediction with Artificial Intelligence

Introduction/Background This research introduces the EO-optimized Lightweight Automatic Modulation Classification Network (EO-LWAMCNet) model, employing AI and sensor data for forecasting chronic illnesses within the Internet of Things framework. A transformative tool in remote healthcare monitoring, it exemplifies AI's potential to revolutionize patient experiences and outcomes. This study unveils a novel Healthcare System integrating a Lightweight Convolutional Neural Network (CNN) for swift disease prediction through Artificial Intelligence. Leveraging the efficiency of lightweight CNN, the model holds promise for revolutionizing early diagnosis and enhancing overall patient care. By merging advanced AI techniques, this healthcare model holds the potential for revolutionizing early diagnosis and improving overall patient care. Materials and Methods The Lightweight Convolutional Neural Network (CNN) is implemented to analyze sensor data in real-time within an Internet of Things (IoT) framework. The methodology also involves the integration of the EO-LWAMCNet model into a cloud-based IoT ecosystem, demonstrating its potential for reshaping remote healthcare monitoring and expanding access to high-quality care beyond conventional medical settings. Results Utilizing the Chronic Liver Disease (CLD) and Brain Disease (BD) datasets, the algorithm achieved remarkable accuracy rates of 94.8% and 95%, respectively, showcasing the robustness of the model as a reliable clinical tool. Discussion These outcomes affirm the model's reliability as a robust clinical tool, particularly crucial for diseases benefiting from early detection. The potential transformative impact on healthcare is emphasized through the model's integration into a cloud-based IoT ecosystem, suggesting a paradigm shift in remote healthcare monitoring beyond traditional medical confines. Conclusion Our proposed model presents a cutting-edge solution with remarkable accuracy in forecasting chronic illnesses. The potential revolutionization of remote healthcare through the model's integration into a cloud-based IoT ecosystem underscores its innovative impact on enhancing patient experiences and healthcare outcomes.

Monitoring Equipment Malfunctions in Composite Material Machining: Acoustic Emission-Based Approach for Abrasive Waterjet Cutting

This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process.

Spatiotemporal Assessment of Water Pollution for Beira Lake, Sri Lanka

Beira Lake, located in Colombo, Sri Lanka, has suffered severe anthropogenic impacts, with previous restoration attempts failing due to a limited understanding of pollutant dynamics. Aiming to fill this gap, a comprehensive study was conducted during dry and wet seasons to assess the spatiotemporal water pollution of Beira Lake, employing key physicochemical parameters, numerical indices, and remote sensing analysis. The water pollution index (WPI) results categorize Beira Lake as highly polluted, with WPI values ranging from 2.38 ± 0.92 in the wet season to 2.53 ± 1.32 in the dry season. Comparatively higher COD levels recorded in the Beira Lake network, especially for Gangarama Lake show significant pollution levels during both the dry and wet seasons, e.g., the highest COD levels, at 306.40 mg/L, were observed during the wet season. The Trophic State Index (TSI) results indicate eutrophic and hypereutrophic conditions in Beira Lake, which are particularly pronounced during the wet season. The heavy metal pollution index (HPI) results suggest elevated heavy metal concentrations in Beira Lake, especially in the wet season. Combined with field investigation results, a remote sensing data analysis between 2016 and 2023 reveals significant improvements in water transparency, suggesting positive effects of recent management interventions. Parameters demanding attention include COD, nitrate, and total phosphate levels due to their consistent exceedance of permissible limits. The PCA results of indices correlations between wet and dry seasons offer valuable insights into the complex dynamics of Beira Lake’s water quality. The study makes recommendations for restoring Beira Lake, including stringent pollution controls, regular dredging, green infrastructure implementation, implementing new rules and regulations, and community engagement.

Methane (CH4) Detection System Using The TGS2611 Sensor and MQ-4 Sensor

Abstract This research focused on cow dung digesters as measurement samples, utilizing the TGS2611 and MQ-4 sensors, both known for their affordability. The objective was to develop a low-cost gas sensor system for monitoring methane gas concentrations through a straightforward web-based data acquisition platform. The DHT11 sensor was employed to measure air temperature and humidity. The electronic sensor circuit in this research utilized a voltage divider circuit. Testing the performance of the low-cost gas sensor involved integrating it with an anaerobic digestion system. For both the TGS2611 and MQ-4 sensors, the graph illustrating the relationship between sensor voltage (volts) and methane gas concentration (ppm) formed a logarithmic or non-linear pattern. The measurement range for the system was around 4700 ppm to 6300 ppm for TGS2611 and 4000 ppm to 5100 ppm for MQ-4. The sensor sensitivity was determined as 0.5433 Volts/ppm for TGS2611 and 0.5639 Volt/ppm for MQ-4. The TGS2611 sensor exhibited a higher response to changes in methane gas concentration, while the MQ-4 sensor successfully detected methane gas within its designated range. Interestingly, the TGS2611 sensor accurately detected methane gas concentrations above its specified range. The gas sampling procedure involved automatic gas sampling in a vacuum chamber for measurement, a strategy potentially enhancing sensor robustness. This research successfully amalgamated sensor advantages, circuit control, and data analysis to realize an economical yet reliable monitoring system.

VIRTUAL POTHOLE DETECTION SYSTEM

The intersection of artificial intelligence and vehicular technology has birthed a realm of innovation, revolutionizing our engagement with the automotive sphere. This survey delves into the realm of virtual pothole detection systems, a pioneering endeavor aimed at mitigating road hazards through technological intervention. As society gravitates towards a digitalized landscape, the imperative for enhanced road safety looms large. Virtual pothole detection systems represent a concerted effort to address this pressing concern, leveraging cutting-edge advancements in sensor technology, data analytics, and vehicular communication protocols. This paper scrutinizes the evolution, methodologies, and efficacy of virtual pothole detection systems, offering insights into their operational paradigms and prospects. Amidst the backdrop of burgeoning urbanization and escalating vehicular traffic, the significance of such systems cannot be overstated. By elucidating the intricacies of virtual pothole detection systems, this survey endeavors to catalyze further research and innovation in the pursuit of safer, more resilient road networks Keywords: Virtual Pothole Detection System (VPDS), Sensor Technology, Data Analytics, Vehicular Communication, Road Safety, Pothole Detection, Accident Prevention, Road Maintenance, Artificial Intelligence.

Evaluation of Patients' Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital.

This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not. To classify these groups, the Transformer model analyzes the left gait cycle data from eight inertial sensors. The accuracy using all the sensor data was 0.836. When sensor data from the right ankle, right wrist, and left wrist were excluded, the accuracy decreased the most. When analyzing the data from these three sensors alone, the accuracy was 0.795. Further reducing the number of sensors to only the right ankle and wrist resulted in an accuracy of 0.736. This study demonstrates the potential of a neural network-based analysis of inertial sensor data for clinically assessing a patient's level of walking independence.