A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with fewer annotated data. This review investigates how machine learning addresses data heterogeneity in HAR by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.

Exploiting the features of deep residual network with SVM classifier for human posture recognition

Over the last decade, there have been a lot of advances in the area of human posture recognition. Among multiple approaches proposed to solve this problem, those based on deep learning have shown promising results. Taking another step in this direction, this paper analyzes the performance of deep learning-based hybrid architecture for fall detection, In this regard, the fusion of the residual network (ResNet-50) deep features with support vector machine (SVM) at the classification layer has been considered. The proposed approach outperforms the existing methods yielding an accuracy of 98.82%, 97.95%, and 99.98% on three datasets i.e. Multi-Camera Fall (MCF) using four postures, UR Fall detection (URFD) using four postures, and UP-Fall detection (UPFD) using four postures respectively. It is important to mention that the existing methods achieve accuracies of 97.9%, 97.33%, and 95.64% on the MCF, URDF and UPFD datasets, respectively. Moreover, we achieved 100% accuracy on the UPFD two-posture task. The URFD and MCF datasets have been utilized to assess the fall detection performance of our method under a realistic environment (e.g. camouflage, occlusion, and variation in lighting conditions due to day/night lighting variation). For comparison purposes, we have also performed experiments using six state-of-the-art deep learning networks, namely; ResNet-50, ResNet-101, VGG-19, InceptionV3, MobileNet, and Xception. The results demonstrate that the proposed approach outperforms other network models both in terms of accuracy and time efficiency. We also compared the performance of SVM with Naive Bayes, Decision Tree, Random Forest, KNN, AdaBoost, and MLP used at the classifier layer and found that SVM outperforms or is on par with other classifiers.

Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments

A little explored area of human activity recognition (HAR) is in people operating in relation to extreme environments, e.g., mountaineers. In these contexts, the ability to accurately identify activities, alongside other data streams, has the potential to prevent death and serious negative health events to the operators. This study aimed to address this user group and investigate factors associated with the placement, number, and combination of accelerometer sensors. Eight participants (age = 25.0 ± 7 years) wore 17 accelerometers simultaneously during lab-based simulated mountaineering activities, under a range of equipment and loading conditions. Initially, a selection of machine learning techniques was tested. Secondly, a comprehensive analysis of all possible combinations of the 17 accelerometers was performed to identify the optimum number of sensors, and their respective body locations. Finally, the impact of activity-specific equipment on the classifier accuracy was explored. The results demonstrated that the support vector machine (SVM) provided the most accurate classifications of the five machine learning algorithms tested. It was found that two sensors provided the optimum balance between complexity, performance, and user compliance. Sensors located on the hip and right tibia produced the most accurate classification of the simulated activities (96.29%). A significant effect associated with the use of mountaineering boots and a 12 kg rucksack was established.

Personalized Activity Recognition with Deep Triplet Embeddings

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks.

Marine Adaptive Sampling Scheme Design for Mobile Platforms under Different Scenarios

Marine adaptive sampling is a technique that makes full use of limited observation resources by selecting the optimal positions. Recently, the design of an adaptive sampling scheme based on a mobile platform has become a research hotspot. However, adaptive sampling system involves multiple subsystems, and the attributes as well as tasks are always different, which may lead to different sampling scenarios. A great deal of research has been conducted for specific situations, especially with fixed starting and ending points. However, systematic design and simulation experiments under various circumstances are still lacking. How to design the adaptive observation system, so as to cope with the observation task under different scenarios, is still a problem worth studying. Aiming to solve this problem, we designed a systematic scheme design process. The process includes setting up and verifying the background field, adopting the hierarchical optimization framework to adapt to different circumstances, and variable adjustments for twin frames. The needs covered in this paper include not having a fixed starting point and ending point, only having a fixed starting point, having a fixed starting point and ending point, increasing sampling coverage, and simple obstacle avoidance. Finally, the relevant conclusions are applied to the multi-platform simultaneous observation scenario. It provides a systematic flow pattern for designing adaptive sampling scheme of mobile platforms.

Denetimsiz Anomali Tespiti Yaklaşımı ile Düşme Algılama

Yaşlı nüfusunun hızla artması ve yaşlılığa bağlı olarak karşılaşılan fiziksel, duyusal ve bilişsel gerilemeler, düşmeyi her geçen gün büyüyen bir problem olarak karşımıza çıkarmakta ve düşme tespiti çalışmalarının hız kazanmasına sebep olmaktadır. Günlük aktivitelerin düşmeden ayırt edilmesinden ibaret olan düşme tespiti probleminde, denetimli öğrenme yaklaşımları kullanılmasına rağmen, düşmenin nadir rastlanan ve çok farklı biçimlerde karşılaşılabilen bir olay olması genel bir model elde edilmesine izin vermemektedir. Bu çalışmada denetimsiz anomali tespiti ile düşmenin belirlenmesi önerilmektedir. Denetimsiz öğrenme modelinin elde edilmesinde ve model vasıtasıyla düşmenin tespitinde 35 tip düşme ve 44 tip günlük aktiviteye sahip kapsamlı bir veri setinden faydalanılmıştır. Denetimsiz öğrenme yöntemi olan Gauss karışım modelinin eğitiminde, günlük aktivitelerden toplanan 3-eksen ivmeölçer sinyallerinden elde edilen öznitelikler kullanılmıştır. Test aşamasında model, düşme ve günlük aktivite verileri ile karşılaşmış, modele göre olasılığı çok düşük olan veriler anomali, dolayısıyla düşme olarak kabul edilmiştir. Testlerde düşmeler %90,5 civarında doğru olarak tespit edilmiştir. Sonuçlar düşmenin anomali tespiti yaklaşımları ile belirlenebileceğini ve makine öğrenmesi modelinin elde edilmesi için yalnız günlük aktivite verilerinin yeterli olduğu yaklaşımını doğrulamaktadır.

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

Improving enterprises' independent innovation capability is critical to improving their competitive strength, industries' independent innovation capability, industries' international competitiveness, and countries' independent innovation capability, as well as building an innovative country. It is now the era of strategic innovation. Many growing businesses are focused on strategic innovation, but they overlook the issue of ensuring that strategic innovation is implemented. Management and innovation are perennial themes in the long-term development of businesses. The most pressing issue in enterprises' strategic innovation activities is how to combine their strategic direction with their superior ability and choose a strategic innovation path that is appropriate for their development. This paper uses data mining theory to establish the K-means clustering algorithm to identify the best strategic orientation of enterprise innovation strategic direction selection based on the existing advantages and capabilities of enterprises based on the analysis of the direction of enterprise strategic innovation.

Identifying and Monitoring the Daily Routine of Seniors Living at Home

As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers’ interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.

COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.

Effect of Aberrant Long Noncoding RNA on the Prognosis of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a kind of lethal cancer. Although there are mature treatment methods, there is still a lack of rigorous and scientific means for cancer diagnosis. Long noncoding RNAs (lncRNAs) are a kind of noncoding RNA (ncRNA). Recent studies find that alteration of lncRNA expression is related to the occurrence of many cancers. In order to find lncRNAs which can effectively predict the prognosis of ccRCC, RNA-seq count data and clinical information were downloaded from TCGA-KIRC, and gene expression profiles from 530 patients were included. Then, K-means was used for clustering, and the number of clusters was determined to be 5. The R-package "edgeR" was used to perform differential expression analysis. Subsequently, a risk model composed of 10 lncRNA biomarkers significantly related to prognosis was identified via Cox and LASSO regression analyses. Then, patients were divided into two groups according to the model-based risk score, and then, GSEA pathway enrichment was performed. The results showed that metabolism- and mTOR-related pathways were activated while immune-related pathways were inhibited in the high-risk patients. Combined with previous studies, it is believed that these 10 lncRNAs are potential targets for the treatment of ccRCC. In addition, Cox regression analysis was used to verify the independence of the risk model, and as results revealed, the risk model can be used to independently predict the prognosis of patients. In conclusion, our study found 10 lncRNAs related to the prognosis of ccRCC and provided new ideas for clinical diagnosis and drug development.

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

An Online Classification Method for Fault Diagnosis of Railway Turnouts

Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.

Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition

Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.

FilterK: A new outlier detection method for k-means clustering of physical activity

In this paper, a new algorithm denoted as FilterK is proposed for improving the purity of k-means derived physical activity clusters by reducing outlier influence. We applied it to physical activity data obtained with body-worn accelerometers and clustered using k-means. We compared its performance with three existing outlier detection methods: Local Outlier Factor, Isolation Forests and KNN using the ground truth (class labels), average cluster and event purity (ACEP). FilterK provided comparable gains in ACEP (0.581 → 0.596 compared to 0.580-0.617) whilst removing a lower number of outliers than the other methods (4% total dataset size vs 10% to achieve this ACEP). The main focus of our new outlier detection method is to improve the cluster purities of physical activity accelerometer data, but we also suggest it may be potentially applied to other types of dataset captured by k-means clustering. We demonstrate our method using a k-means model trained on two independent accelerometer datasets (training n = 90) and re-applied to an independent dataset (test n = 41). Labelled physical activities include lying down, sitting, standing, household chores, walking (laboratory and non-laboratory based), stairs and running. This type of clustering algorithm could be used to assist with identifying optimal physical activity patterns for health.

A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering

Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a device’s pose in the procedure of using a smartphone to perform the PDR algorithm, this study proposes a novel heading estimation solution by calculating the integral of acceleration along the direction of the user’s movement. First, a lightweight algorithm, that is, a finite state machine (FSM)-decision tree (DT), is used to monitor and recognize the device mode, and the characteristics of the gyroscope at the corners are used to improve the heading estimate performance during the linear phase. Moreover, to solve the problem of heading angle deviation accumulation on positioning, a map-aided particle filter (PF) and behavior perception techniques are introduced to constrain the heading and correct the trajectory through the wall after filtering. The results indicate that the recognition of phone pose can be 93.25%. The improved heading estimation method can achieve higher stability and accuracy than the traditional step-wise method. The localization error can reduce to approximately 2.2 m when the smartphone is held at certain orientations.

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

Consequences of chronic diseases and other limitations associated with old age - a scoping review

BACKGROUND

The phenomenon of the increasing number of ageing people in the world is arguably the most significant economic, health and social challenge that we face today. Additionally, one of the major epidemiologic trends of current times is the increase in chronic and degenerative diseases. This paper tries to deliver a more up to date overview of chronic diseases and other limitations associated with old age and provide a more detailed outlook on the research that has gone into this field.

METHODS

First, challenges for seniors, including chronic diseases and other limitations associated with old age, are specified. Second, a review of seniors' needs and concerns is performed. Finally, solutions that can improve seniors' quality of life are discussed. Publications obtained from the following databases are used in this scoping review: Web of Science, PubMed, and Science Direct. Four independent reviewers screened the identified records and selected relevant publications published from 2010 to 2017. A total of 1916 publications were selected. In all, 52 papers were selected based on abstract content. For further processing, 21 full papers were screened."

RESULTS

The results indicate disabilities as a major problem associated with seniors' activities of daily living dependence. We founded seven categories of different conditions - psychological problems, difficulties in mobility, poor cognitive function, falls and incidents, wounds and injuries, undernutrition, and communication problems. In order to minimize ageing consequences, some areas require more attention, such as education and training; technological tools; government support and welfare systems; early diagnosis of undernutrition, cognitive impairment, and other diseases; communication solutions; mobility solutions; and social contributions.

CONCLUSIONS

This scoping review supports the view on chronic diseases in old age as a complex issue. To prevent the consequences of chronic diseases and other limitations associated with old age related problems demands multicomponent interventions. Early recognition of problems leading to disability and activities of daily living (ADL) dependence should be one of essential components of such interventions.

Classifying Diverse Physical Activities Using "Smart Garments"

Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.

False Positive RFID Detection Using Classification Models

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

K-means clustering of overweight and obese population using quantile-transformed metabolic data

OBJECTIVE

Use of K-means clustering for big data technology to cluster an overweight and obese population metabolically.

METHODS

K-means clustering with the help of quantile transformation of attribute values was applied to overcome the impact of the considerable variation in the values of obesity attributes involving outliers and skewed distribution.

RESULTS

Overall, 447 subjects were categorized into six clusters; metabolically normal, mild, and severe categories. There were clearly separated metabolically normal Cluster 1 and severe Cluster 2, as well as intermediate Cluster 3, 4, and 5 that had profiles of fewer attributes with abnormal values. Cluster 3 was characteristic of sole hypertension. Cluster 3 and 4 exhibited contrasting HDL-C and LDL-C levels despite similarly elevated total cholesterol. Cluster 6 with slightly elevated triglyceride was closest to the normal group. Four- and 10-quantile-transformations yielded consistent clustering results. Compared with the original data, the quantile-transformed data produced more regular and spherical clusters and evenly distributed clusters in terms of object numbers.

CONCLUSIONS

This big data analysis strategy makes use of quantile-transformation of data to overcome the issue of outliers and the irregular distribution and applies to the analysis of other non-communicable diseases.

Wear Degree Quantification of Pin Connections Using Parameter-Based Analyses of Acoustic Emissions

Pin connections are commonly used in many engineering fields, and continuous operation may cause severe wear on the pins and may lead to their eventual fracture, if undetected. However, a reliable nonintrusive real-time method to monitor the wear of pin connections is yet to be developed. In this paper, acoustic emission (AE)-based parametric analysis methods, including the logarithm of the cumulative energy (LAE), the logarithm of the slope of cumulative energy (LSCE), the b-value method, the Ib-value method, and the fast Fourier transformation (FFT), were developed to quantify the wear degree of pin connections. The b-value method offers a criterion to quickly judge whether severe wear occurs on a pin connection. To assist the research, an experimental apparatus to accelerate wear test of pin connections was designed and fabricated. The AE sensor, mounted on the test apparatus in a nondestructive manner, is capable of real-time monitoring. The micrographs of the wear of pins, and the surface roughness of pins, verified that the values of the max LAE and the max LSCE became larger as the wear degree of pin connections increased, which means different values of the max LAE and the max LSCE can reflect different wear degree of pin connections. Meanwhile, the results of the micrographs and surface roughness confirmed that the b-value is an effective method to identify severe wear, and the value “1” can be used as a criterion to detect severe damage in different structures. Furthermore, the results of spectrum analysis in the low frequency range showed that the wear frequency was concentrated in the range of 0.01 to 0.02 MHz for the pin connection. This study demonstrated that these methods, developed based on acoustic emission technique, can be utilized in quantifying the wear degree of pin connections in a nondestructive way.

Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data

Grooving is widely used to improve airport runway pavement skid resistance during wet weather. However, runway grooves deteriorate over time due to the combined effects of traffic loading, climate, and weather, which brings about a potential safety risk at the time of the aircraft takeoff and landing. Accordingly, periodic measurement and evaluation of groove performance are critical for runways to maintain adequate skid resistance. Nevertheless, such evaluation is difficult to implement due to the lack of sufficient technologies to identify shallow or worn grooves and slab joints. This paper proposes a new strategy to automatically identify airport runway grooves and slab joints using high resolution laser profiling data. First, K-means clustering based filter and moving window traversal algorithm are developed to locate the deepest point of the potential dips (including noises, true grooves, and slab joints). Subsequently the improved moving average filter and traversal algorithms are used to determine the left and right endpoint positions of each identified dip. Finally, the modified heuristic method is used to separate out slab joints from the identified dips, and then the polynomial support vector machine is introduced to distinguish out noises from the candidate grooves (including noises and true grooves), so that PCC slab-based runway safety evaluation can be performed. The performance of the proposed strategy is compared with that of the other two methods, and findings indicate that the new method is more powerful in runway groove and joint identification, with the F-measure score of 0.98. This study would be beneficial in airport runway groove safety evaluation and the subsequent maintenance and rehabilitation of airport runway.