Traj-ARIMA: A Spatial-Time Series Model for Network-Constrained Trajectory

Trajectory data play an important role in analyzing real world applications that involve movement features, e.g. natural and social phenomena such as bird migration, transportation management, urban planning and tourism analysis. Such trajectory data are a special kind of time series with another focus on the spatial dimension besides the temporal one. Traditional time series models, especially the ARIMA (Auto-Regression Integrated Moving Average) model, have provided sound theoretical backgrounds and promoted many successful applications for managing and forecasting time-relevant sequential data. This paper aims at extending the ARIMA model with spatial dimension, and further applying it for the network-constrained trajectory data. We implement and evaluate the model for trajectory database, in the context of traffic application scenario about vehicle movement constrained under a given network infrastructure. The proposed Traj-ARIMA model has many application perspectives, such as trajectory data regression and compression, outliers detection, traffic flow and vehicle speed prediction. In this paper, the major focus is on vehicle speed forecasting

Semantic Trajectories:Computing and Understanding Mobility Data

Thanks to the rapid development of mobile sensing technologies (like GPS, GSM, RFID, accelerometer, gyroscope, sound and other sensors in smartphones), the large-scale capture of evolving positioning data (called mobility data or trajectories) generated by moving objects with embedded sensors has become easily feasible, both technically and economically. We have already entered a world full of trajectories. The state-of-the-art on trajectory, either from the moving object database area or in the statistical analysis viewpoint, has built a bunch of sophisticated techniques for trajectory data ad-hoc storage, indexing, querying and mining etc. However, most of these existing methods mainly focus on a spatio-temporal viewpoint of mobility data, which means they analyze only the geometric movement of trajectories (e.g., the raw ‹x, y, t› sequential data) without enough consideration on the high-level semantics that can better understand the underlying meaningful movement behaviors. Addressing this challenging issue for better understanding movement behaviors from the raw mobility data, this doctoral work aims at providing a high-level modeling and computing methodology for semantically abstracting the rapidly increasing mobility data. Therefore, we bring top-down semantic modeling and bottom-up data computing together and establish a new concept called "semantic trajectories" for mobility data representation and understanding. As the main novelty contribution, this thesis provides a rich, holistic, heterogeneous and application-independent methodology for computing semantic trajectories to better understand mobility data at different levels. In details, this methodology is composed of five main parts with dedicated contributions. Semantic Trajectory Modeling. By investigating trajectory modeling requirements to better understand mobility data, this thesis first designs a hybrid spatio-semantic trajectory model that represents mobility with rich data abstraction at different levels, i.e., from the low-level spatio-temporal trajectory to the intermediate-level structured trajectory, and finally to the high-level semantic trajectory. In addition, a semantic based ontological framework has also been designed and applied for querying and reasoning on trajectories. Offline Trajectory Computing. To utilize the hybrid model, the thesis complementarily designs a holistic trajectory computing platform with dedicated algorithms for reconstructing trajectories at different levels. The platform can preprocess collected mobility data (i.e., raw movement tracks like GPS feeds) in terms of data cleaning/compression etc., identify individual trajectories, and segment them into structurally meaningful trajectory episodes. Therefore, this trajectory computing platform can construct spatio-temporal trajectories and structured trajectories from the raw mobility data. Such computing platform is initially designed as an offline solution which is supposed to analyze past trajectories via a batch procedure. Trajectory Semantic Annotation. To achieve the final semantic level for better understanding mobility data, this thesis additionally designs a semantic annotation platform that can enrich trajectories with third party sources that are composed of geographic background information and application domain knowledge, to further infer more meaningful semantic trajectories. Such annotation platform is application-independent that can annotate various trajectories (e.g., mobility data of people, vehicle and animals) with heterogeneous data sources of semantic knowledge (e.g., third party sources in any kind of geometric shapes like point, line and region) that can help trajectory enrichment. Online Trajectory Computing. In addition to the offline trajectory computing for analyzing past trajectories, this thesis also contributes to dealing with ongoing trajectories in terms of real-time trajectory computing from movement data streams. The online trajectory computing platform is capable of providing real-life trajectory data cleaning, compression, and segmentation over streaming movement data. In addition, the online platform explores the functionality of online tagging to achieve fully semantic-aware trajectories and further evaluate trajectory computing in a real-time setting. Mining Trajectories from Multi-Sensors. Previously, the focus is on computing semantic trajectories using single-sensory data (i.e., GPS feeds), where most datasets are from moving objects with wearable GPS-embedded sensors (e.g., mobility data of animal, vehicle and people tracking). In addition, we explore the problem of mining people trajectories using multi-sensory feeds from smartphones (GPS, gyroscope, accelerometer etc). The research results reveal that the combination of two sensors (GPS+accelerometer) can significantly infer a complete life-cycle semantic trajectories of people's daily behaviors, both outdoor movement via GPS and indoor activities via accelerometer

Computational Study of Flooding Due to Overtopping Breach of Landslide Dams

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

With increasing availability of mobile sensing devices including smartphones, online mobile data segmentation becomes an important topic in reconstructing and understanding mobile data. Traditional approaches like online time series segmentation either use a fixed model or only apply an adaptive model on one dimensional data; it turns out that such methods are not very applicable to build online segmentation for multiple dimensional mobile sensor data (e.g., 3D accelerometer or 11 dimension features like ‘mean’, ‘vari- ance’, ‘covariance’, ‘magnitude’, etc). In this paper, we design an adaptive model for segment- ing real-time accelerometer data from smartphones, which is able to (a) dynamically select suitable dimensions to build a model, and (b) adaptively pick up a proper model. In addition to using the traditional residual-style regression errors to evaluate time series segmentation, we design a rich metric to evaluate mobile data segmentation results, including (1) traditional regression error, (2) Information Retrieval style measurements (i.e., precision, recall, F-measure), and (3) segmentation time delay

OptiMoS: Optimal Sensing for Mobile Sensors

Both sensor coverage maximization and energy cost minimization are the fundamental requirements in the design of real-life mobile sensing applications, e.g., (1) deploying environ- mental sensors (like CO2, fine particle measurement) on public transports to monitor air pollution, (2) analyzing smartphone embedded sensors (like GPS, accelerometer) to recognize people daily activities. However sensor coverage and energy cost are intuitively contradictory. The higher frequency mobile sensing takes, the more energy is used; and vise versa. In this paper, we design a novel two-step mobile sensing process (“OptiMoS”) to achieve optimal mobile sensing that can effectively balance sensor coverage and energy cost. In the first step, OptiMoS divides the continuous mobile sensor readings into several segments, where the readings in one segment are highly- correlated rather than readings amongst different segments. In the second step, OptiMoS identifies optimal sampling for the sensor readings in each segment, where the selected readings can guarantee reasonably high sensor coverage with limited sampling rate. Various greedy & near-optimal segmentation and sampling methods are designed in OptiMoS, and are evaluated using real- life environmental data from mobile sensors. In this paper, we design a novel two-step mobile sensing process (``OptiMoS'') to achieve optimal mobile sensing that can effectively balance sensor converge and energy cost. In the first step, OptiMoS divides the continuous mobile sensor readings into several segments, where the readings in one segment are highly-correlated rather than readings amongst different segments. %the two neighboring segments, in terms of data modeling. In the second step, OptiMoS identifies optimal sampling for the sensor readings in each segment, where the selected readings can guarantee reasonably high sensor coverage with limited sampling rate. Various greedy \& near-optimal {\em segmentation} and {\em sampling} methods are designed in OptiMoS, and are evaluated using real-life environmental data from mobile sensors

ERICA: Enabling real-time mistake detection and corrective feedback for free-weights exercises

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Integrative profiling of metabolome and transcriptome of skeletal muscle after acute exercise intervention in mice

This study aims to explore the molecular regulatory mechanisms of acute exercise in the skeletal muscle of mice. Male C57BL/6 mice were randomly assigned to the control group, and the exercise group, which were sacrificed immediately after an acute bout of exercise. The study was conducted to investigate the metabolic and transcriptional profiling in the quadriceps muscles of mice. The results demonstrated the identification of 34 differentially expressed metabolites (DEMs), with 28 upregulated and 6 downregulated, between the two groups. Metabolic pathway analysis revealed that these DEMs were primarily enriched in several, including the citrate cycle, propanoate metabolism, and lysine degradation pathways. In addition, the results showed a total of 245 differentially expressed genes (DEGs), with 155 genes upregulated and 90 genes downregulated. KEGG analysis indicated that these DEGs were mainly enriched in various pathways such as ubiquitin mediated proteolysis and FoxO signaling pathway. Furthermore, the analysis revealed significant enrichment of DEMs and DEGs in signaling pathways such as protein digestion and absorption, ferroptosis signaling pathway. In summary, the identified multiple metabolic pathways and signaling pathways were involved in the exercise-induced physiological regulation of skeletal muscle, such as the TCA cycle, oxidative phosphorylation, protein digestion and absorption, the FoxO signaling pathway, ubiquitin mediated proteolysis, ferroptosis signaling pathway, and the upregulation of KLF-15, FoxO1, MAFbx, and MuRF1 expression could play a critical role in enhancing skeletal muscle proteolysis

Energy-efficient Continuous Activity Recognition on Mobile Phones: An Activity-adaptive Approach

Power consumption on mobile phones is a painful obsta-cle towards adoption of continuous sensing driven appli-cations, e.g., continuously inferring individual’s locomotive activities (such as ‘sit’, ‘stand ’ or ‘walk’) using the embed-ded accelerometer sensor. To reduce the energy overhead of such continuous activity sensing, we first investigate how the choice of accelerometer sampling frequency & classi-fication features affects, separately for each activity, the “energy overhead ” vs. “classification accuracy ” tradeoff. We find that such tradeoff is activity specific. Based on this finding, we introduce an activity-sensitive strategy (dubbed “A3R ” – Adaptive Accelerometer-based Activity Recogni-tion) for continuous activity recognition, where the choice of both the accelerometer sampling frequency and the clas-sification features are adapted in real-time, as an individual performs daily lifestyle-based activities. We evaluate the performance of A3R using longitudinal, multi-day observa-tions of continuous activity traces. We also implement A3R for the Android platform and carry out evaluation of energy savings. We show that our strategy can achieve an energy savings of 50 % under ideal conditions. For users running the A3R application on their Android phones, we achieve an overall energy savings of 20-25%.