A context-sensitive conceptual framework for activity modeling

Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place along the way, such as reading on the bus, are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., holidaying in a country, could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts

Atmospheric Cold Plasma: A Brief Journey and Therapeutic Applications from Wound Healing to Cancer Biology

Cold Atmospheric Plasma (CAP) has now become a well-known new edge technology in the field of biomedical science to agriculture and food technology. Ionized gas known as cold atmospheric plasma has recently been the subject of intense inquiry by scientists for its potential application for treatment in oncology and dentistry. Air, Helium, Argon, Nitrogen, and other gases can all be used to create Cold Atmospheric Plasma. Cold plasma can effectively and safely inactivate spores, bacteria, fungi, viruses, and small molecules and thereby improving wound healing, combating microbial infections, and treating skin conditions with great efficiency. Interestingly the in vitro and in vivo demonstration of CAP has shown promising applications in cancer healing and treatment. The most widely employed technique for producing and sustaining a low-temperature plasma for use in technological and scientific applications involves applying an electric field to a neutral gas. The non-equilibrium atmospheric pressure plasma jet (NAPPJ) and the dielectric barrier discharge (DBD) have both been widely used in biomedical applications. This review aims to evaluate the emerging plasma technology - the basic science, technical aspects and provide insights of biomedical application in diverse area

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Towards urban mobility-based activity knowledge discovery: interpreting motion trajectories

© 2017 Dr. Rahul Deb DasUnderstanding travel behaviour is important for an effective urban planning and to enable different context-aware mobility service provisions. To this end, it is essential to model different mobility-based activities in available trajectory data. However, the semantics of activity varies from context to context, which poses a challenge for developing a connected knowledge flow for different services. Currently, such mobility-based information is typically collected through manual paper-based surveys. These surveys preserve context, but come with their own inherent quality issues, and are expensive in comparison to data analytics methods. To address this issue this research leverages the emerging concept of smartphone-based travel surveys that collect people’s movement behaviour in terms of raw trajectories. This research proposes an ontological framework that can model activities in a hierarchical manner adapting to different contexts and thereby addressing the challenges of trajectory data analytics mentioned above. This research also explores how raw trajectories collected by a smartphone can be interpreted to generate mobility information (e.g., transport modes, trips). While interpreting the trajectories this thesis models uncertainties that may exist during people’s travel behaviour and interpretation process. In this research, a particular focus is given to knowledge representation, that is understanding urban movement behaviour from detecting transport modes in trajectories. One presented form of knowledge representation is a fuzzy logic based approach to mode detection. The knowledge representation is essential to extract semantics related to a given activity. This research also introduces the concept of near-real time mode detection and investigates the performance of a purely knowledge-driven model works effectively in a near-real time scenario. Since a knowledge-driven model at different temporal granularities while detecting a given transport mode. The knowledge-driven model that works in offline, typically requires kinematic features computed over sufficiently long segments. But in near-real time these segments must be shorter and requires the model to be adaptive. To address this issue a machine learning based model has been deployed, which can learn from the historical data, and work in varied conditions. But machine learning models work as a black box and cannot explain their reasoning scheme owing to a semantic gap in the activity knowledge base. On the other hand, a fuzzy logic based model can explain its reasoning scheme but cannot adapt to varying conditions. To bridge the trade-off between these approaches this research proposes a hybrid knowledge-driven framework that is capable of self-adaptation and explaining its reasoning scheme. The results show the hybrid model performs better than a purely knowledge-driven model and works at par with the machine learning models for transport mode detection. This research also justifies a hybrid approach can model the activity in a consistent and adaptive manner while explaining the semantics related to different mobility-based activities. In this research different uncertainties related to a motion trajectory interpretation process have been addressed. A particular focus is given on modelling the temporal uncertainties that exist between predicted, scheduled and reported trips. Such a temporal uncertainty quantification measures the reliability (or uncertainty) in an inference process in the interest of information retrieval at different contexts. Considering the lack of semantics in GPS trajectories an investigation is also made whether incorporating low sampled IMU information in addition to a GPS trajectory can improve the accuracy. This research also identifies existing trajectory segmentation approaches (e.g., clustering-based or walking-based approaches) are subjective and thus lacks adaptivity. In order to address these issues a novel state-based bottom-up trajectory interpretation model is developed, which can generate mobility information at different temporal granularities. The model also demonstrates its efficacy, flexibility, and adaptivity over the existing top-down approaches This research also demonstrates that using a GPS trajectory, it is possible to generate modal state information comparatively at a coarser granularity but shorter than the time required to generate information from a historical GPS trajectory. The response time is subject to a particular application domain. The research presented in this thesis has a potential to improve the background intelligence in smartphone-based travel surveys and smartphone-based travel applications facilitating mobility-based context-aware service provisions where the notion of activity is prevalent at different granularities. However, this research cannot distinguish composite activities, which require future work. With the emergence of Web 2.0 and ubiquitous location sensing technologies, the location information can come from various sources with the different level of inaccuracies and space-time granularities. The models developed in this research currently work best on GPS trajectories sampled at 1 Hz to 2 Hz frequency, which may be enriched with IMU information. However, the models need some adjustments and incorporations of additional features and rules when the location information comes not only from GPS but also from GSM, Wi-Fi, smart-card. The models developed in this research are flexible, transparent and offer provisions for further enrichment of raw trajectories and extract finer activity information. This research has a potential to understand mobility patterns at an aggregate and a disaggregate level, and thereby serve different application domains e.g., personalized activity recommendations during a travel, emergency service provisions, real-time traffic management and long term urban policy making

Understanding Users’ Satisfaction towards Public Transit System in India: A Case-Study of Mumbai

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score

A fuzzy logic based transport mode detection framework in urban environment

Transport mode detection is an emerging research area in different domains such as urban planning, context-aware mobile computing, and intelligent transportation systems. Current approaches are mostly data-driven, based on machine learning approaches. However, machine learning models require substantial training data and cannot explain the reasoning procedure. Data-driven approaches also fall short while interpreting trajectories where ground truth information is limited. Therefore, this paper develops a novel knowledge-based approach for interpreting smartphone global positioning system trajectories by detecting various transport modes used during travel. The proposed model is based on an expert system that can work without any training, based solely on expert knowledge. Core is a fuzzy multiple-input multiple-output expert system using kinematic and spatial information with a well explained fuzzy reasoning scheme through a fuzzy rule base. The model can provide alternate predictions with varied certainty factors. Different membership function combinations have been evaluated in terms of accuracy and ambiguity, and the result demonstrates that the model performs best using a Gaussian–Gaussian combination, comparable to the existing machine learning approaches

Exploring the potential of Twitter to understand traffic events and their locations in Greater Mumbai, India

Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter - a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment

Urban Traffic Event Detection using Twitter Data

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Towards the usefulness of user-generated content to understand traffic events

This paper explores the usefulness of Twitter data to detect traffic events and their geographical locations in India through machine learning and NLP. We develop a classification module that can identify tweets relevant for traffic authorities with 0.80 recall accuracy using a Naive Bayes classifier. The proposed model also handles vernacular geographical aspects while retrieving place information from unstructured texts using a multi-layered georeferencing module. This work shows Mumbai has a wide spread use of Twitter for traffic information dissemination with substantial geographical information contributed by the users