Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Development of an evidence-based medicine mobile application for the use in medical education

BACKGROUND: Evidence-based medicine (EBM) is a methodology that is being incorporated into more medical school curricula. Boston University School of Medicine was one of early adopters of Evidence Based Medicine in the United States. A growing concern in the medical community was that the complexities of applying EBM might be lost when students enter into their clinical rotations, thus there is a need for development of a tool to help reinforce the EBM principles. METHODS: The research team in collaboration with the designers of the Finding Information Framework, a custom-made EBM finding information tool, worked to develop a mobile application to help reinforce the framework for medical students. The app was designed with both Apple and PC operating systems in mind. Key features that were identified from current literature to provide the most user-friendly mobile application. Thus, the research team specifically utilized iOS and Android platforms as both platforms have a centralized app store, possess the highest volume of medical apps available, and are most widely used in the United States by medical students. RESULTS: The Finding Information Framework was a custom-made tool developed to guide new users of EBM, and help them to apply the principles in practice. The mobile application served an added convenience by allowing easy access and fast utilization of the EBM tools. The app was designed on an Android platform first due to its open-source OS and ease in app development to new programmers. Initially, the user-friendly web-based tool, App Inventor (AI), powered by Massachusetts Institute of Technology was evaluated to program the pilot Android app. Using both the AI Component Designer and the Block Editor, several problems were encountered in AI, such as the simplicity of the program and the lack of freedom in design. This moved the project to create the app natively and with a collaborative effort with the BU's Global App Initiative club. Initially, a wireframe was built using Balsamiq. Subsequently, the Android app was built using Android SDK and the iOS app was built in XCode with Objective C; both platforms had design sections prepared in Sketch, Adobe Photoshop and Illustrator. The last and final step was to obtain Boston University branding privileges for the app. CONCLUSION: The research team identified necessary features based on research to build a user-friendly, professional mobile application of an information mastery framework that can be used off-line. The app is called FIF as it is the title of the information mastery tool designed by BUSM EBM-VIG. With a clear mobile interface, it will be beneficial to the learning and training of medical students in EBM

Construction Ergonomic Risk and Productivity Assessment Using Mobile Technology and Machine Learning

The construction industry has one of the lowest productivity rates of all industries. To remedy this problem, project managers tend to increase personnel\u27s workload (growing output), or assign more (often insufficiently trained) workers to certain tasks (reducing time). This, however, can expose personnel to work-related musculoskeletal disorders which if sustained over time, lead to health problems and financial loss. This Thesis presents a scientific methodology for collecting time-motion data via smartphone sensors, and analyzing the data for rigorous health and productivity assessment, thus creating new opportunities in research and development within the architecture, engineering, and construction (AEC) domain. In particular, first, a novel hypothesis is proposed for predicting features of a given body posture, followed by an equation for measuring trunk and shoulder flexions. Experimental results demonstrate that for eleven of the thirteen postures, calculated risk levels are identical to true values. Next, a machine learning-based methodology was designed and tested to calculate workers\u27 productivity as well as ergonomic risks due to overexertion. Results show that calculated productivity values are in very close agreement with true values, and all calculated risk levels are identical to actual values. The presented data collection and analysis framework has a great potential to improve existing practices in construction and other domains by overcoming challenges associated with manual observations and direct measurement techniques

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field