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Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Published in Journal of NeuroEngineering and Rehabilitation

BackgroundCompensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have ...

Context-aware rule learning from smartphone data: survey, challenges and future directions

Published in Journal of Big Data

Smartphones are considered as one of the most essential and highly personal devices of individuals in our current world. Due to the popularity of context-aware technology and recent developments in smartphones, these devices can collect and process raw contextual data about users’ surrounding environment and their corresponding behavioral activitie...

A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

Published in Journal of Big Data

A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classifica...

Scoring and classifying regions via multimodal transportation networks

Published in Applied Network Science

In order to better understand the role of transportation convenience in location preferences, as well as to uncover transportation system patterns that span multiple modes of transportation, we analyze 500 locations in the Tokyo area using properties of their multimodal transportation networks. Multiple sets of measures are used to cluster regions ...

Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. In this approach we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification ...

A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Published in Journal of Medical Systems

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, the...

SEPATH: benchmarking the search for pathogens in human tissue whole genome sequence data leads to template pipelines

Published in Genome Biology

BackgroundHuman tissue is increasingly being whole genome sequenced as we transition into an era of genomic medicine. With this arises the potential to detect sequences originating from microorganisms, including pathogens amid the plethora of human sequencing reads. In cancer research, the tumorigenic ability of pathogens is being recognized, for e...