A multimodal dataset of real world mobility activities in Parkinson’s disease

Parkinson’s disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson’s disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being “on” or “off” medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated

Epigenetic variation in lingonberries

Epigenetic variation plays a role in developmental gene regulation, response to the environment, and in natural variation of gene expression levels. The purpose of the study is to investigate cytosine methylation and secondary compounds of lingonberry (Vaccinium vitis-idaea) among cutting-propagated cultivar Erntedank (ED) and its tissue-culture plants (NC, LC). This was analyzed by using Methylation Sensitive Amplified Polymorphism (MSAP) where the primers were cleaved in cytosine residues at 5'-CCGG-3' sites in CpG-islands. In leaf regenerants (LC1), we observed highest methylated sites from all primer combinations (108 bands), with their highest variation in secondary metabolites. We measured that tissue-cultured plants showed higher methylation bands than maternal plants. For instance, we identified the mother plant ED exhibited 79 bands of methylation, which is comparatively low. On the other hand, we observed the highest total phenolic content in (NC3) but LC1 represents low phenolic content. Our study showed more methylation in micropropagated plants (NC1, NC2, NC3 and LC1) than those derived from ED cutting cultivar where methylation was not present. On the contrary, we observed higher secondary metabolites in cutting cultivar ED but comparatively less in micropropagated plants (NC1, NC2, NC3 LC1). Hence, our study confirmed that higher methylation sites observed in micropropagated plants and less amount of secondary metabolites appears

An Ellipse Fitted Training-Less Model for Pedestrian Detection

The problem of pedestrian detection has gained much popularity in the computer vision community in recent times. We have noted that the existing solutions to this problem are mostly supervised in nature. However, it is difficult to guarantee availability of labelled training data in all situations. In this paper, we propose a training-less solution of pedestrian detection. Some of the additional challenges for pedestrian detection are proper handling of viewpoint dependencies, background clutter, illumination variation and occlusion. We design an ellipse fitting model, as a part of our training-less solution, for accurate pedestrian detection. In this model, we fit an ellipse to each competing bounding box (proposal). An area and entropy based quality factor is introduced for every such (fitted) ellipse to discriminate among the proposals. We filter out proposals with low quality factors. Performance comparisons with some well-known supervised pedestrian detection approaches on publicly available PETS2009 dataset demonstrate that our solution is highly promising.</p

Multi-level threat analysis in anomalous crowd videos

Crowd anomaly detection is a challenging problem in the field of computer vision. An abnormal event in a crowd scene can be labeled as threat in a video. Several existing solutions in this area have marked video frames either normal or abnormal event. Such categorization of frames can be referred as two-class threat labeling problem. However, this notion of two-class threat labeling is not well defined in literature. An event can have multiple aspects as it can be treated as anomalous or non-anomalous based on the situation of occurrence. Based on this argument, we propose a new paradigm of extending this two class threat labeling problem to multi-class labeling. As a solution to this multi-class labeling problem, we cluster frames with low, medium and high threat. We also propose a new feature known as pseudo-entropy for better clustering of threats. Our framework consists of two main components, namely, Earth mover distance (EMD) based anomaly detection system and multi-level threat analysis. As an outcome frame-wise and segment-wise threat representation are also presented to facilitate real time video search for relevant events. Exhaustive internal comparison and statistical analysis over benchmark UCSD and UMN dataset clearly indicates the merit of the proposed framework.</p

Computer-vision-guided human pulse rate estimation:A review

Human pulse rate (PR) can be estimated in several ways, including measurement instruments that directly count the PR through contact- and noncontact-based approaches. Over the last decade, computer-vision-assisted noncontact-based PR estimation has evolved significantly. Such techniques can be adopted for clinical purposes to mitigate some of the limitations of contact-based techniques. However, existing vision-guided noncontact-based techniques have not been benchmarked with respect to a challenging dataset. In view of this, we present a systematic review of such techniques implemented over a uniform computing platform. We have simultaneously recorded the PR and video of 14 volunteers. Five sets of data have been recorded for every volunteer using five different experimental conditions by varying the distance from the camera and illumination condition. Pros and cons of the existing noncontact image- and video-based PR techniques have been discussed with respect to our dataset. Experimental evaluation suggests that image- or video-based PR estimation can be highly effective for nonclinical purposes, and some of these approaches are very promising toward developing clinical applications. The present review is the first in this field of contactless vision-guided PR estimation research.</p

ESAC:An Algorithm for Fissure Line Detection

Pulmonary fissure detection is an important step for lung lobe segmentation which is necessary for accurate diagnostics and surgical planning. Automatic detection of fissures in CT images is a challenging task due to varying intensity, pathological deformation and noisy acquisitions. In this paper, we propose a novel fissure line detection technique using eigen analysis of the hessian matrix and an exhaustive sample consensus (ESAC) based line fitting in small overlapping windows. The idea behind using the line fitting technique is that the fissure line appears as piece-wise linear segment in a small window. As opposed to RANSAC, the point selection mechanism in the proposed method chooses all combination of data points exhaustively. This approach reduces the possibility of missing the possible candidate points for a fissure line. Our main contribution lies in detection of the fissure line without using any training data as well as any template matching model. The performance of our method is validated on the publicly available LOLA11 database. Comparisons with some existing approaches on this database indicate the advantage of the proposed solution.</p