Human studies often rely on wearable lifelogging cameras that capture videos of individuals and their surroundings to aid in visual confirmation or recollection of daily activities like eating, drinking, and smoking. However, this may include private or sensitive information that may cause some users to refrain from using such monitoring devices. Also, short battery lifetime and large form factors reduce applicability for long-term capture of human activity. Solving this triad of interconnected problems is challenging due to wearable embedded systems’ energy, memory, and computing constraints. Inspired by this critical use case and the unique design problem, we developed NIR-sighted, an architecture for wearable video cameras that navigates this design space via three key ideas: (i) reduce storage and enhance privacy by discarding masked pixels and frames, (ii) enable programmers to generate effective masks with low computational overhead, and (iii) enable the use of small MCUs by moving masking and compression off-chip. Combined together in an end-to-end system, NIR-sighted’s masking capabilities and off-chip compression hardware shrinks systems, stores less data, and enables programmer-defined obfuscation to yield privacy enhancement. The user’s privacy is enhanced significantly as nowhere in the pipeline is any part of the image stored before it is obfuscated. We design a wearable camera called NIR-sightedCam based on this architecture; it is compact and can record IR and grayscale video at 16 and 20+ fps, respectively, for 26 hours nonstop (59 hours with IR disabled) at a fraction of comparable platforms power draw. NIR-sightedCam includes a low-power Field Programmable Gate Array that implements our mJPEG compress/obfuscate hardware, Blindspot. We additionally show the potential for privacy-enhancing function and clinical utility via an in-lab eating study, validated by a nutritionist.

Clustering researchers in communities is an important task to support a range of techniques for analyzing and making sense of the research environment and helps re-searchers find people in the same field of interest to collaborate. In computer science, ontology is commonly used to capture knowledge about a particular area using relevant concepts and relations. This study investigates the use of overlapping community detection algorithms on a multilayered Arabic scholarly network to detect communities of researchers who share their research interests. Two researchers can share an interest if they co-authored a publication or share some keywords in their publications. The set of keywords is expanded via semantic search within a cross-domain ontology, e.g. DBpedia, allowing more researchers with indirect relationships to be connected. A 2-layer scholarly network was constructed by retrieving the scholarly data of faculty members from three colleges at Umm AlQura University (UQU) with rich Arabic publications. Four versions of this network were tested: unweighted, weighted, semantically expanded, and reduced semantically expanded. It was found that weights have an insignificant role in community detection within this study. In addition, a semantically expanded network does have better clustering potentials but only if was performed selectively. Otherwise, the expanded network might suffer from generic and non-discriminative keywords, making the community detection task more challenging. To our knowledge, this is the first investigation into detecting communities within an Arabic scholarly network.

Wearable cameras provide an informative view of wearer activities, context, and interactions. Video obtained from wearable cameras is useful for life-logging, human activity recognition, visual confirmation, and other tasks widely utilized in mobile computing today. Extracting foreground information related to the wearer and separating irrelevant background pixels is the fundamental operation underlying these tasks. However, current wearer foreground extraction methods that depend on image data alone are slow, energy-inefficient, and even inaccurate in some cases, making many tasks–like activity recognition–challenging to implement in the absence of significant computational resources. To fill this gap, we built ActiSight, a wearable RGB-Thermal video camera that uses thermal information to make wearer segmentation practical for body-worn video. Using ActiSight, we collected a total of 59 hours of video from 6 participants, capturing a wide variety of activities in a natural setting. We show that wearer foreground extracted with ActiSight achieves a high dice similarity score while significantly lowering execution time and energy cost when compared with an RGB-only approach.