Internet of Things Fault Detection and Classification via Multitask Learning

This paper presents a comprehensive investigation into developing a fault detection and classification system for real-world IIoT applications. The study addresses challenges in data collection, annotation, algorithm development, and deployment. Using a real-world IIoT system, three phases of data collection simulate 11 predefined fault categories. We propose SMTCNN for fault detection and category classification in IIoT, evaluating its performance on real-world data. SMTCNN achieves superior specificity (3.5%) and shows significant improvements in precision, recall, and F1 measures compared to existing techniques.Comment: Under Review, International Conference on Embedded Wireless Systems and Networks (EWSN) 202

Recent Trend of Nanotechnology Applications to Improve Bio-accessibility of Lycopene by Nanocarrier: A Review

Lycopene, rich in red, yellow, or orange-colored fruits and vegetables, is the most potent antioxidant among the other carotenoids available in human blood plasma. It is evident that regular lycopene intake can prevent chronic diseases like cardiovascular diseases, type-2 diabetes, hypertension, kidney diseases and cancer. However, thermal processing, light, oxygen, and enzymes in gastrointestinal tract (GIT) compromise the bioaccessibility and bioavailability of lycopene ingested through diet. Nanoencapsulation provides a potential platform to prevent lycopene from light, air oxygen, thermal processing and enzymatic activity of the human digestive system. Physicochemical properties evidenced to be the potential indicator for determining the bioaccessibility of encapsulated bioactive compounds like lycopene. By manipulating the size or hydrodynamic diameter, zeta potential value or stability, polydispersity index or homogeneity and functional activity or retention of antioxidant properties observed to be the most prominent physicochemical properties to evaluate beneficial effect of implementation of nanotechnology on bioaccessibility study. Moreover, the molecular mechanism of the bioavailability of nanoparticles is not yet to be understood due to lack of comprehensive design to identify nanoparticles' behaviors if ingested through oral route as functional food ingredients. This review paper aims to study and leverage existing techniques about how nanotechnology can be used and verified to identify the bioaccessibility of lycopene before using it as a functional food ingredient for therapeutic treatments

PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing

Person re-identification is a critical privacy breach in publicly shared healthcare data. We investigate the possibility of a new type of privacy threat on publicly shared privacy insensitive large scale wearable sensing data. In this paper, we investigate user specific biometric signatures in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we propose PhysioGait, a context-aware physiological signal model that consists of a Multi-Modal Siamese Convolutional Neural Network (mmSNN) which learns the spatial and temporal information individually and performs sensor fusion in a Siamese cost with the objective of predicting a person's identity. We evaluated PhysioGait attack model using 4 real-time collected datasets (3-data under IRB #HP-00064387 and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons.Comment: Accepted in IEEE MSN 2022. arXiv admin note: substantial text overlap with arXiv:2106.1190

Considerations in Designing Human-Computer Interfaces for Elderly People

As computing devices continue to become more heavily integrated into our lives, proper design of human-computer interfaces becomes a more important topic of discussion. Efficient and useful human-computer interfaces need to take into account the abilities of the humans who will be using such interfaces, and adapt to difficulties that different users may face – such as the difficulties that elderly users must deal with. Interfaces that allow for user-specific customization, while taking into account the multiple difficulties that older users might face, can assist the elderly in properly using these newer computing devices, and in doing so possibly achieving a better quality of life through the advanced technological support that these devices offer. In this paper, we explore common problems the elderly face when using computing devices and solutions developed for these problems. Difficulties ultimately fall into several categories: cognition, auditory, haptic, visual, and motor-based troubles. We also present an idea for a new adaptive operating system with advanced customizations that would simplify computing for older users

PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology

With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature. Given the high promise of accurate PCD technologies, we develop, PALMAR, a multiple-inhabitant activity recognition system by employing efficient signal processing and novel machine learning techniques to track individual person towards developing an adaptive multi-inhabitant tracking and HAR system. More specifically, we propose (i) a voxelized feature representation-based real-time PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive Order Hidden Markov Model based multi-person tracking and crossover ambiguity reduction techniques and (iii) novel adaptive deep learning-based domain adaptation technique to improve the accuracy of HAR in presence of data scarcity and diversity (device, location and population diversity). We experimentally evaluate our framework and systems using (i) a real-time PCD collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants, (ii) one publicly available 3D LiDAR activity data (28 participants) and (iii) an embedded hardware prototype system which provided promising HAR performances in multi-inhabitants (96%) scenario with a 63% improvement of multi-person tracking than state-of-art framework without losing significant system performances in the edge computing device.Comment: Accepted in IEEE International Conference on Computer Communications 202