Stress management is paramount to maintaining optimal health and well-being; stress builds up in spikes, causing problems like hypertension and anxiety, necessitating personalized interventions delivered in real-time through wearable technology. This work underscores the pivotal role of unobtrusive stress detection and presents the development of auto-generated compact models tailored for on-device inference through Neural Architecture Search (NAS). These models aim to facilitate efficient stress monitoring directly on low-power devices, representing a promising avenue for advancing personalized healthcare with continuous monitoring and effective digital interventions.

Every human being is unique and behaves differently in any given context. Standard approaches like surveys that are used today to assess human behaviour often generate subjective responses and can be administered either before or after task/activity only. This presents an opportunity to develop solutions that can monitor a human being's cognitive, affective and mental state in their current context continuously and in real time, during activities and in an unobtrusive manner. We have developed a composite system called Mind Indriya that can unobtrusively measure cognitive load (CL), anxiety and visual attention using a combination of frugal sensors like wrist wearable and webcam. The accuracies of each of the individual algorithms have been proven with cognitive load at accuracy 69.5%, anxiety at accuracy of 86 % and the detection of eye blinks as an index of visual attention at$F_{score}$of 0.91. Moreover, our system is person and task independent while state-of-the-art techniques are either task dependent or requires heavy personalization. The proposed system can be used in various real life applications like a) neuro-marketing - to understand how customer behavior towards new products or advertisement, b) online learning - to personalize contents or understand the learning outcome, c) Website/user interface design - to understand the effect of the new design on people's cognitive load, affect and attention span and therefore how effective the design is and so on.

In this paper, we present "ThermoTrak", a smartphone accessory based, real-time and accurate temperature measurement mechanism, which can be used to screen for fever, which is a manifestation of infectious diseases including the symptoms caused by SARS-CoV-2. Our system accurately identifies face and forehead region from a safe distance of one meter, calculates accurate temperature of forehead with accuracy of ±0.5° C on a linear scale. An AI based algorithm is employed for the purpose of accurately detecting the Region of interest (ROI) (Face & point near center of Forehead) and calculate the absolute temperature within 300 milliseconds.

Wearable health monitoring has become a very familiar term in today'sworld. One of the most popular means ofwearable sensing is photoplethysmogram (PPG). Due to its unobtrusive and ubiquitous nature, it is gaining popularity among people everywhere. Due to the ease of use, the utility of such technology is increasing day by day. However, in theworld of researchers, the accurate estimation of heart rate (HR) in presence of motion artefacts remains an unsolved problem due to the susceptibility of PPG signals to corruption by motion artefacts. The way in which a person fastens the device on the wrist plays an important role in the acquisition of signal from the device. While there are various research works going on in this field, there is always a trade-off between accuracy and complexity of algorithm and hardware resources. Also, in such scenarios where the sensor gets misplaced due to movements, there might be no PPG signal component available in the acquired signal data. In such cases the sophisticated denoising algorithms make no sense.

Dynamic or demand-driven service deployment in a Grid or Cloud environment is an important issue considering the varying nature of demand. Most distributed frameworks either offer static service deployment which results in resource allocation problems, or, are job-based where for each invocation, the job along with the data has to be transferred for remote execution resulting in increased communication cost. An alternative approach is dynamic demand-driven provisioning of services as proposed in earlier literature, but the proposed methods fail to account for the volatility of resources in a Grid environment. In this paper, we propose a unique peer-to-peer based approach for dynamic service provisioning which incorporates a Bit-Torrent like protocol for provisioning the service on a remote node. Being built around a P2P model, the proposed framework caters to resource volatility and also incurs lower provisioning cost.

Developing analytical applications for IoT based on sensor signal processing tends to be complicated as applications are executed as sequence of steps comprising of multiple alternative algorithms, including suitable feature extraction modules depending on the goal of the application. Experience shows that developers spend considerable time and effort in performing feature extraction and dimensionality reduction. In this paper we propose a framework based on a relevant case study which allows developers to drag and drop algorithms to create a workflow chain, automatically select the most relevant signal features for the particular analytic application using a training data set to generate a model and deploy the model for use. The method reduces the effort and cost of development which is deemed highly important for the analytics industry.