In recent years, AI has undergone significant changes. Firstly, there is a growing recognition of the need to deploy inference models based on Deep Neural Networks (DNNs) on edge devices. Secondly, there is an increasing demand for low-energy inferencing ...

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 ...

In this work, we present a lightweight attention network to perform Time Series Classification on Edge devices. We evaluate the merit of our system on a Human Activity Recognition dataset and show the demonstration with the help of a Wearable device (...

The growing need for artificial intelligence in multitude of domains has over the past few years triggered some concerns related to latency and reliability when huge amounts of sensor data are moved from sensors to the cloud for computation. These ...

The availability of “AI cameras" with both image sensors and tiny, in situ computing platforms, has opened up the possibility of running the first level vision analytics on the network edge. Vision-based edge applications, such as Automatic Face ...

Vision-based hand gesture recognition in human-computer interface design has useful applications in virtual-reality, gaming control, communication through sign language, medical rehabilitation etc. In many scenarios, such applications are deployed on ...

Smoking is a significant cause of death and deterioration of health worldwide, affecting active and passive smokers. Cessation of smoking contributes to an essential health and wellness application owing to the broad range of health problems such as ...

Customized, on-device applications that provide timely interventions about smoking episodes are very helpful for smoking cessation. For this, real-time detection of smoking puffs are necessary through unobtrusive wearable devices. This work ...

Continuous monitoring of cardiac health through single-lead wearable Electrocardiogram (ECG), is important for paroxysmal Atrial Fibrillation (AF) detection. Wearable ECG straps, watches, and implantable loop recorders (ILR) are based on this paradigm. ...

Fire-related incidents continue to be reported as a leading cause of life and property destruction. Automated fire detection and localization (AFDL) systems have grown in importance with the evolution of applied robotics, especially because use of robots ...

Resource-constrained platforms such as micro-controllers are the workhorses in embedded systems, being deployed to capture data from sensors and send the collected data to cloud for processing. Recently, a great interest is seen in the research ...

Deep Neural Networks (DNN) have made machine learning accessible to a wide set of practitioners working with field deployment of analytics algorithms over sensor data. Along with it, focus on data privacy, low latency inference, and sustainability has ...

Deep Learning architectures used in computer vision, natural language and speech processing, unsupervised clustering, etc. have become highly complex and application-specific in recent times. Despite existing automated feature engineering techniques, ...

Deep Learning (DL) has been instrumental in pushing artificial intelligence (AI)/ machine learning (ML) algorithms to edge of the network. It allows building AI/ML algorithms for computer vision, speech processing, and other timeseries analytics tasks ...

With increased focus on in situ analytics, artificial intelligence (AI) algorithms are getting deployed on embedded devices at the network edge. Growing popularity of Deep Learning (DL) and inference largely due to minimization of feature engineering, ...

Running deep learning algorithms at the edge is a necessity in many industrial use-cases, especially in applications that use robots and drones in disaster recovery, surveillance, oil & gas operations etc. Current state of the art deep learning ...

Fog Computing has in recent times captured the imagination of industrial and research organizations working on various aspects of connected livelihood and governance of smart cities. Improvements in deep neural networks imply extensive use of such ...

Of late, Cloud Robotics paradigm is being used to augment low-end robots with enhanced sensor data processing, storage and communication capabilities. In an era, where costly specialized hardware are being replaced by commodity hardware, software ...

Offloading computationally expensive Simultaneous Localization and Mapping (SLAM) task for mobile robots have attracted significant attention during the last few years. Lack of powerful on-board compute capability in these energy constrained mobile ...

In recent times, many human-centric applications are being developed to leverage the diverse range of sensors present in Android smartphones. Smartphones are also being used as edge network gateways for fusing data from multiple sensors. These ...