Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning--based HAR.

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances.

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR. 

/pdf/deep-learning-in-human-activity-recognition-with-wearable-2n4xie8e.pdf

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

In recent years, wearable sensors have enabled the unique mode of real-time and noninvasive monitoring to develop rapidly in medical care, sports, and other fields. Sweat contains a wide range of biomarkers such as metabolites, electrolytes, and various hormones. Combined with wearable technology, sweat can reflect human fatigue, disease, mental stress, dehydration, and so on. This paper comprehensively describes the analysis of sweat components such as glucose, lactic acid, electrolytes, pH, cortisol, vitamins, ethanol, and drugs by wearable sensing technology, and the application of sweat wearable devices in glasses, patches, fabrics, tattoos, and paper. The development trend of sweat wearable devices is prospected. It is believed that if the sweat collection, air permeability, biocompatibility, sensing array construction, continuous monitoring, self-healing technology, power consumption, real-time data transmission, specific recognition, and other problems of the wearable sweat sensor are solved, we can provide the wearer with important information about their health level in the true sense.

/pdf/wearable-sensor-for-continuous-sweat-biomarker-monitoring-n5s4bf0m.pdf

Wearable Sensor for Continuous Sweat Biomarker Monitoring

Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient’s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.

/pdf/bio-signals-in-medical-applications-and-challenges-using-9jchhn7c.pdf

Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence

The eye tracking can give a lot of useful information that may be use to design interactive systems, where the movement of the iris is used. In the present thesis, the human attitude under specific circumstances will be examined using the eye tracking glasses called Tobii Pro Glasses 2. Using this glasses, as well as Tobii Pro Glasses 2 Controller software, the researcher has the opportunity to track the focus of the eye, when the user surfs in different web pages. The controller software saves the eye movement in a video file. In order to extract more information about the user focus, a semi- automated software in the graphical environment of Matlab ® was created. The proposed system, based on researches that focus to the assessing of a webpage that is under construction that will replace the current page, in such a way that the appropriate changes will be made to ensure that it will be more user friendly. Moreover, other aspects that have been checked, are the recommended systems and the different kinds of advertisement that are used in webpages. Finally, the proposed system, is suitable for a lot of companies that aim to have the most efficiency in their products.

/pdf/anikhneuse-kai-epexergasia-anthropines-estiases-me-ten-4elgr2k1w2.pdf

Ανίχνευση και επεξεργασία ανθρώπινης εστίασης με την χρήση των γυαλιών παρακολούθησης ματιών TOBII PRO GLASSES 2

Facial expression monitoring is crucial in fields including mental health care, driver assistant systems, and advertising. However, existing systems typically rely on cameras that capture entire faces, or contact-based bio-signal sensors, which are neither comfortable nor portable. In this demonstration, we present a wireless glasses system for non-contact facial expression monitoring. The system is composed of an IR camera and an embedded processing unit mounted on a 3D-printed glasses frame, and a novel data processing pipeline running across the glasses platform and a computer. Our system performs high-accuracy and real-time facial expression detection with a running time of up to 9 hours. We will show the fully-functioning wearable system in this demonstration.

Demo Abstract: Wireless Glasses for Non-contact Facial Expression Monitoring

We present a System for Processing In-situ Bio-signal Data for Emotion Recognition and Sensing (SPIDERS)– a low-cost, wireless, glasses-based platform for continuous in-situ monitoring of user's facial expressions (apparent emotions) and real emotions. We present algorithms to provide four core functions (eye shape and eyebrow movements, pupillometry, zygomaticus muscle movements, and head movements), using the bio-signals acquired from three non-contact sensors (IR camera, proximity sensor, IMU). SPIDERS distinguishes between different classes of apparent and real emotion states based on the aforementioned four bio-signals. We prototype advanced functionalities including facial expression detection and real emotion classification with a landmarks and optical flow based facial expression detector that leverages changes in a user's eyebrows and eye shapes to achieve up to 83.87% accuracy, as well as a pupillometry-based real emotion classifier with higher accuracy than other low-cost wearable platforms that use sensors requiring skin contact. SPIDERS costs less than $20 to assemble and can continuously run for up to 9 hours before recharging. We demonstrate that SPIDERS is a truly wireless and portable platform that has the capability to impact a wide range of applications, where knowledge of the user's emotional state is critical.

SPIDERS: Low-Cost Wireless Glasses for Continuous In-Situ Bio-Signal Acquisition and Emotion Recognition

SPIDERS+: A light-weight, wireless, and low-cost glasses-based wearable platform for emotion sensing and bio-signal acquisition

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Demo Abstract: Wireless Glasses for Non-contact Facial Expression Monitoring

SPIDERS: Low-Cost Wireless Glasses for Continuous In-Situ Bio-Signal Acquisition and Emotion Recognition

SPIDERS+: A light-weight, wireless, and low-cost glasses-based wearable platform for emotion sensing and bio-signal acquisition