Wearable Device to Detect Cardiac Arrest

Cardiac arrest is one of the leading causes of death globally and is unwitnessed in most cases. This begs the question: how can anyone know that an individual is experiencing cardiac arrest if no one is around to see it? MULT 603 has designed a cardiac arrest monitoring device that a person can wear on their wrist. This device detects the individual’s heartbeat; in the case that the person’s heartbeat drops below their normal individual threshold, their family, doctors, and friends along with emergency medical services will be notified of their location and condition. The hope is to drastically improve response times, put the minds of families in which a loved one has had past heart complications at ease, and eliminate unnecessary loss of life. The branding for this concept is named Cardian, a hybrid of “Cardiac” and “Guardian”. The mission of Cardian is to provide ease of mind and eliminate unnecessary loss of life by utilizing simple and easy to use technology and pairing it with powerful engineering and emergency medical services. The primary customer (the target market) is those age 65 plus with heart disease. The unmet need is a gap in telehealth services (NAICS 621999) for autonomous alerting of medical emergencies; in this project, the emergency of SOHCA. Over 85 million Americans have some form of heart disease, and the target market makes up of 40 million of this number. At one percent market capture and the current price point of $150, annual revenue potential is$60,000,000.https://scholarscompass.vcu.edu/capstone/1146/thumbnail.jp

Wearable device to assist independent living.

Older people increasingly want to remain living independently in their own homes. The aim of the ENABLE project is to develop a wearable device that can be used both within and outside of the home to support older people in their daily lives and which can monitor their health status, detect potential problems, provide activity reminders and offer communication and alarm services. In order to determine the specifications and functionality required for development of the device user surveys and focus groups were undertaken and use case analysis and scenario modeling carried out. The project has resulted in the development of a wrist worn device and mobile phone combination that can support and assist older and vulnerable wearers with a range of activities and services both inside and outside of their homes. The device is currently undergoing pilot trials in five European countries. The aim of this paper is to describe the ENABLE device, its features and services, and the infrastructure within which it operates

A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker

The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For this work, a multi-sensory wearable device has been designed and implemented to be used in the Doñana National Park in order to control and monitor wild and semiwild life animals. The data obtained with these sensors is processed using a Spiking Neural Network (SNN), with Address-Event-Representation (AER) coding, and it is classified between some fixed activity behaviors. This works presents the full infrastructure deployed in Doñana to collect the data, the wearable device, the SNN implementation in SpiNNaker and the classification results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

SMART WATCH POWER SAVING PREDICTION BASED ON CONTEXTUAL SIGNALS

A wearable computing device (e.g., a smart watch), referred herein as a “wearable device” or “device,” is described that utilizes contextual signals to predict when to toggle between a power savings mode (e.g., “Sleep Mode”), where one or more lower power processors control functionality of the device, and a higher power mode, where one or more higher power processors control functionality of the device. The wearable device may predict, based on contextual signals, when the user may not require full functionality of the wearable device and switches to the power savings mode. For example, the user may not require full functionality of the wearable device when the user is asleep, exercising, or when the device is on “Do Not Disturb” mode. The wearable device may also predict, based on contextual signals, when the user may require full functionality and may switch from a lower power mode, such as the power savings mode, to a higher power mode in advance of the predicted time or may prevent the wearable device from switching into the power savings mode from the regular power mode. The contextual signals may include state of the user, movement patterns, and context information such as location, time, user behavior history, device configuration information, calendar events, or other relevant data

Wearable Device Charging Dongle with Integrated Heatsink and Fan

This disclosure describes techniques for effective thermal management of wearable devices such as AR/VR headsets or smartglasses when used in developer mode. Per techniques of this disclosure, a charging dongle that includes a heatsink and/or a fan is provided for use of a wearable device when in developer mode. The heatsink and/or fan on the charging dongle improves heat dissipation from the wearable device and mitigates overheating of the wearable device. Optionally, a temperature sensor is provided to enable accurate measurement of the temperature of the wearable device. If the measured temperature of the heatsink is close to a threshold, an alert is transmitted to the user and/or the charging current adjusted to a lower value. Further, the fan can be automatically activated based on the sensed temperature

Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous

Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge