IoT-based Wireless Mobile Computing Platform for Fall Risk Prediction

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IoT-based Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an imperative component in monitoring injuries due to these falls. Progress in gait analysis systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. A thorough investigation of existing systems and using predictive analytics, I built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of fall before it happens. I used built-in sensors in smartphones and a sensor embedded smart-shoe. The interactions between these sensors and the user resulted in distinct challenges for this work while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work will make several contributions to current and future generation IoT-based research.

 

Registration:

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Social Time: 5:30 - 6:00 (Cash bar, free soft beverages)

Dinner: 6:00 - 7:00 (Reservation required)

Presentation: 7:00 - ~8:45 (You may attend the presentation without buying dinner).

 

Buffet Menu:

Roasted Turkey & Dressing
Baked Ham with Pineapples
Three Cheese Lasagna
Sweet Potato Casserole
Scalloped Potatoes
Sauteed Seasonal Vegetables
Cranberry Relish
Dinner Rolls and Butter
Chef’s Choice of Dessert

Tea and soft drinks are included, alcohol is available for purchase

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