Smart Shoe Performing Real Time Motion Classification using Machine Learning for External Motion Alerts in Vehicles.
Disciplines
Other Computer Engineering
Abstract (300 words maximum)
As networked sensors become more ubiquitous and integrated into the Internet-of-Things, various technologies can be leveraged to provide an increased amount of safety to the public. In this research we have constructed a pressure sensor within a shoe covering two regions of detection: heel and toe. The pressure sensor is centered around polymeric foil Velostat which has the property of changing its resistance dependent upon amount of pressure applied. When made into pads placed inside of the shoe and connected in a circuit, we can record the pressure values from the pads over time and use them to train decision trees for classification. We assess the performance of our sensor + model in a deployment scenario where inferencing is accomplished at the user wearable. We envision our wearable to assist in alerting approaching vehicles of the type of mobility of surrounding users with our system.
Academic department under which the project should be listed
SPCEET - Electrical and Computer Engineering
Primary Investigator (PI) Name
Billy Kihei
Smart Shoe Performing Real Time Motion Classification using Machine Learning for External Motion Alerts in Vehicles.
As networked sensors become more ubiquitous and integrated into the Internet-of-Things, various technologies can be leveraged to provide an increased amount of safety to the public. In this research we have constructed a pressure sensor within a shoe covering two regions of detection: heel and toe. The pressure sensor is centered around polymeric foil Velostat which has the property of changing its resistance dependent upon amount of pressure applied. When made into pads placed inside of the shoe and connected in a circuit, we can record the pressure values from the pads over time and use them to train decision trees for classification. We assess the performance of our sensor + model in a deployment scenario where inferencing is accomplished at the user wearable. We envision our wearable to assist in alerting approaching vehicles of the type of mobility of surrounding users with our system.