e-Shoes: Smart shoes for unobtrusive human activity recognition

Many approaches to human activity recognition such as wearable based or computer vision based are obtrusive in the sense that they prevent the users from performing activities in a natural way, or they might raise privacy invasion concerns. This paper presents e-Shoes — smart shoes for unobtrusive human activity recognition. E-Shoes are shoes instrumented with tiny wireless accelerometers embedded inside the insole of the shoes. The sensors are seamless to the users making the system suitable for recognizing everyday activities. To analyze sensor signals, we propose a convolution neutral networks (CNN) model that automatically learns features from sensing data and makes predictions about performing activities. We verify the effectiveness of the approach with a real dataset that covers seven daily activities. The system achieved 93% accuracy in average, which is very promising, while being energy efficient and easy to use.