Sensing from the Bottom: Smart Insole Enabled Patient Handling Activity Recognition Through Manifold Learning

The risk of overexertion injury caused by patient handling and movement activities causes chronic pain and severe social issues among the nursing force. The accurate recognition of patient handling activities (PHA) is the first step to reduce injury risk for caregivers. In this paper, we propose a novel solution comprising a smart footwear device and an action manifold learning framework to address the challenge. The wearable device, called Smart Insole, is equipped with a rich set of sensors and can provide an unobtrusive approach to obtain and characterize the action information of patient handling activities. Our proposed action manifold learning (AML) framework extracts the intrinsic signature structure by projecting raw pressure data from a high-dimensional input space to a low-dimensional manifold space. We performed a pilot study with eight subjects including eight common activities in a nursing room. The experimental results show the overall classification accuracy achieves 86.6%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.9% and 88.3%, respectively.