Abstract
RF-based gesture sensing and recognition has increasingly attracted intense academic and industrial interest due to its various device-free applications in daily life, such as elder monitoring, mobile games. State-of-the-art approaches achieved accurate gesture sensing by using fine-grained RF signatures (such as CSI, Doppler effect) while could not achieve the same accuracy with coarse-grained RF signatures such as received signal strength (RSS). This paper presents rRuler, a novel feature extraction method which aims to get fine-grained human gesture features with coarse-grained RSS readings, which means rought ruler could measure fine things. In order to further verify the performance of rRuler, we further propose rRuler-HMM, a hidden Markov model (HMM) based human gesture sensing and prediction algorithm which utilizes the features extracted by rRuler as input. We implemented rRuler and rRuler-HMM using TI Sensortag platforms and off-the-shelf (CTOS) laptops in an indoor environment, extensively performance evaluations show that rRuler and rRuler-HMM stand out for their low cost and high practicability, and the average gesture sensing accuracy of rRuler-HMM can achieve 95.71% in NLoS scenario and 97.14% in LoS scenario, respectively, which is similar to the performance that fine-grained RF signatures based approaches could achieve.
Highlights
RF signature based human gesture sensing and prediction is the core technology that enables a wide variety of device-free applications such as fitness tracking, elders monitoring, smart homes and Human-Computer Interactions (HCI)
In order further improve the accuracy of the human sensing with coarse-grained received signal strength (RSS), this paper takes the first attempt to explore the feasibilities to achieve accurate human gesture sensing with coarse-grained RSS which extracted from the off-the-shelf Sensortag with ZigBee protocol, and our approach is to transplant in VOLUME 7, 2019
In order to achieve accurate human gesture sensing with coarse-grained RSS, we compared RSS with channel state information (CSI) to investigate potential solutions to increase the sensing accuracy with coarse grained RSS which pervasively available in all kinds of wireless radios
Summary
RF signature based human gesture sensing and prediction is the core technology that enables a wide variety of device-free applications such as fitness tracking, elders monitoring, smart homes and Human-Computer Interactions (HCI). Most RF signature-based human gesture sensing solutions utilize fine-grained RF signatures such as Doppler shifts [1]–[4] channel state information (CSI) [5]–[21] to achieve accurate human gesture sensing and recognition. We find that different channels does not have obvious impact to the human gesture sensing accuracy while the human gestures occurred in non-line-of-sight (NLoS) path do not have obvious impact to data packet receptions among different communication devices
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