Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Mobile-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, or human intervention, this paper presents TONARI, an effortless CPC detection framework that performs in a reactive manner. Particularly, TONARI is developed firstly with LoRa, the representative of unlicensed Low Power Wide Area Network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a subtle feature arbitrator aiming to distinguish different types of LoRa chirp-based additive sample magnitude sequences. TONARI further includes a LoRa chirp mapping operation and makes final decision based on multiple received packets. Experiment shows that TONARI can achieve the CPC detection accuracy of 100% in most practical cases.