Performance Evaluation of Machine/Deep Learning-Based Object Recognition Techniques Leveraging Channel State Information Using a Real Testbed

Object recognition is a quite critical task that involves the identification and potential localization of a target inside a monitored area. Target detection usually relies on material, shape and size identification in order to infer higher level information, and it can be employed in many different frameworks and applications. In this connection, we carry out a performance evaluation campaign on WiFi Channel State Information (CSI)-based object recognition techniques used to automatically identify material, category and specific objects among daily life items. The main contribution of this work is to provide a thorough comparison of the performance of different machine learning algorithms on recognition using a real-life experimental testbed. The performance study shows that the Random Forest (RF) classifier proves very accurate in terms of correct target recognition, independently of the considered task. The employed deep learning algorithm, Long Short-Term Memory (LSTM), is similarly able to attain very good results. In particular, accuracy values reach 94.8% in material identification, 97.1% in category differentiation and 98.1% in object recognition tasks.