Characterization of the iPhone LiDAR-Based Sensing System for Vibration Measurement and Modal Analysis

Abstract

Portable depth sensing using time-of-flight LiDAR principles is available on iPhone 13 Pro and similar Apple mobile devices. This study sought to characterize the LiDAR sensing system for measuring full-field vibrations to support modal analysis. A vibrating target was employed to identify the limits and quality of the sensor in terms of noise, frequency, and range, and the results were compared to a laser displacement transducer. In addition, properties such as phone-to-target distance and lighting conditions were investigated. It was determined that the optimal phone-to-target distance range is between 0.30 m and 2.00 m. Despite an indicated sampling frequency equal to the 60 Hz framerate of the RGB camera, the LiDAR depth map sampling rate is actually 15 Hz, limiting the utility of this sensor for vibration measurement and presenting challenges if the depth map time series is not downsampled to 15 Hz before further processing. Depth maps were processed with Stochastic Subspace Identification in a Monte Carlo manner for stochastic modal parameter identification of a flexible steel cantilever. Despite significant noise and distortion, the natural frequencies were identified with an average difference of 1.9% in comparison to the laser displacement transducer data, and high-resolution mode shapes including uncertainty ranges were obtained and compared to an analytical solution counterpart. Our findings indicate that mobile LiDAR measurements can be a powerful tool in modal identification if used in combination with prior knowledge of the structural system. The technology has significant potential for applications in structural health monitoring and diagnostics, particularly where non-contact vibration sensing is useful, such as in flexible scaled laboratory models or field scenarios where access to place physical sensors is challenging.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

“…Nonconventional approaches are not limited to invasive smartphone-based SHM techniques. Another interesting example is a non-contact approach that utilizes Light Detection and Ranging (LIDAR) technology embedded in the front face of new-generation smartphones for modal identification [ 66 ]. With more types of sensors embedded in smartphones, improvements in sensor quality, and exponentially increasing on-board intelligence, we expect to observe more innovative approaches that come up with completely new implementations or identification/detection theories involving smartphone data.…”

“…The first journal article dedicated to smartphone-based SHM is proposed as lab trials of different sensor technologies embedded in smartphones by Weimar, Germany [ 31 ]. The same group extended their research in the smartphone-based SHM area with more studies on the cable-force identification [ 64 ], smartphone-controlled Raspberry pi sensors [ 65 ], and a non-contact modal analysis approach discussed further in Section 7 [ 66 ]. Another early example from Europe was [ 67 ], which utilized nonlinear acoustics techniques to detect cracks on metal plates using smartphones.…”