LIDAR Assist spatial sensing for the visually impaired and performance analysis

Carolyn Ton, Abdelmalak Omar, Vitaliy Szedenko, Viet Hung Tran, Alina Aftab, Fabiana Perla, Michael J. Bernstein, Yi Yang

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Echolocation enables people with impaired or no vision to comprehend the surrounding spatial information through the reflected sound. However, this technique often requires substantial training, and the accuracy of echolocation is subject to various conditions. Furthermore, the individuals who practice this sensing method must simultaneously generate the sound and process the received audio information. This paper proposes and evaluates a proof-of-concept light detection and ranging (LIDAR) assist spatial sensing (LASS) system, which intends to overcome these restrictions by obtaining the spatial information of the user's surroundings through a LIDAR sensor and translating the spatial information into the stereo sound of various pitches. The stereo sound of relative pitch represents the information regarding objects' angular orientation and horizontal distance, respectively, thus granting visually impaired users an enhanced spatial perception of his or her surrounding areas and potential obstacles. This paper is divided into two phases: Phase I is to engineer the hardware and software of the LASS system and Phase II focuses on the system efficacy study. The study, approved by the Penn State Institutional Review Board, included 18 student volunteers, who were recruited through the Penn State Department of Psychology Subject Pool. This paper demonstrates that the blindfolded individuals equipped with the LASS system are able to quantitatively identify the surrounding obstacles, differentiate their relative distance, and distinguish the angular location of multiple objects with minimal training.

Original languageEnglish (US)
Article number8419727
Pages (from-to)1727-1734
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number9
StatePublished - Sep 2018

All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering
  • Rehabilitation


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