Work

Movement Direction Classification Using Low-Resolution ToF Sensor and LSTM-Based Neural Network

Year: 2025

Type: article

Abstract: This study proposes an effective method for identifying human movement direction in indoor environments by leveraging a low-resolution time-of-flight (ToF) sensor and a long short-term memory (LSTM) neural network model. While previous studies have employed camera-based or high-resolution ToF-based sensors, we utilize an 8 × 8 array ToF sensor, which is neither expensive nor related to any privacy issues. Furthermore, in contrast to the conventional rule-based algorithm, the proposed method employs the LSTM model to effectively handle the sequential time-series data. Experimental evaluations, including both basic single-person scenarios and complex multi-user challenge scenarios, confirm that the proposed LSTM-based approach achieves outstanding accuracy of 98% in identifying human entry and exit movements.

Source: Journal of Sensor and Actuator Networks

DOI: 10.3390/jsan14030061

Authors: Sejik Oh, Kyoung Min Lee, Seok Young Lee, Nam Kyu Kwon

Institutions: Yeungnam University, Sejong University

Language: English

Topic: Indoor and Outdoor Localization Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,413

Funders: National Research Foundation, Yeungnam University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea