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

Sejik Oh

doi:10.3390/jsan14030061

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

2025-07-30

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DOI：10.3390/jsan14030061

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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.

Keywords:

indoor human movement

time-of-flight sensor

LSTM neural network

direction identification

low-resolution sensing

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

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