Park Do-Eun, Youn Jong-Hoon, Song Teuk-Seob
Department of Computer Engineering, Mokwon Uninversity, Daejeon 35349, Republic of Korea.
Department of Computer Science, University of Nebraska, Omaha, NE 68182, USA.
Sensors (Basel). 2025 Jul 7;25(13):4228. doi: 10.3390/s25134228.
Walking-friendly cities not only promote health and environmental benefits but also play crucial roles in urban development and local economic revitalization. Typically, pedestrian interviews and surveys are used to evaluate walkability. However, these methods can be costly to implement at scale, as they demand considerable time and resources. To address the limitations in current methods for evaluating pedestrian pathways, we propose a novel approach utilizing wearable sensors and deep learning. This new method provides benefits in terms of efficiency and cost-effectiveness while ensuring a more objective and consistent evaluation of sidewalk surfaces. In the proposed method, we used wearable accelerometers to capture participants' acceleration along the vertical (), anterior-posterior (AP), and medio-lateral (ML) axes. This data is then transformed into the frequency domain using Fast Fourier Transform (FFT), a Kalman filter, a low-pass filter, and a moving average filter. A deep learning model is subsequently utilized to classify the conditions of the sidewalk surfaces using this transformed data. The experimental results indicate that the proposed model achieves a notable accuracy rate of 95.17%.
适宜步行的城市不仅能促进健康和带来环境效益，还在城市发展和地方经济振兴中发挥着关键作用。通常，通过行人访谈和调查来评估步行适宜性。然而，这些方法在大规模实施时成本高昂，因为它们需要大量的时间和资源。为了解决当前评估行人道路方法的局限性，我们提出了一种利用可穿戴传感器和深度学习的新方法。这种新方法在效率和成本效益方面具有优势，同时能确保对人行道表面进行更客观、一致的评估。在所提出的方法中，我们使用可穿戴加速度计来捕捉参与者在垂直（）、前后（AP）和左右（ML）轴向上的加速度。然后，利用快速傅里叶变换（FFT）、卡尔曼滤波器、低通滤波器和移动平均滤波器将这些数据转换到频域。随后，利用深度学习模型根据这些转换后的数据对人行道表面状况进行分类。实验结果表明，所提出的模型达到了95.17%的显著准确率。
