School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia.
Faculty of Electrical and Electronics Engineering, University of Malaysia Pahang, 26600, Pekan, Malaysia.
Sci Rep. 2023 Sep 27;13(1):16177. doi: 10.1038/s41598-023-43428-9.
Gait data collection from overweight individuals walking on irregular surfaces is a challenging task that can be addressed using inertial measurement unit (IMU) sensors. However, it is unclear how many IMUs are needed, particularly when body attachment locations are not standardized. In this study, we analysed data collected from six body locations, including the torso, upper and lower limbs, to determine which locations exhibit significant variation across different real-world irregular surfaces. We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. Our results revealed two combinations of body locations, including the thigh and shank (i.e., the left and right shank, and the right thigh and right shank), from which IMU data should be collected to accurately classify walking patterns over real-world irregular surfaces (with classification accuracies of 97.24 and 95.87%, respectively). Our findings suggest that the identified numbers and locations of IMUs could potentially reduce the amount of data recorded and processed to develop a fall prevention system for overweight individuals.
从超重个体在不规则表面行走时收集步态数据是一项具有挑战性的任务，可以使用惯性测量单元 (IMU) 传感器来解决。然而，目前尚不清楚需要多少个 IMU，特别是当身体附着位置没有标准化时。在这项研究中，我们分析了从六个身体位置（包括躯干、上肢和下肢）收集的数据，以确定哪些位置在不同的真实不规则表面上表现出显著的变化。然后，我们使用深度学习方法来验证从识别出的身体位置记录的 IMU 数据是否可以对表面上的行走模式进行分类。我们的结果揭示了两种身体位置的组合，包括大腿和小腿（即左小腿和右小腿，以及右大腿和右小腿），应该从这些位置采集 IMU 数据，以准确地对真实世界不规则表面上的行走模式进行分类（分类准确率分别为 97.24%和 95.87%）。我们的研究结果表明，所确定的 IMU 数量和位置有可能减少记录和处理的数据量，从而为超重个体开发防跌倒系统。
