Institute of Design, Robotics and Optimisation (iDRO), The School of Mechanical Engineering at the University of Leeds, Leeds, LS2 9JT, UK.
Institute of Design, Robotics and Optimisation (iDRO), The School of Mechanical Engineering at the University of Leeds, Leeds, LS2 9JT, UK.
Neural Netw. 2018 Jun;102:107-119. doi: 10.1016/j.neunet.2018.02.017. Epub 2018 Mar 9.
In this paper, a novel approach for recognition of walking activities and gait events with wearable sensors is presented. This approach, called adaptive Bayesian inference system (BasIS), uses a probabilistic formulation with a sequential analysis method, for recognition of walking activities performed by participants. Recognition of gait events, needed to identify the state of the human body during the walking activity, is also provided by the proposed method. In addition, the BasIS system includes an adaptive action-perception method for the prediction of gait events. The adaptive approach uses the knowledge gained from decisions made over time by the inference system. The action-perception method allows the BasIS system to autonomously adapt its performance, based on the evaluation of its own predictions and decisions made over time. The proposed approach is implemented in a layered architecture and validated with the recognition of three walking activities:level-ground, ramp ascent and ramp descent. The validation process employs real data from three inertial measurements units attached to the thigh, shanks and foot of participants while performing walking activities. The experiments show that mean decision times of 240 ms and 40 ms are needed to achieve mean accuracies of 99.87% and 99.82% for recognition of walking activities and gait events, respectively. The validation experiments also show that the performance, in accuracy and speed, is not significantly affected when noise is added to sensor measurements. These results show that the proposed adaptive recognition system is accurate, fast and robust to sensor noise, but also capable to adapt its own performance over time. Overall, the adaptive BasIS system demonstrates to be a robust and suitable computational approach for the intelligent recognition of activities of daily living using wearable sensors.
本文提出了一种使用可穿戴传感器识别行走活动和步态事件的新方法。这种方法称为自适应贝叶斯推理系统（BasIS），它使用概率公式和顺序分析方法，用于识别参与者执行的行走活动。该方法还提供了识别步态事件的功能，这对于确定人体在行走活动期间的状态是必要的。此外，BasIS 系统还包括用于预测步态事件的自适应感知方法。自适应方法利用推理系统随时间做出的决策所获得的知识。感知方法允许 BasIS 系统根据其自身预测的评估和随时间做出的决策，自主调整其性能。该方法采用分层架构实现，并通过识别三种行走活动进行验证：平地、斜坡上升和斜坡下降。验证过程使用从附着在参与者大腿、小腿和脚上的三个惯性测量单元收集的真实数据进行。实验表明，分别实现 99.87%和 99.82%的行走活动和步态事件识别准确率，需要 240ms 和 40ms 的平均决策时间。验证实验还表明，当向传感器测量值添加噪声时，性能（在准确性和速度方面）不会受到显著影响。这些结果表明，所提出的自适应识别系统准确、快速且对传感器噪声具有鲁棒性，并且能够随时间调整自身性能。总的来说，自适应 BasIS 系统证明是一种使用可穿戴传感器进行日常活动智能识别的强大且合适的计算方法。
